101 results about "Window detection" patented technology

The invention relates to the technical field of cooperative control, in particular to a multi-machine cooperative control method and system for a bridge cable hoisting device, and the method comprises the following steps: collecting tension change characteristics through a distributed sensor array, extracting indexes through a sliding window algorithm to generate a response time window, and generating a cooperative time window through time sequence overlapping and speed trend adjustment. Calculating a compensation value by combining inertial parameters and acceleration to generate a trigger window, detecting tension and displacement difference, inputting the tension and displacement difference into a dynamic threshold function for screening and fusion, outputting a synchronous trigger condition, and generating a synchronous control instruction by encoding a device number and a tension change rate after timestamp alignment. According to the method, multi-node tension synchronous acquisition and normalization processing, dynamic response generation through a sliding window, boundary matching action triggering adjustment through an overlapping algorithm, response lag correction through inertia compensation, tension and displacement difference real-time detection, dynamic threshold screening and triggering condition fusion and timestamp alignment packaging instruction consistency are carried out; and the cooperation precision and the system robustness are improved.

The invention discloses an out-of-focus identification and restoration method for a microscopic system. The method comprises the following steps: 1, preparing a microscopic out-of-focus image data set and carrying out degradation modeling; 2, constructing an out-of-focus parameter prediction network based on multi-label parameter reasoning and microscopic system priori knowledge and an image restoration network based on a generative adversarial network, performing independent training, and then performing merging training through a circulation system formed by mutual connection based on predicted point spread function convolution and deconvolution operation; 3, model fine tuning based on a specific system and a new data set; and 4, performing model testing, performing large-view image sliding window detection and collage fusion, and explicitly outputting defocus parameters of the system during image shooting. The method can be applied to out-of-focus fuzzy recognition and restoration of microscopic imaging of various systems, and is beneficial to the accuracy of functions such as particle counting and particle size statistics of the systems, thereby further promoting the application of the full-depth-of-view microscopic system in biomedical detection.

The invention discloses a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation Satellite System) anti-deception navigation method based on ESKF (Enhanced Shift Keying Function) and dynamic window detection, which comprises the following steps of: 1, acquiring observation data of an INS and a GNSS, performing time synchronization and coordinate system unification on the observation data of the INS and the GNSS, and respectively eliminating a time error and a coordinate difference; 2, a dynamic window detection mechanism is designed, an error state dynamical model is constructed, the dynamic window detection mechanism is used for detecting whether GNSS cheating exists or not, and meanwhile the error state dynamical model can output stable navigation parameters; and 3, outputting a final high-precision navigation result according to the stable navigation parameters. According to the method, the problems of error accumulation and covariance distortion of nonlinear system model linearization are effectively solved, pollution of GNSS deception signals can be effectively inhibited, high-precision positioning and anti-deception capability are kept in complex environments (such as urban shielding and electromagnetic interference), and a navigation system with high precision and high reliability is formed.

The invention provides a brain-computer interface decoding method and system based on multi-modal signal fusion, and relates to the technical field of brain-computer interfaces. The method comprises the following steps: S1, acquiring a multi-mode signal, unifying a time reference and starting a sliding window detection intention; s2, de-prompting, sequential bias checking and permutation correction, artifact suppression and band-pass filtering are carried out; s3, channel and modal quality is calculated, and a reliability weight and a rejection list are generated and marked; s4, multi-scale coding is carried out, cross-modal alignment is realized through learnable time delay compensation, and a first fusion representation is obtained; s5, performing layering and cross attention fusion under reliability gating so as to align and reconstruct constraint to keep semantic consistency, and obtaining joint semantic representation; and S6, sequence-level decoding is carried out to output semantics / states / instructions. According to the method, bias and noise are suppressed, time delay difference and cross-main-body equipment change are resisted, and decoding accuracy, robustness and practicability are remarkably improved.

