177 results about "Calibration matrix" patented technology

The invention discloses a temperature control management method for an electric oven, and relates to the technical field of intelligent household appliance control, and the method comprises the steps: carrying out the offset correction of a pre-stored mode mapping table based on a calibration matrix, loading a target temperature interval and a three-dimensional temperature field partition parameter according to a cooking mode selected by a user, and triggering a dynamic learning mark; according to the dynamic learning mark, the furnace chamber temperature and the environment temperature change rate are collected in real time, the power compensation amount is calculated through a thermal inertia compensation model, and a partition power control matrix is generated in combination with the three-dimensional temperature field partition parameters; and distributing the power compensation amount and the partition power control matrix to the heating pipe according to a preset topology, dynamically adjusting power output, and implementing temperature early warning and no-load identification based on an acoustic emission spectrum entropy value. The temperature gradient change rate is calculated through the Fourier heat conduction law, the three-dimensional temperature field partition parameters are dynamically generated, and accurate control over the temperature field in the complex three-dimensional space is ensured so as to cope with environment changes and user demand changes.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

The invention relates to the technical field of nondestructive testing, solves the problems of insufficient flaw detection precision and adaptability of a metal component caused by restriction in the prior art, and particularly discloses a flaw detection system and method for the metal component. The flaw detection system comprises a multi-modal sensor group, a cross-modal feature alignment module, a self-adaptive weight distribution module, a defect decision module and a closed-loop control unit, the flaw detection method comprises the following steps: S100, synchronously acquiring data through the multi-mode sensor group; s200, performing space-time registration on the multi-source data of the metal component by using the dynamic calibration matrix; s300, evaluating the confidence coefficient of the sensor in real time, and generating a defect feature map; s400, inputting the defect feature map into a defect classification network, matching a defect map database, and outputting a defect type and a quantization parameter; and S500, automatically adjusting the detection motion track and the sensor according to the defect parameters. According to the multi-method combined detection for the metal component, the detection precision is improved, and a detection result with higher accuracy can be obtained through comprehensive evaluation according to multi-source data.

The invention relates to the technical field of machine vision, in particular to a calibration method and device for an industrial machine vision measurement system, and the method comprises the following steps: collecting error data through a glass linear scale and a circular target, constructing a standardized calibration data set, obtaining a global error gradient field, a residual error trend map and a residual error weight coefficient table, and obtaining a standard calibration data set; and fusing the size compensation value and the directivity correction value, constructing a calibration matrix, and obtaining a system calibration result. According to the method, through fusion modeling of multi-source error data, the recognition precision of the system on error sources is improved, gradient field construction and trend graph extraction are combined, the directionality and spatial continuity of error compensation are enhanced, and a weight coefficient distribution mechanism is utilized, so that differential control on dynamic and static error influence is realized; and a calibration matrix with multi-dimensional parameter fusion is adopted to optimize the consistency and adaptability of the overall measurement precision, so that the system keeps stable error correction capability and calibration accuracy in a complex environment.

The invention relates to a saline intrusion measurement method, system and equipment based on dynamic ship motion conditions and a medium, and aims to solve the technical problem that the salinity inversion precision is reduced due to spatial-temporal dislocation between particle image velocity measurement flow field data and plane laser-induced fluorescence concentration field data caused by ship six-degree-of-freedom motion. According to the method, future poses are predicted by collecting ship motion parameters and environment disturbance parameters, and space-time compensation parameters are generated in combination with a static calibration matrix; performing three-dimensional coordinate transformation and time synchronization on synchronously acquired original flow field and concentration field data by applying compensation parameters, and eliminating space-time deviation caused by ship movement; a three-dimensional salinity distribution field is calculated based on registration data fusion flow field diffusion characteristics and concentration field distribution; and meanwhile, the pose of the measuring equipment is dynamically adjusted through the closed-loop controller so as to optimize the data acquisition quality. According to the method, ship motion interference is effectively inhibited, and accurate characterization of saline invasion frontal surface spatial characteristics is guaranteed.

