295 results about "Transformation algorithm" patented technology

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

The invention relates to the technical field of regional security and protection monitoring, in particular to a regional intrusion identification method and system under the view angle of an unmanned aerial vehicle, which adopts a multi-source information fusion framework, combines the real-time position, attitude and flight state information of the unmanned aerial vehicle through an accurate space coordinate conversion algorithm, and realizes the regional intrusion identification under the view angle of the unmanned aerial vehicle. GIS geographic information, unmanned aerial vehicle monitoring and a deep learning detection result are deeply fused, high-precision positioning of an intrusion area in an image shot by an unmanned aerial vehicle is realized, and robustness in a complex environment is improved. Targets such as personnel and vehicles in the image are efficiently recognized by applying a YOLO series algorithm, and violation intrusion behaviors are accurately judged by calculating the intersection-to-union ratio IoU of the targets and the intrusion area. The unmanned aerial vehicle is utilized to quickly cover a large-area monitoring area, and the problem that the monitoring range of a traditional fixed camera is limited is solved. In addition, by means of a space coordinate conversion algorithm, the influence caused by height and angle changes in the flight of the unmanned aerial vehicle is effectively overcome, and accurate positioning of the intrusion area in the image is achieved.

The invention relates to the technical field of tower crane control, and provides an aerial high-risk area identification and hoisting path reconstruction method and system. The method comprises the following steps of: respectively mounting two rotary radars on a rotatable bracket of the tower crane to form a radar sensing module comprising a double-radar module; dynamically scanning the potential risk area in the construction scene through a radar sensing module to obtain real-time position information of the potential risk area; receiving the real-time position information through a processing module, and converting the real-time position information into real-time three-dimensional coordinate data in a motion coordinate system of the hoisting equipment by adopting a three-dimensional coordinate conversion algorithm; constructing a three-dimensional model of the potential risk area based on the real-time three-dimensional coordinate data; and identifying the three-dimensional model to determine a high-risk area, adopting an improved fast exploration random tree algorithm as a path obstacle avoidance algorithm, combining the three-dimensional model to dynamically reconstruct an initial hoisting path set by a user, and generating an optimal risk-avoiding hoisting track avoiding the high-risk area.

The invention discloses an intelligent fructus aurantii sorting machine based on a five-connecting-rod mechanical arm and a sorting method. The sorting machine comprises a collecting mechanism, a conveying mechanism, a supporting mechanism, a sorting mechanism, a camera support, a feeding device, a lower computer, an upper computer and a vacuum adsorption control system. The sorting mechanism comprises a vacuum adsorption sorting device, a five-connecting-rod mechanical arm and a sorting mechanism supporting base, double motors are adopted to cooperatively drive and control space positioning of an end effector of the mechanical arm, and high-precision target grabbing is achieved through angle difference compensation. The upper computer converts the target center position recognized by the visual recognition device into space coordinates of the tail end of the mechanical arm through a coordinate conversion algorithm, and the five-connecting-rod mechanical arm is driven to complete precise sorting operation. Stable adsorption of the fructus aurantii is completed by means of vacuum negative pressure, the fructus aurantii naturally falls off by directly cutting off a negative pressure air source, and intelligent grading and sorting operation of the quality of the fructus aurantii is achieved. The device is suitable for automatic sorting of the traditional Chinese medicinal material fructus aurantii and is high in sorting efficiency, accurate in positioning and high in adaptability.

The invention discloses a crystal oscillator circuit fault classification technology based on a multi-source information fusion network, and belongs to the technical field of analog circuit fault diagnosis. Firstly, a multi-source data set of different measurement points of the crystal oscillator circuit is acquired; carrying out conversion from a time domain to a frequency domain on the data set by utilizing fast Fourier transform; extracting data fault features by using a convolutional neural network, and performing a trust distribution function under each piece of source data by using a softmax classifier; fusing different trust distribution functions under the multi-source data by adopting a D-S evidence theory to obtain a final diagnosis result and diagnosis probability output, and calculating cross entropy loss; and finally, training model parameters through back propagation to obtain a final diagnosis model. According to the method, the time-frequency transformation algorithm, the deep neural network algorithm and the information fusion algorithm are combined, a multi-source information fusion network is constructed, the defects of an existing diagnosis model in crystal oscillator circuit fault classification are overcome, and the accuracy and stability of crystal oscillator circuit fault diagnosis are remarkably improved.