The invention discloses a video target tracking system and method combined with deep learning, and relates to the technical field of computer vision, and the method comprises the following steps: obtaining video frame sequence data of each monitoring device; performing segmentation processing on the time coding sequence of the video frame sequence data to obtain track fragments of corresponding monitoring equipment; and extracting a time coding subsequence of a track fragment pair formed by any two track fragments, and carrying out feature analysis on the time coding subsequence to obtain a multi-dimensional matching confidence score of each track fragment pair. According to the scheme, a shielding time window detection mechanism is introduced, shielding influences are actively recognized and quantified, the problem of time coding distortion caused by shielding in a multi-monitoring-device environment is solved, a similarity calculation result can be dynamically corrected, mismatching caused by local shielding is avoided, and the accuracy of time coding is improved by combining motion continuity constraint and a nonlinear fusion strategy. The track matching robustness in a complex shielding scene is effectively improved, and a high-confidence matching reference is provided for subsequent time axis unification.

The invention relates to the technical field of data processing, and discloses a visual large-screen dynamic display method and system based on data driving, and the method comprises the steps: collecting business scene demand data, generating a display logic configuration instruction, and setting a display content type, a spatial layout and an interaction mode; the calling data interface is connected with a background storage system and obtains service data in real time through the data stream processing module; performing time sequence analysis, sliding window detection and association rule calculation on the data to obtain a freshness index, an abnormal fluctuation parameter and a coupling degree, and judging whether abnormal fusing is triggered or not; performing exception filtering and standardization on the processed data, mapping the processed data into a graph data structure, optimizing layout through the Fresnel's law, adjusting color matching through a color contrast algorithm, and generating a chart graph; and dynamically refreshing the interface by the rendering engine. According to the method, on-demand customization and dynamic adaptation are realized, and the influence of wrong data on the accuracy of a large screen is avoided.

1. The name of the design product: door and window detection equipment. 2. The use of the design product: a detection device for detecting the fatigue performance of window hardware hinges. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The utility model relates to the field of door and window detection, and discloses a door and window smog prevention detection sensor mounting structure, which comprises a smog machine and a connecting pipe, a handle is slidably mounted on the inner wall of the smog machine, two rollers are rotatably connected to the bottom end of the smog machine, and one end of the connecting pipe communicates with a shielding cloth. A plurality of supporting rods are fixedly installed on the inner wall of the shielding cloth and evenly distributed at the four corners of the shielding cloth, one end of each supporting rod is rotationally connected with a connecting base, the two rolling wheels are symmetrically distributed with the smoke sprayer as the axis, and one end of a connecting pipe communicates with one end of the smoke sprayer; the shielding cloth communicates with the smoke sprayer through the connecting pipe, the multiple supporting rods are installed on the inner wall of the shielding cloth and connected through the connecting base, so that the shielding cloth can be attached to the surface of the door and window conveniently, the size of the shielding cloth is adjusted through rotation of the supporting rods and the connecting base, and smoke generated by the smoke sprayer can be shielded conveniently.

The utility model discloses a detection device, which relates to the technical field of building door and window detection and comprises a driving assembly composed of a detection frame, a driving motor, a driving rod and a detection shifting fork. Through cooperative use of the driving assembly and the stress mechanism, the door and window can be circularly pushed and pulled, so that the durability of the door and window can be tested, meanwhile, the stress mechanism can detect the force required by the whole push-pull stroke of the door and window, so that the reaction is comprehensive, the force curve is accurate, the detection result is accurate and reliable, and the detection efficiency is improved. The problems that when an existing building door and window detection device detects the push-pull force, detection is achieved through manual pulling of a pull force scale, the detection mode cannot well reflect data such as a force curve needed when a door and window are pushed and pulled, and meanwhile the service life of the door and window cannot be detected are solved.

The invention discloses a distributed acoustic sensing road cavity detection method based on an active excitation source. The method is based on distributed acoustic sensing (DAS), and comprises the following steps of: firstly, acquiring a DAS signal sampling data sequence to be processed; preprocessing the data of each DAS channel; calculating a short-time zero-crossing rate to remove channels with abnormal data records; then, signal space-time positioning is carried out through sliding window detection, signal envelopes in a signal area are extracted, and space-time distribution information of signals is obtained; and finally, extracting spatial correlation features and energy distribution features of the signals, and realizing automatic detection of the road cavity based on feature judgment. According to the method, all-weather sensing and high-resolution precision characteristics of the DAS array are fully utilized, accurate estimation of road cavity detection is achieved with small calculation amount, large-range sensing can be achieved, and the method is suitable for real-time engineering application occasions.