The embodiment of the invention provides a parameter pruning method and device for a large language model, equipment and a readable storage medium. The method comprises the following steps: acquiring a first measurement matrix of each network layer in a preset large language model, and determining a pruning rate of the corresponding network layer; determining a plurality of importance scores for a plurality of attention heads contained in each network layer, and constructing a second measurement matrix; determining a first calibration matrix of the second measurement matrix according to the pruning rate and the second measurement matrix, and obtaining a first mask matrix based on the sparse distribution characteristic of the first calibration matrix; pruning a key channel and a value channel; determining a second calibration matrix of the second measurement matrix according to the target number and the second measurement matrix, and obtaining a second mask matrix based on the sparse distribution characteristic of the second calibration matrix; and pruning a query channel and an output channel associated with the second mask matrix to obtain a large language model. Therefore, the storage space of the large language model can be reduced, and the calculation efficiency of the large language model is improved.

The invention discloses a transformer substation intelligent patrol recognition algorithm optimization method based on multi-modal fusion, and belongs to the technical field of transformer substation patrol, and the method comprises the following steps: synchronously obtaining a visible light image, an infrared thermal imaging spectrum, an ultrasonic partial discharge signal and environmental parameters, and building a four-dimensional space-time calibration matrix; a dual-channel attention mechanism is adopted to realize multi-source data fusion, a spatial alignment channel and a feature enhancement channel are included, the spatial alignment channel realizes pixel-level registration of visible light and infrared images through affine transformation, and the feature enhancement channel utilizes a graph neural network to construct topological correlation mapping of acoustic, thermal and optical features; constructing a three-stage training architecture containing noise injection; and adding an adversarial generative network in a preprocessing layer to simulate rain fog and electromagnetic interference environments. According to the method, the identification threshold is updated online by deploying a sliding time window mechanism in an output layer, and the problems of poor feature extraction robustness and high false alarm rate of a traditional algorithm in complex environments such as strong electromagnetic fields and rain and fog are solved.

The invention provides a dynamic calibration virtual system of a piezoelectric six-dimensional force sensor and a calibration method thereof, and the system collects and processes force and torque signals of the sensor in real time through integrating the six-dimensional force sensor, a data collection and processing module, a virtual simulation module, a virtual calibration module and a control module. The calibration matrix is automatically adjusted through a dynamic optimization algorithm, the virtual simulation module performs simulation calculation based on the physical characteristics of the sensor and data acquired in real time to generate optimized calibration data, so that the calibration precision is improved, the system can flexibly adjust calibration parameters in different working environments, and the calibration precision is improved. The sensor is ensured to maintain high-precision output under the change conditions of temperature, humidity and the like, the system effectively reduces coupling errors through an error correction unit and a dynamic calibration algorithm, and the stability and precision of the calibration process are improved.

The invention relates to the technical field of audio processing, in particular to a method and a system for compensating frequency response of a microphone. The method comprises the following steps: obtaining microphone output signal data and multi-dimensional environment data, and carrying out frequency response multi-level feature integration to obtain coupling frequency response data; frequency response-environment parameter correlation analysis is carried out based on the coupling frequency response data, and a frequency characteristic matrix is obtained; acquiring environmental noise monitoring data, and performing frequency band environmental noise calibration to obtain a noise calibration matrix; performing Monte Carlo multi-dimensional parameter optimization based on the frequency characteristic matrix and the noise calibration matrix so as to obtain an optimization compensation strategy; acquiring real-time microphone output signal data, and performing dynamic compensation feedback to obtain dynamic compensation feedback data; and performing user personalized compensation strategy selection according to the dynamic compensation feedback data to obtain a user microphone personalized compensation strategy. According to the invention, the accuracy and quality of audio processing can be improved.

The invention provides a traceability analysis method, device and system based on electroencephalogram signals collected by a mechanical arm. The method comprises the steps that coordinate information of all electroencephalogram electrodes in a first coordinate system is obtained; according to the coordinate information of each electroencephalogram electrode in the first coordinate system and the coordinate transformation relation between the first coordinate system and the second coordinate system, obtaining the first coordinate information of each electroencephalogram electrode in the second coordinate system; calibrating the first coordinate information of each electroencephalogram electrode in the second coordinate system according to the calibration matrix to obtain second coordinate information of each electroencephalogram electrode in the second coordinate system; electroencephalogram signals are acquired through each electroencephalogram electrode; and performing traceability analysis according to the second coordinate information of each electroencephalogram electrode in the second coordinate system and the electroencephalogram signal. According to the traceability analysis method, device and system based on the electroencephalogram signals collected by the mechanical arm, the accuracy of traceability analysis is improved.