The invention provides a cross-modal feature fusion method based on joint attention, which combines multi-modal data from different sources to predict the relative position of a target to be tracked, and comprises the following steps of: firstly, designing a cross-modal data representation network model for the data of different modals; generating a time sequence high-dimensional implicit code retaining key dependency information from the original data through a code conversion algorithm; secondly, a heterogeneous attention network unit is designed, and features of time sequence high-dimensional implicit codes corresponding to different modal data are extracted through two different forms of attention; and finally, through a cooperative training mechanism, information complementation among different modal data is utilized to generate the most effective feature representation for a time sequence prediction model, and accurate prediction of the moving position of the tracked target is realized.

The invention is suitable for the field of intelligent jet cleaning, and provides a dirt recognition cleaning method, device and equipment, and the dirt recognition cleaning method comprises the steps: obtaining an original image corresponding to target dirt and a sound reflection signal corresponding to the target dirt; converting the sound reflection signal corresponding to the target dirt into an image representation; obtaining a fused image corresponding to the target dirt according to the original image corresponding to the target dirt, the image representation and a preset multi-channel image fusion algorithm; inputting a fusion image corresponding to the target dirt into the improved deep neural network to obtain segmentation mask data and size data of the target dirt; adjusting the pose of the dirt cleaning equipment according to the segmentation mask data and a preset coordinate conversion algorithm; according to the size data of the target dirt, dirt cleaning parameters are adjusted, and dirt cleaning equipment is controlled to execute cleaning operation. According to the method, the dirt recognition and cleaning precision and efficiency are remarkably improved, and various cleaning requirements and operation environments can be better met.

The invention provides a battery broadband electrochemical impedance spectroscopy time domain rapid detection method and system, and the method comprises the steps: S1, dividing a target signal frequency band into a low frequency band, a middle frequency band and a high frequency band from low to high, and setting the parameters of an excitation signal DIBS of each frequency band; s2, enabling the discrete binary sequence to have spectrum distribution corresponding to the target signal through an optimization algorithm, and obtaining a DIBS signal containing all set frequency points; s3, performing current excitation on the battery by adopting DIBS excitation signals of different frequency bands to obtain excitation current and response voltage; and S4, respectively calculating the frequency spectrums of the excitation current and the response voltage under the three frequency bands, and obtaining the final impedance. A multi-band excitation mode is adopted, the sequence length of a single-segment signal is shortened, the clock period of the single-segment signal is prolonged, and the storage burden of excitation equipment and the data processing complexity of a time-frequency conversion algorithm are effectively prevented from being aggravated due to the long sequence period.

The invention discloses an underground multi-source fusion positioning method and system. The method comprises the following steps that initialization is carried out based on a known initial pose of underground equipment during starting; acquiring environment point cloud data scanned by a vehicle-mounted laser radar of underground equipment and motion information acquired by an inertial measurement unit; registration is carried out based on the environment point cloud data and a pre-constructed high-precision three-dimensional map, and first pose estimation is obtained by using an OpenMP-based parallel normal distribution transformation algorithm; using the motion information to obtain second pose estimation through integral operation; inputting the first pose estimation and the second pose estimation into a Kalman filter for loose coupling fusion, and outputting the fused equipment pose; and outputting the final pose as a positioning result of underground equipment for vehicle navigation and pose control. The method effectively improves the positioning precision of underground equipment.