This invention provides an obstacle avoidance method, system, electronic device, and storage medium for autonomous driving, belonging to the field of autonomous driving control technology. It includes: continuously performing dynamic window detection in front of the vehicle to obtain detection results; dividing the dynamic window into multiple detection areas based on vehicle driving parameters and a pre-set mapping relationship between vehicle driving parameters and detection areas; if the detection results contain obstacles, comparing the relative positional relationship between the obstacles and each detection area to obtain position comparison results; and determining the vehicle's obstacle avoidance control logic based on the position comparison results and a pre-set mapping relationship between the position comparison results and obstacle avoidance control logic for obstacle avoidance. This invention can effectively reduce the time and resource consumption of meaningless window creation, reduce the computational load on the central processing unit, improve the overall vehicle control performance such as control cycle, and increase the efficiency and experience of autonomous driving.

The application provides a door and window hardware motion simulation test system with adaptive passive following and universal adjustment function, which solves the problems of door and window detection motion constraint, and comprises a support frame, a non-powered horizontal guide rail movably installed on the support frame, and a freely-slidable sliding mounting seat installed on the non-powered horizontal guide rail; a swing driving motor for driving the non-powered horizontal guide rail to swing relative to the support frame is installed on the support frame, a rotary driving motor is carried on the sliding mounting seat, an output shaft of the rotary driving motor is connected with a handle operating clamp through a universal transmission assembly, and the universal transmission assembly allows an included angle between the axes of the rotary driving motor and the handle operating clamp while transmitting the rotary torque; when the opening and closing action of the door and window fan drives the handle to produce horizontal displacement, the sliding mounting seat passively slides on the non-powered horizontal guide rail following the displacement. The application has the advantages of good structural stability, strong compatibility and the like.

Various embodiments described herein relate to a method, apparatus, and a non-transitory machine-readable storage medium including one or more of the following: locating a 2D plane correlated with a 3D mesh representing a surface of a room; taking a virtual 2D picture of a 3D mesh along the 2D segment; within the virtual 2D picture, finding a hole; determining the virtual picture floor, and hole width; when the hole intersects the virtual picture floor and is at least as wide as a door width then classifying the hole as a door; else classifying the hole as a window.

This invention belongs to the field of image processing technology and discloses a method, device, equipment, and storage medium for judging the results of COVID-19 antigen detection. By calling a camera device to acquire the detection image based on the user's shooting command input on the user interface, image recognition is performed to complete the antigen detection result acquisition, thereby enabling rapid interpretation of the detection results. Simultaneously, the detection image undergoes a qualification verification process; only after passing the verification is the antigen card area further detected. An affine transformation is performed on the antigen card area to obtain a corrected scaled image before window detection. Finally, the detected window areas are classified. This avoids capturing unqualified images from affecting subsequent recognition results, reduces the probability of false detection, achieves a coarse-to-fine detection process, and improves the accuracy of interpretation.

The embodiment of the invention provides a pop-up window processing method and device and a storage medium. In the embodiment of the invention, based on a collaborative architecture of two AI models, pop-up window processing is divided into two stages of potential pop-up window area analysis and pop-up window feature recognition, a potential pop-up window area in a screen image is analyzed by using a first AI model, and a sub-image of the potential pop-up window area is cut out from the screen image; and performing multi-dimensional feature analysis and popup window feature identification based on the sub-images by using the second AI model, positioning position information of closing options according to the multi-dimensional feature analysis and the popup window feature identification, executing popup window closing operation based on the position information, cutting out the sub-images of potential popup window areas, converting global analysis into local analysis, reducing calculation overhead of irrelevant areas, and on the basis, improving the popup window closing efficiency. Model reasoning is used for analyzing the cut sub-images, the efficiency and accuracy of pop-up window detection and closing are effectively improved, then the probability that automatic testing is interrupted due to pop-up window interference is reduced, and the testing stability and continuity are improved.