The invention provides a real-time visual positioning correction method and device, electronic equipment and a storage medium. The method comprises the following steps: performing feature registration on an initial feature value of an initial image acquired by an image acquisition device and a real-time image feature value to generate a calibration matrix; calibrating the current pixel coordinate of the target identified in the real-time image based on the calibration matrix, and determining the calibrated pixel coordinate of the target; and performing coordinate conversion on the calibrated pixel coordinates based on calibration information of the image acquisition device, and determining real-time positioning information of the target. According to the real-time visual positioning correction method disclosed by the embodiment of the invention, the target information is sensed in real time through deep learning under the condition that only the image acquisition device is used and no additional equipment (a gyroscope, an inertial sensor and the like) is added, and the real-time visual positioning correction method is obtained according to the calibration data obtained in the initialization stage and the image pixel information obtained by target detection. And converting the target to world coordinates to realize real-time positioning.

The invention discloses a multi-source 5G message video cooperative monitoring method and system, particularly relates to the technical field of video monitoring, and is used for solving the problems of multi-camera calibration parameter misalignment and video fusion dislocation caused by thermal deformation asynchronism in the prior art. A temperature sensor is arranged on an industrial camera supporting structure, a thermal deformation three-dimensional displacement compensation vector is calculated, a preferential transmission object is marked according to the displacement gradient of an adjacent camera and a material heat conductivity coefficient threshold value, and the displacement vector is transmitted to an edge calculation node according to the priority through a 5G message. All displacement data are fused at a node end to dynamically correct space calibration parameters, a collaborative calibration matrix is generated, and finally, the matrix is utilized to perform real-time registration on multiple paths of video streams and perform workpiece defect detection, so that the geometric consistency of a multi-camera system in a thermal deformation environment is guaranteed, and the visual detection precision and stability are improved.

The invention belongs to the technical field of medical robots, and discloses a surgical robot accurate pose real-time registration method and system based on multi-sensor fusion, and the system comprises a multi-source sensing collection assembly; a time-space calibration mechanism; fusing a resolving platform and an exception elimination platform; a deformation sensing and dynamic compensation device; and a registration result closed-loop feedback unit. According to the invention, a multi-source sensing acquisition assembly and a synchronous control board are integrated on a hardware level, a nanosecond timestamp is generated by using a high-precision clock driven by an onboard crystal oscillator, and a cross-correlation delay calculation chip is combined to execute a cross-correlation function method delay estimation formula, so that microsecond-level accurate correction of delay amount among sensing sources is realized; the coordinate transformation unit automatically selects a matched calibration matrix according to a correction result, multi-source data such as optical positioning, inertial measurement, a torque sensor, ultrasonic or electromagnetic positioning and the like are unified to a global rectangular coordinate system, and the structure can effectively ensure the consistency of time and space references of data output by different sensors.

The invention discloses a DTG printing method and system based on line scanning camera visual deformation and rigid body positioning, and relates to the technical field of digital textile printing. Dynamic images are collected in real time through a line scanning camera, and image coordinates are mapped to a printing equipment world coordinate system through a coordinate calibration module in combination with a calibration matrix; real-time error compensation is used for dynamically eliminating coordinate offset, and a dual-mode positioning module intelligently switches a positioning strategy; rigid body positioning realizes accurate positioning of non-deformation materials through template matching; deformation positioning performs adaptive compensation based on feature point extraction and grid deformation analysis, an image mapping module inversely corrects target pattern deformation according to a positioning result to generate a high-precision TIF image, the high-precision TIF image is converted into a printing instruction through an RIP processing module, and an integrated control module performs one-key triggering and dynamic parameter configuration and performs printing on a target pattern. The collection efficiency is optimized through light source synchronization and self-adaptive triggering, the system stability is improved in combination with a template database and an exception handling module, and high-quality printing of complex deformation materials is achieved.