The invention discloses a quantum circuit conversion method and system, electronic equipment and a storage medium, and relates to the technical field of quantum circuit conversion, and the method comprises the steps: extracting a feature vector of a logic quantum circuit based on initial quantum bit mapping; the feature vector is input into a pre-trained decision tree, recommendation parameters of a line conversion algorithm are obtained, the decision tree is constructed by a training set, and the feature vector of each sample corresponds to the optimal parameter of the decision tree, namely the parameter with the minimum number of physical line gates; and configuring a line conversion algorithm by using recommended parameters, converting a logic line into a physical line, and ensuring that all two quantum bit gates meet quantum computer connected graph constraints and are equivalent in function. According to the method, adaptive parameter configuration can be achieved through the feature vectors and the decision tree, the line conversion quality is effectively improved, the number of physical line gates is remarkably reduced, and the quantum program execution efficiency is improved on the premise that function equivalence and connectivity are guaranteed.

The invention discloses a cradle head dynamic control method and device, and the method comprises the steps: obtaining a two-dimensional pixel coordinate of a target object on a display plane, converting the two-dimensional pixel coordinate into a physical space coordinate through a coordinate conversion algorithm, and obtaining the pixel offset of the target object relative to the center of a cradle head; based on the pixel offset and the pixel density parameter, performing angle calculation by using a trigonometric function calculation module to obtain rotation angle parameters required by the horizontal axis and the vertical axis of the holder; pWM control signal conversion is carried out on the rotation angle parameter, a corresponding number of pulse waveforms are generated through a stepping motor driving module, and a holder double-shaft motor is controlled to rotate; and based on the deviation value between the current position of the holder and the target angle, real-time error correction is performed by adopting a PID control algorithm, so that the holder tracks the target object in real time and keeps pointing to the position of the target object. Through the solid geometry correction technology, the problems of precise positioning and dynamic tracking of a traditional pan-tilt control system in a complex scene are solved, and the precision and stability of pan-tilt control are improved.

The invention discloses a geographic positioning optimization method based on STN-YOLO and adaptive affine transformation, and relates to the technical field of geographic positioning optimization, and the method comprises the steps: employing a camera to carry out the shooting of an environment, and obtaining an original input image; performing spatial attitude alignment processing on the original input image by adopting an image pre-correction method to obtain an attitude normalized corrected image; an STN integrated YOLO target detection model is adopted to perform multi-category target recognition and bounding box regression on the corrected image subjected to attitude normalization, and a detection result with initial pixel-level coordinates is obtained; performing dynamic geometric correction on the initial pixel-level coordinates by adopting a self-adaptive affine transformation algorithm to obtain relative position information under a geographic mapping coordinate system; adopting a multi-source data fusion method to combine data of sensors such as a GPS (Global Positioning System) and an I MU (International Mobile Unit) and the relative position information to carry out collaborative filtering processing so as to obtain data after collaborative filtering; and optimizing the data after collaborative filtering, eliminating noise and abnormal values, and obtaining a global geographic positioning result.

The disclosed embodiment provides an ecological environment quality monitoring system, comprising: a gateway device sends its own fingerprint information to a remote server; the server uses a private conversion algorithm to process the fingerprint information to obtain a gateway ID; the gateway device generates a report message based on the gateway ID, fingerprint information, and reported data sent by the environmental monitoring device, and sends the report message to the remote server; after the remote server parses the report message to obtain a resolved gateway ID, resolved fingerprint information, and resolved reported data, it verifies whether the resolved gateway ID and resolved fingerprint information match based on the stored association relationship in the data table; if it is determined that the resolved gateway ID and fingerprint information do not match and the resolved gateway ID is stored in the data table, the association relationship corresponding to the resolved gateway ID is deleted. The data falsification device cannot obtain the gateway ID, so it does not know the correspondence between the gateway ID and the gateway fingerprint information, making it difficult to falsify reports from a large number of gateway devices.