The invention provides a security detection method and device for network traffic data, and the method comprises the steps: extracting a plurality of preset network features of each traffic data packet in a target time period, and obtaining a plurality of pieces of evolution data; dividing the plurality of evolution data of the target time period into to-be-detected data of a plurality of windows by using a sliding window; processing the to-be-detected data of each window to obtain a matrix representation of the to-be-detected data; determining an approximation ratio test value distribution based on the matrix representation of the data to be detected; determining a threshold value based on the approximate ratio test value distribution and a preset false alarm probability; whether the to-be-detected data is larger than a threshold value or not is judged, and if yes, it is determined that the to-be-detected data is abnormal data; and processing the abnormal data by utilizing hash function calculation and bit operation, and judging whether the abnormal data is attack data or not according to a processing result. According to the invention, the detection efficiency and detection precision of the network traffic data are improved.

The utility model discloses a door and window thermal insulation detection device, which relates to the technical field of door and window detection equipment and comprises a bottom plate, a thermal insulation detection chamber is arranged in the middle of the top of the bottom plate, a refrigeration assembly is arranged on one side of the exterior of the thermal insulation detection chamber, and a heating assembly is arranged on the other side of the exterior of the thermal insulation detection chamber. A door and window adjusting assembly is arranged in the middle of the interior of the heat preservation detection chamber, ventilation plates are symmetrically arranged on the two sides of the door and window adjusting assembly, temperature adjusting assemblies are symmetrically arranged on the two sides of the interior of the heat preservation detection chamber, and a controller is arranged outside the heat preservation detection chamber and located on one side of the top of the bottom plate. The device can clamp and fix doors and windows, guarantees the stability of the doors and windows in the detection process, can adapt to doors and windows of different sizes through adjusting the distance between the two groups of door and window fixing plates, and improves the adaptability.

Disclosed is a method for detecting a line in an image or a video. The method includes smoothing the image, selecting an area in which a line is to be detected, determining a center pixel in the selected area, applying a window centered on the center pixel, detecting a sum of gradient angle differences of pixels surrounding the center pixel, classifying the center pixel determined as the line candidate into a ridge or a valley, and eliminating a non-maximum pixel.

The utility model provides a window air tightness detection device, which belongs to the technical field of window detection and is provided with four supporting columns, a top plate is fixedly mounted at the tops of the supporting columns, a supporting seat is fixedly mounted on the supporting columns jointly, and a detection frame for placing a window is slidably connected onto the supporting seat. A telescopic cylinder is fixedly installed on the top plate, the movable end of the telescopic cylinder extends downwards, an adjusting plate is fixedly installed at the end of the telescopic cylinder, and a detection cover used for pressing a window body is fixedly installed on the adjusting plate. Two push plates A are transversely and slidably connected to the detection frame, and two push plates B are longitudinally and slidably connected to the detection frame; two sliding rails are fixedly installed on the supporting seat, sliding blocks are connected to the detection frames in a sliding mode, and the sliding blocks are symmetrically and fixedly installed on the bottom walls of the detection frames. According to the utility model, because the placement of the door and window is lack of a positioning function, the door and window may deviate, the placement is not corresponding, the stress is not uniform, and errors occur during detection.

The utility model relates to the technical field of door and window detection, and discloses a building door and window air tightness detection sealing pressurization device, which comprises a detection table, an air tightness detector is fixedly arranged on one side of the upper surface of the detection table, the output end of the air tightness detector is connected with a detection pipe in a penetrating manner, and a sealing box is arranged in the middle of the upper surface of the detection table. One side of the sealing box is in through connection with a connecting pipe, the connecting pipe is in through connection with one end of the detection pipe, a detection window body is arranged above the sealing box, and one side of the upper surface of the detection table is fixedly connected with a fixing frame. According to the utility model, the mounting plate with the movable periphery is adopted to limit and push the door and window and the sealing box with the adaptive size, so that the centers of the door and window and the sealing box are aligned and the edges are attached, and the adjustable fine tuning block adapts to concave-convex surfaces of different door and window edges, thereby improving the test precision; and the spring is utilized to push the push column to bounce the sealing gasket, and the pressing plate is matched to press downwards to extrude the sealing gasket by the edge of the door and window, so that the sealing performance in the detection process is effectively improved.