According to the robot vision calibration device and method, through collaborative design of the industrial personal computer, the vision system, the robot, the calibration jig and the calibration plate, the problems caused by complex structure, low calibration precision and poor adaptability in the prior art are solved. A floating mechanism of the calibration jig is combined with a displacement sensor to realize multi-degree-of-freedom dynamic displacement compensation, so that mechanical positioning errors are remarkably reduced; the conical surface guide structure and the reference hole layout of the calibration plate optimize the stability of the positioning reference, and ensure the accurate embedding of the calibration needle. The flexible installation of the visual system adapts to different scene requirements. According to the calibration method, the generation of a high-precision visual calibration matrix is realized through the processes of multi-reference hole coordinate recording, calibration ring coordinate calculation and visual image acquisition and matching. According to the whole scheme, through a dynamic compensation mechanism and intelligent process design, the calibration precision, efficiency and robustness are improved, meanwhile, the operation complexity is reduced, and reliable technical support is provided for an industrial automation scene.

The invention discloses a three-dimensional calibration automatic system suitable for a six-axis physiotherapy robot, and relates to the technical field of robot vision calibration and automatic control, and the system comprises an equipment communication management module which automatically identifies and distributes connection equipment through an intelligent detection technology, and outputs an equipment control instruction; the data acquisition and processing module is used for identifying 13-point feature pixels through RGB information, performing deep matching, calculating 13-point theoretical feature three-dimensional information and outputting an optimized calibration matrix MB2R in combination with a transformation relation between the camera and the tail end of the mechanical arm and a base coordinate transformation relation; and the result verification and backup uploading module is used for converting the relative position of the three-dimensional feature platform and the robot, acquiring three-dimensional information of an evaluation point, controlling the mechanical arm to move to an optimization point position, and calculating a pixel difference value and a height difference value. The method has the advantages that full-automatic millimeter-level calibration precision is achieved through a collaborative structure of multi-protocol heterogeneous equipment intelligent integration, iterative optimization algorithm fitting and closed-loop double-index verification.

The invention provides an automatic calibration method and device for a mechanical arm, electronic equipment and a storage medium, and the method comprises the steps that the mechanical arm is controlled to conduct RCM motion in the Y-Z axis direction, and the pitch angle range is determined; performing multiple RCM rotation sampling in the pitch angle range, and determining a motion deviation error of each rotation position point based on the Y-axis unit pixel actual distance, the first needle point pixel coordinate, the first space point coordinate, and the second needle point pixel coordinate and the second space point coordinate of the rotation position point under each RCM rotation sampling; determining a calibration matrix based on the motor position of each rotation position point and a forward kinematics equation of the mechanical arm; and least square method processing is conducted on the multiple motion deviation errors and the calibration matrix, and the calibration amount of the to-be-calibrated parameters of the mechanical arm in the YZ axis calibration direction is determined. The device is low in cost, low in calibration time consumption, high in calibration accuracy and automatic, and factory calibration of the mechanical arms can be carried out on a large scale.

The invention discloses a mechanical arm intelligent grabbing system and method based on three-dimensional vision. The system comprises a three-dimensional vision system, an image preprocessing module, a model matching module, a grabbing point generation module, a grabbing control module and a dynamic environment self-adaption module. The method comprises the steps of multi-modal reference model collection and feature library establishment, precise hand-eye calibration and grabbing strategy self-learning and multi-modal fusion recognition, and dynamic prediction and self-adaptive grabbing. By adopting the three-dimensional visual recognition system and combining algorithms such as point cloud processing, point cloud method phase generation, point cloud downsampling, rough model matching and fine model matching, the limitation that a traditional mechanical arm system depends on two-dimensional image training and position coordinate point matching is overcome; by generating the grabbing points and calculating the pose coordinates of the grabbing points according to the hand-eye calibration matrix, the problem that the estimated pose of the grabbed object under the camera coordinate system is inaccurate in the prior art is solved. The system is easy to implement and has high practicability and popularization value.

The embodiment of the invention provides a robot control method and system based on virtual reality and hand motion capture, and the method comprises the steps: collecting the hand posture data of a user, obtaining the first hand posture data of the user through a virtual reality device, the first hand posture data comprises the hand end posture information, and obtaining the first hand posture data of the user through a virtual reality device; obtaining second hand posture data of the user through wearable hand motion capture equipment, wherein the second hand posture data comprises hand joint fine motion information; standardizing the hand position and posture data, and mapping the hand position and posture data into robot target motion data based on a preset calibration matrix, the robot target motion data including first robot target motion data mapped based on the first hand position and posture data and second robot target motion data mapped based on the second hand position and posture data; robot motion control data is generated based on the robot target motion data, and robot parts are controlled to move. According to the robot control method and system based on virtual reality and hand motion capture, the operation precision of the robot can be greatly improved, the operation real-time performance and smoothness are enhanced, and the motion state of the robot can be monitored in real time.