The invention discloses an unmanned aerial vehicle road disease detection and analysis system and method based on a YOLO algorithm, and the method comprises the following steps: synchronously collecting a road image and geographic coordinates through an unmanned aerial vehicle, and constructing an image data set with geographic reference; constructing an initial YOLO target detection model based on the data set; introducing an additional detection layer for detecting a pixel area into the model, and embedding a channel attention mechanism into the feature extraction network to obtain an improved YOLO target detection model; reasoning a data set by using the model, and outputting a disease category, a bounding box coordinate and a confidence coefficient; pixel coordinates are converted into geographic coordinates through a coordinate conversion algorithm, and disease records with accurate geographic coordinates are generated; and optimizing and updating the model based on the record as an incremental training sample. According to the method, the disease detection precision and the small target identification capability are effectively improved, and the long-term adaptability and generalization performance of the model are enhanced through a closed-loop incremental learning mechanism.

The invention relates to the technical field of water monitoring and detection, in particular to a simulation system and an early warning method for multi-point dam break flood. The simulation system comprises a data collection module, a data processing module, a model construction module and a simulation calculation module, and the simulation calculation module is used for completing calculation of multi-point dam break flood in the flood dynamic coupling model and outputting a simulation result. Different reference data are unified to the same projection coordinate system through a coordinate conversion algorithm, and terrain splicing dislocation is avoided; the model construction module firstly generates an unstructured grid based on accurate topographic data, and grid nodes are overlapped with actual topographic feature points through a grid boundary optimization algorithm; extracting a grid where a potential overflow point is located, and writing the grid into a parameter file as a dynamic boundary condition; finally integrating geometry, boundaries, parameters and source files to construct a coupling model; the problem that an existing simulation system cannot truly reflect nonlinear phenomena such as turbulence and vortex in the flood routing process is solved.

The invention relates to a template-based multi-format layout file generation method and system, and the method comprises the following steps: generating a template object and a data object through analyzing a template file and a data file, constructing a document object of a fixed page through a dynamic paging calculator and a preset strategy class diagram, and supporting multi-format output; data volume self-adaptive paging is realized through a paging strategy, and cross-page table header repeated rendering and cell merging are supported; a middle data layer is adopted to separate contents and formats, and a coordinate system conversion algorithm is utilized to realize output of formats such as PDF and OFD. I / O consumption is reduced by lazy loading of a font resource pool, and table dynamic expansion and cross-page association are realized based on gridding modeling. According to the method and the device, the problems of incapability of self-adaptive paging and poor compatibility of multi-format output of traditional template sleeve printing are solved, and the efficiency and the consistency of layout file generation in a government affair electronic scene are remarkably improved.

The invention discloses an unmanned aerial vehicle flight path rapid planning method for electric power tower inspection, and the method comprises the following steps: (1), obtaining the basic information of a target electric power tower, including the latitude and longitude of the tower, the altitude, the height, the voltage grade and the line topological relation; and (2) generating a full view waypoint of the tower based on the basic information: calculating the position of the waypoint through a preset distance to the tower top and a preset distance in front of the tower in combination with a three-dimensional coordinate conversion algorithm, and setting the pitch angle of a pan-tilt to be-43 degrees. According to the invention, through four core innovations of automatic generation, dynamic adaptation, intelligent emergency and cooperative management, the problems of low path planning efficiency, poor flexibility, insufficient safety and the like in the prior art are solved, the efficiency, precision and reliability of unmanned aerial vehicle electric power inspection are significantly improved, and the dependence on artificial experience and hardware cost is reduced at the same time.

The invention relates to a navigation mark type and health state detection method based on improved YOLOv9, and belongs to the technical field of computer vision and navigation mark inspection. According to the method, in order to solve the problem of insufficient navigation mark data sets, a shape-damaged navigation mark data set is generated by using a CGAN technology, a navigation mark data set containing stains is generated through expansion by using a Canny algorithm, and a skew navigation mark data set is increased by using an affine transformation algorithm; in addition, traditional data enhancement methods such as geometric transformation and image splicing are introduced to simulate the imaging effects of the navigation mark at various angles and positions, so that the diversity of the image is improved. And the improved YOLOv9 model is utilized to improve the recognition accuracy of the small-size navigation mark. According to the method, the high-efficiency performance is maintained, meanwhile, the detection capability on fine and complex features is greatly improved, and accurate recognition on the navigation mark under different conditions is ensured.