An embodiment of the present invention discloses a display adjustment method, device, terminal and medium based on a modal window. The method includes: when it is determined to be a modal window, obtaining the parent window of the modal window, and detecting whether the modal window meets the requirements of the standard for creating a modal window effect; when the requirements are met, using the surface hierarchy tree of the parent window as the parent in a synthesizer, creating a display adjustment node in the surface hierarchy tree; obtaining the size and location of the parent window; setting a rectangular frame in the display adjustment node according to the size and location of the parent window; obtaining the rounded corner status information of the parent window; setting the rounded corner status of the rectangular frame in the display adjustment node according to the rounded corner status information; setting the color of the rectangular frame to an inactive color in the display adjustment node, and setting the status of the rectangular frame to an inactive display status; and adjusting the level of the display adjustment node to the highest level.

The embodiment of the invention relates to the technical field of unmanned platform autonomous exploration, and provides a building space autonomous exploration method and system, and the method comprises the steps: carrying out the three-dimensional reconstruction through the image point cloud data of a building space, and obtaining a three-dimensional grid map containing a plurality of voxels; the image point cloud data comprises a visual image and radar point cloud data; performing boundary detection on the three-dimensional grid map to obtain a corresponding boundary detection result; performing door and window detection and room modeling by using the image point cloud data to obtain corresponding door and window semantic information; generating a to-be-executed target viewpoint based on a boundary detection result; selecting an optimal target viewpoint from target viewpoints to be executed by using the door and window semantic information; and performing exploration path planning based on the optimal target viewpoint to obtain a corresponding exploration trajectory, so that the unmanned platform explores the building space according to the exploration trajectory. According to the embodiment of the invention, the problem that the exploration efficiency is greatly reduced due to incomplete exploration or a backtracking phenomenon in the existing method is effectively solved.

The utility model provides a slope device for surveying and mapping mine repair engineering, which comprises a base and a transparent window arranged at the top of the base, scale marks are arranged on the outer side of the transparent window, a rear shell is fixedly arranged at the top of the base at the rear end of the transparent window, and a rolling sleeve is rotationally arranged between the transparent window and the rear shell. A pulling rope is wound on the outer side of the winding sleeve, a guiding pipe is slidably installed between the transparent window and the rear shell, and one end of the pulling rope penetrates through the interior of the guiding pipe. The winding sleeve can rotate along the rotating shaft by pulling the pulling rope, the pulling rope wound on the winding sleeve can be wound and unwound, and the guide pipe can rotate between the transparent window and the rear shell in the pulling process of the pulling rope, so that the scale line can be always located at the detection center part of the transparent window, and the detection accuracy of the transparent window is improved. Therefore, the slope angle can be better measured between the traction rope and the scale line, and the degree on the scale line is more accurate.

The invention discloses a contact window detection method and device based on dynamic density clustering decision. The method comprises the following steps: collecting multi-source physical signals, and performing weighted summation on the multi-source physical signals based on a compensation coefficient to obtain a synthetic Tsyn signal; dividing the continuous synthetic Tsyn signal into a plurality of equal-width intervals and generating a histogram; extracting a maximum count value from the histogram and multiplying the maximum count value by a signal-to-noise ratio correlation coefficient k to serve as a main density threshold value; comparing the main density threshold value with a 0.05 quantile value of the synthesized Tsyn signal, and taking the larger value as a dynamic density threshold value; extracting a section which meets a dynamic density threshold and has a duration exceeding M in the continuous synthetic Tsyn signal as a feature sequence; judging the sample number of the signal amplitude in the feature sequence, and if the sample number is not greater than 5, processing and identifying the contact section by adopting a statistical mode; and if the number of the samples is greater than 5, processing and identifying the contact segment by adopting a clustering mode. According to the invention, real-time accurate identification of the contact window can be realized.