The invention discloses a micro-fluidic chip imaging positioning device and an imaging positioning method, and belongs to the technical field of micro-fluidic chip positioning. According to the technical scheme, the method comprises the following steps: taking a photo of a micro-fluidic chip by using a macroscopic camera, and carrying out distortion correction; carrying out mark point or channel identification on the micro-fluidic chip according to the micro-fluidic chip mark point or channel identification model; performing coordinate transformation according to the calibration matrix to obtain a pixel coordinate set in microscopic camera coordinates, namely a positioning coordinate set of the micro-fluidic chip; coordinates of any position of the micro-fluidic chip can be obtained according to the positioning coordinate set, and imaging positioning of the micro-fluidic chip is completed. The micro-fluidic chip clamp is applied to the aspect of micro-fluidic chip imaging positioning, overcomes the defects caused by an existing micro-fluidic chip clamp, solves the technical problems of universality, automation and high precision of micro-fluidic chip positioning, and has the characteristics of rapidness, automation, high precision and universality.

Techniques are provided for real-time calibration of an eye-tracking system in a wearable display device using user interactions with a graphical user interface. A background calibration process continuously or intermittently updates a calibration matrix based on stable gaze-target pairs inferred from natural user behavior, without requiring explicit calibration routines. The system detects user interactions such as selections of UI elements, and associates them with gaze direction data captured immediately beforehand. Fixation filtering is applied to identify stable gaze intervals based on dispersion thresholds and minimum duration criteria.

The embodiment of the invention provides an RGB-IR sensor calibration method and device, a storage medium and an electronic device, and the method comprises the steps: obtaining a first calibration matrix containing IR signal residues and a second calibration matrix which filters IR signal residues according to a filtered IR signal of a to-be-calibrated scene and a to-be-calibrated signal containing the IR signal; and obtaining an optimized calibration matrix according to the first calibration matrix and the second calibration matrix so as to calibrate the RGB-IR sensor. Through the method and the device, the problems of poor calibration accuracy and stability and low scene color rendition correctness of the IR filtering filter in related technologies are solved, and the effects of improving the calibration accuracy and stability of the IR filtering filter and improving the scene color rendition correctness are achieved.

The invention belongs to the technical field of intelligent traffic sensor calibration, and particularly relates to an end-to-end thunder all-in-one machine automatic calibration system and method based on deep learning, and the system comprises a data collection module, an image target detection module and a calibration network module. The system collects images and three-dimensional coordinates of a target point through a camera and a radar. Identifying a target in the image by using a deep learning model and acquiring pixel coordinates; and the calibration network learns the mapping relation between the image and the radar point, and directly outputs a high-precision calibration matrix T. According to the method, end-to-end automatic calibration in the whole process is achieved, manual arrangement of calibration plates or selection of corresponding points is not needed, efficiency is remarkably improved, and stable operation can be achieved in complex scenes such as dynamic traffic flow, night and rainy days. Through deep fusion of the high-dimensional features of the Leiyu, error accumulation of step-by-step calibration is effectively avoided, the projection error can be controlled within 5 pixels, and the high-precision sensing requirement of intelligent traffic is met.

The invention discloses a projector calibration method and device, a storage medium and electronic equipment, and relates to the technical field of projection, and the method comprises the steps: sequentially projecting a plurality of pictures of different colors through a projector, and carrying out the color coordinate collection of the plurality of pictures, and obtaining the test color coordinate data of a plurality of colors; performing combination processing on the test color coordinate data of the plurality of colors according to a projection calibration matrix layout to obtain a test polynomial matrix; performing conversion and rearrangement processing on the test polynomial matrix to obtain a test color gamut matrix matched with the specification of the projection processor; and calculating according to the test color gamut matrix and a standard color gamut matrix to obtain a color gamut mapping conversion matrix, so that the projector performs projection based on the color gamut mapping conversion matrix. According to the invention, the color of the projector can be effectively calibrated, the color distortion improvement effect of the projector is improved, and the user experience is improved.