The invention discloses a multimedia network-oriented electronic publication automatic typesetting system and a multimedia network-oriented electronic publication automatic typesetting method, which relate to the technical field of electronic publication typesetting, and comprise a typesetting module for customizing multiple parameters of an initial layout of an electronic publication reader according to the type of a media terminal, analyzing and identifying the type of the media terminal by adopting equipment UA or API (Application Program Interface), establishing an adaptive font size calculation model to dynamically generate a basic typesetting rule, and outputting the basic typesetting rule to a layout planning module and a typesetting rule library; according to the invention, a basic typesetting rule is dynamically generated by establishing an adaptive font size calculation model, dynamic content data is acquired in a content analysis module by adopting an FFmpeg audio and video processing technology and an ARIMA time sequence analysis model, and format conversion and terminal adaptation are carried out by adopting a dynamic coding optimization algorithm and a multimedia cross-format compatible conversion algorithm. The compatibility of the electronic publication to the multimedia elements on different browsers and devices is improved.

The invention discloses an unmanned aerial vehicle aerial photography water surface small target detection method and system based on dynamic slicing and correlation constraint, and belongs to the technical field of computer vision, artificial intelligence and geographic information processing, and the method comprises the steps: S1, data acquisition: obtaining high-resolution video stream data collected by an unmanned aerial vehicle flight platform; s2, carrying out dynamic slicing, and segmenting the original high-resolution image frame into a plurality of sub-image slices of which the sizes are matched with the input of the detection network; s3, target detection: inputting the subgraph slices into the model for reasoning, and outputting the category and local pixel coordinates of the target; s4, result fusion: mapping detection results of the sub-image slices back to an original image coordinate system to generate a global detection result; and S5, performing space-time positioning, and converting the pixel coordinates of the global detection result into geographic latitude and longitude coordinates by using a coordinate conversion algorithm. According to the method, the problems of leak detection and false detection caused by extremely high image resolution, small target size and background noise interference in a water surface environment are effectively solved.

The present disclosure provides a method, system, device, and storage medium for generating and producing a 3D lenticular image. The method comprises determining depth information of a 2D target image of a target object; generating a multi-viewpoint image corresponding to the 2D target image by utilizing a 2D-3D image transformation algorithm and a dynamic viewpoint interpolation technique based on the depth information, the multi-viewpoint image including a plurality of image frames corresponding to different shooting angles; and generating a 3D lenticular image of the target object based on the multi-viewpoint image.

The invention relates to the technical field of augmented reality image processing, and discloses a traditional Chinese medicine acupuncture auxiliary positioning system based on an AR technology, which comprises a body surface anatomical feature intelligent extraction module, a personalized meridian model adaptive deformation module, a dynamic virtual-real registration tracking module and an AR visual rendering and quality closed loop module, according to the method, a thin-plate spline transformation algorithm is adopted to elastically deform a standard meridian model to the body surface of an individual, the individualized cun unit length is dynamically calculated in combination with a bone degree cun method, virtual-real registration is optimized through an iterative nearest point algorithm, movement and breathing deviation of a patient are compensated, the positioning quality is evaluated through a confidence coefficient thermodynamic diagram, closed-loop reacquisition optimization is driven, and the positioning accuracy is improved. Accurate AR-assisted positioning of acupuncture points of the whole body is realized.