This invention relates to a method for reconstructing existing building information models based on image target detection and unmanned aerial vehicles (UAVs), comprising the following steps: acquiring images of the four facades of a target building taken by a UAV; employing a deep learning object detection model (YOLO V9) to obtain a detection map containing window detection boxes and center point markers, window position information, and window size information; determining the correspondence between the pixel length captured in the image and the actual length; performing edge detection processing on the detection map, and combining the correspondence to obtain the boundary position information of the walls and the actual coordinates of the windows based on the walls; performing parametric modeling of the building envelope based on the boundary position information of the walls, while simultaneously acquiring the building's floor information and the floor plan layout of each floor, thus completing the reconstruction of the entire building information model. Compared with existing technologies, this invention can quickly and easily identify the position and size of the building envelope, thereby facilitating modeling, and has a certain degree of automation and low cost.

The present disclosure relates to a split-screen control method, device and storage medium. The split-screen control method is applied to a terminal, and the terminal has the function of responding to a control instruction triggered by a stylus, including: in response to receiving a control instruction triggered by a stylus, and determining that the control instruction is a control instruction for triggering a split screen, creating a window, the window is used to intercept non-stylus touch events and detect stylus touch events; based on the touch events of the stylus detected by the window, determining the split-screen position of the terminal; based on the split-screen position, splitting the terminal. The split-screen control method provided by the embodiment of the present disclosure can reduce false touches caused by gesture control, reduce system power consumption, and improve system stability.

The application discloses a door and window frame rapid splicing structure quality detection method and system, relates to the door and window detection technical field, and the method comprises the following steps: obtaining initial frame structure measurement results, mechanical arm end dynamic behavior information, laser echo signal space distribution information and local temperature field information; predicting measurement result deviation distribution; calculating total measurement deviation value and measurement fluctuation range according to the measurement result deviation distribution; correcting the initial frame structure measurement results according to the total measurement deviation value to obtain target frame structure measurement results; determining a qualified judgment limit according to the measurement fluctuation range; and generating a quality detection result according to the target frame structure measurement results and the qualified judgment limit. The application can combine the deviation distribution, the measurement deviation value and the measurement fluctuation range to generate the quality detection result, so that the structure quality detection is realized, and the detection accuracy and product quality are improved.

The invention relates to the technical field of image processing, and particularly discloses a pastry quality detection device and method, and the method comprises an obtaining module which obtains and analyzes detection pastry data and pastry image data, and determines the detection image data of each detection pastry; the redundancy module is used for determining multiple view angle image data, multiple view angle-equipment image data, multiple view angle-equipment sequence data and view angle-equipment time data of each detected cake; the determination module is used for determining multiple pieces of window image data and window detection data, and determining cake detection data of each detected cake; and the detection module is used for determining visual detection data and safety detection data of the to-be-detected pastries. According to the method, the limitation that traditional detection data are dispersed and manual intervention is much can be broken through, invalid images are effectively eliminated, the problem of multi-device and multi-view data confusion is solved, an integrated quality evaluation system is formed, and key indexes are accurately controlled through targeted safety detection while high efficiency of full-dose visual detection is guaranteed.

The invention relates to a GNSS (Global Navigation Satellite System) abnormal observation data processing method. The method comprises the following steps of: acquiring satellite GNSS observation data; detecting the GNSS observation data and obtaining a detection result; detecting the GNSS observation data through a sliding window detection method so as to detect instantaneous abnormal data directly related to an observation signal; detecting the GNSS observation data through an exponential weighted moving average method so as to detect slow change abnormal data related to the state characteristics of the satellite; and performing grading processing on the GNSS observation data according to the detection result, wherein the grading processing comprises data exception marking and / or exceptional data elimination. According to the method, multi-dimensional classification detection is carried out on the GNSS observation data, data exception caused by multiple paths is detected through cross comparison of different types of data, the data judgment threshold is clear, the data is divided into two intervals, the data exception is marked or the marked exception data is removed, and the method can be applied to all test stages of satellites.