The invention discloses an unmanned aerial vehicle camera repositioning method and device based on image retrieval and a storage medium. The method comprises the steps of collecting image data and constructing an image database. A global feature extraction module is used for extracting global features to obtain the global features, similarity calculation sorting is carried out on the global features and the global features of the images in the database, and the most similar images are selected to form an image pair. After local feature extraction, relative translation and relative rotation are predicted through a relative pose regression network. And finally, converting the relative pose of the retrieved picture into an absolute pose through a relative pose conversion algorithm. According to the overall method, image feature extraction and pose prediction are realized through a deep learning network, and efficient relocation of the unmanned aerial vehicle camera is realized.

The invention discloses a highway bridge multi-mode safety monitoring method and system. The method comprises the steps that ultrasonic information, voiceprint information and image information of a highway bridge are collected; performing graying and normalization processing on the image information by adopting an improved Laplace transformation algorithm, and automatically adjusting the threshold value of the ultrasonic information according to the energy distribution of the ultrasonic information by utilizing a dynamic threshold value filtering method; carrying out noise reduction processing on the voiceprint information by adopting a Wiener filtering algorithm; a Modern-TCN model is adopted to train the voiceprint information obtained after noise reduction is carried out on the voiceprint information; the image information and the ultrasonic information are processed by adopting a Modern-TCN model improved by a Lite-EMA attention mechanism; a progressive multi-modal fusion method optimized by a PID search algorithm is adopted, the Modern-TCN model improved by the Lite-EMA attention mechanism and the Modern-TCN model are fused, and the fused model processing result is combined with an experience knowledge base to obtain a highway bridge safety monitoring result; the overall performance and reliability of highway bridge safety monitoring can be improved.

The invention discloses a three-dimensional visualization method, system and device based on heterogeneous spatial data fusion and dynamic sectioning, and a medium, belongs to the technical field of urban information model visualization, and aims to solve the technical problem of how to improve data fusion efficiency, sectioning response delay and mobile terminal frame rate in a million patch scene. Light-weight and high-interaction visual display of scenes such as smart cities and underground pipe galleries is realized, and the defects of multi-source spatial data splitting, high sectioning interaction delay and poor cross-terminal compatibility are overcome. According to the technical scheme, heterogeneous data dynamic normalization is carried out, wherein local coordinates of a building information model, WGS84 coordinates of a geographic information system and computer aided design data are unified to an Euclidean coordinate system through a space dynamic reference conversion algorithm, and normalized data are obtained; constructing a semantic data relation graph; generating a dynamic sectioning plane in real time; vector elements are highlighted in a linkage manner; and carrying out cross-terminal adaptive rendering.

The invention discloses a reciprocating type passenger ropeway constant deceleration control system, and relates to the technical field of brake control. According to the method, the first-stage constant deceleration braking mode and the second-stage constant torque braking mode are set, analysis is carried out through the second-stage braking conversion algorithm, the deceleration abnormal index, the speed tracking abnormal index and the response abnormal index are output, then the mode abnormal total value is obtained, the braking state is judged, self-adaptive mode switching is achieved, and the braking efficiency is improved. Constant braking deceleration is maintained, swinging of a hanging box is reduced, comfort of passengers is improved, brake rubber abrasion is reduced, meanwhile, regional debugging is not needed, and the operation difficulty of a construction site is reduced; hydraulic loop parameters are analyzed through a failure judgment logic algorithm set by the hydraulic redundancy protection module, a loop abnormal total value is output, and hydraulic loop switching is carried out based on the loop abnormal total value; and through visual display of the fault memory interaction module, the safety redundancy of the braking system is improved, fault tracing and maintenance are facilitated, and the running reliability of the cableway is guaranteed.

The invention discloses a pre-training language model deployment method, electronic equipment and a readable storage medium, and relates to the technical field of pre-training language models. The method comprises the following steps: determining an initial weight matrix and a Hessian matrix of a to-be-quantized initial pre-training language model; the initial weight matrix is used for representing the model information topology of the initial pre-training language model, and the Hessian matrix is used for representing the sensitive weight in the initial weight matrix; generating a sparse transformation matrix based on a sub-sampling random Hadamard transformation algorithm; performing incoherent processing on the initial weight matrix through the sparse transformation matrix to obtain a processed optimized weight matrix; based on the optimized weight matrix and the Hessian matrix, adaptive rounding operation is executed, and a quantized weight matrix is obtained; and determining a quantized target language model according to the quantized weight matrix, and sending the quantized target language model to the to-be-deployed device. The problem that the efficiency of deploying the pre-training language model is low is solved, and the technical effect of improving the efficiency of deploying the pre-training language model is achieved.

The invention relates to a minimally invasive implantable frontal lobe brain-computer interface method and device, and the method comprises the steps: constructing an individualized brain three-dimensional model according to CT and MRI image data of a patient, presetting an optimal nasal implantation path, and carrying out the implantation of the patient through a nose under the dual guidance of a nasal endoscope and a three-dimensional navigation system, constructing a skull base passage through minimally invasive trimming of a nasal cavity / paranasal sinus structure, and performing real-time matching and overlapping on an endoscope view and the three-dimensional model by using a rigid body transformation algorithm to display the position of a surgical instrument; windowing at a preset target point, implanting the flexible electrode array into a target brain region according to a planned depth and angle through a controllable propulsion mechanism, and establishing a neural signal acquisition or stimulation interface; intelligent mode recognition is carried out on the collected neural signals, the intention or brain state of the user is decoded, and corresponding neural stimulation or feedback signals are generated. According to the method, accurate, safe and minimally invasive clinical intervention is realized through a full-chain technical scheme of individualized planning, real-time navigation, minimally invasive implantation and biological integration, and the application value of a brain-computer interface technology is remarkably improved.

The invention provides a method for adapting a satellite-borne domestic NAND Flash to a yaffs file system, which comprises the following steps: designing a block conversion mechanism, and converting a physical address of the domestic NAND Flash into a logic address conforming to MTD specifications through an address conversion algorithm to ensure that the file system can be correctly identified and managed; read-write operation is adjusted according to specific characteristics of the domestic NAND Flash, data correctness and performance optimization are ensured, and efficient management and compatibility improvement of the non-standard NAND Flash are achieved. An adaptation layer is developed, it is ensured that the yaffs file system can recognize and process the characteristics of the domestic NAND Flash, a bottom layer drive is driven, adjusted and modified to meet the MTD interface specification, meanwhile, the unique function of the domestic NAND Flash is reserved, and the system based on the domestic NAND Flash can smoothly operate the yaffs file system; an error correction mechanism based on an RS (Reed-Solomon) code is introduced, a check code is calculated and stored together with original data during data writing, so that error correction is performed during reading, an efficient decoding algorithm is designed, errors of any 8 bytes in 256 bytes can be corrected, and meanwhile, the performance overhead is minimized.

The invention discloses an obstacle distance measurement method and system based on panoramic tiled display. Four-way or six-way fisheye cameras are installed on a vehicle, images around a vehicle body are collected, 3D rendering splicing is carried out based on calibration data (internal parameters / external parameters), and a 360-degree panoramic aerial view image is generated. A user clicks two points of a screen through a display terminal, a system converts screen coordinates into 3D normalized vertex coordinates through viewport inverse transformation, relative coordinate differences are calculated and then expanded into homogeneous coordinates, the homogeneous coordinates are converted into physical coordinate differences in combination with a pre-stored rendering transformation matrix inverse matrix (generated by model / projection / view transformation combination), and the 3D normalized vertex coordinates are converted into 3D normalized vertex coordinates. And finally calculating the actual physical distance between the two points. The system supports dynamic refreshing of a ranging result and renders a distance marking line on a panoramic image. According to the method, through multi-camera calibration, coordinate space mapping and a matrix transformation algorithm, interactive obstacle distance measurement in a vehicle-mounted panoramic display scene is realized, and the method is suitable for intelligent driving, vehicle auxiliary driving and other scenes.