The invention belongs to the technical field of target detection, and particularly relates to a fast target detection algorithm based on multi-modal fusion, which comprises the steps of data acquisition and calibration, synchronous acquisition of images and point clouds and completion of internal and external parameter calibration; performing data enhancement operation such as zooming on the image, performing enhancement such as Pill-processing on the point cloud, and sharing geometric transformation to keep bimodal alignment; obtaining a two-dimensional bounding box and a confidence coefficient by using a YOLOv5 network; obtaining a three-dimensional bounding box by using a PointPillars network, and outputting a projection frame and a confidence coefficient; projecting the three-dimensional bounding box to a pixel plane according to the calibration matrix to form a bounding box; when the intersection-to-union ratio is greater than 0.5, generating a matching pair; each matching pair is coded into a 14-dimensional vector; vectors are input into a multi-layer perceptron, single-mode false detection is effectively inhibited through geometric and semantic consistency screening, 14-dimensional input features learn a nonlinear relation of a multi-mode bounding box through a three-hidden-layer MLP, and fusion precision is remarkably improved.

An automatic end effector tip position initialization mechanism for a micromanipulation system is provided. The method includes: S1, positioning a macro end effector tip, calculating a camera pose by using ArUco Tag, and realizing a three-dimensional positioning of the end effector tip by using a triangulation method, and moving the end effector to a area near a center of a focal plane based on a visual servo; S2, positioning a micro end effector tip; S3, acquiring calibration data based on a visual servo, calibrating the end effector tip and the petri dish, and obtaining a left calibration matrix and a right calibration matrix reflecting the transformation relationship between left and right micromanipulator coordinates and image coordinates; generating an intuitive human-computer interaction interface based on a mouse and a keyboard according to acquired position information. The method has obvious advantages in terms of operation accuracy, operation efficiency and repeatability.

The invention discloses a dynamic environment multi-line radar calibration method and system, and relates to the technical field of road radars, and the system comprises a dynamic reference field generation unit which is disposed on the ground of a radar coverage area, is composed of a reflection target array with known space coordinates, and is adjustable in target distance, and a multi-line laser radar unit which is provided with a horizontal field angle of not less than 120 degrees, and is provided with a multi-line laser radar unit. The number of lines in the vertical direction is larger than or equal to 128 lines per frame, the radar body is installed on the portal frame and connected with the control device, the environmental parameter sensing unit collects the temperature, three-dimensional acceleration and power supply voltage data of the radar body in real time, and the distortion compensation module constructs a space distortion field model based on real-time point cloud feedback and environmental parameters of a dynamic reference field, outputs a calibration matrix and outputs the calibration matrix. And the multi-radar collaborative calibration module is used for synchronizing space-time reference through a PTP protocol. Through a real-time compensation mechanism driven by environmental parameters, vibration acceleration and temperature change data are converted into space coordinate correction, and high-precision stable measurement in a dynamic environment is achieved.

The invention relates to a home safety control method and system based on big data analysis, and belongs to the technical field of home safety control, and the control method comprises the steps: obtaining an original sensor data flow in a home environment; preprocessing the original sensor data stream to obtain a synchronous calibration matrix; calculating a temperature change rate, a smoke concentration accumulation amount, a current harmonic component and a temperature current correlation coefficient to obtain a characteristic matrix; performing anomaly detection on the temperature change rate, and outputting a temperature isolation score; calculating a smoke risk coefficient according to the smoke concentration accumulation amount; reconstructing the current harmonic component, and calculating a current reconstruction error; determining an environmental risk coefficient according to the door and window state; carrying out weighted fusion on the data, and calculating to obtain a dynamic risk index; and sending a corresponding safety control instruction according to the dynamic risk index. According to the invention, the potential safety risk can be efficiently and accurately identified, the control strategy is adjusted according to the real-time data, and more efficient and safer home environment guarantee is provided.

The invention provides an ore rock lumpiness measurement method and device, electronic equipment and a readable storage medium. The method is applied to the technical field of ore rock lumpiness measurement, and comprises the following steps: calculating a gradient value corresponding to any pixel in a grayscale image and a gradient difference standard deviation value; if the corresponding pixel is the candidate edge pixel, a plurality of corresponding associated pixels on the candidate edge fusion image are voted; according to the votes, whether the corresponding pixels are candidate edge fusion pixels is judged, and a mine rock segmentation map is obtained; obtaining a point cloud image mapping relation according to the internal reference calibration matrix and the external reference calibration matrix; according to the point cloud image mapping relation, mapping and plane fitting are carried out on the ore rock three-dimensional point cloud collected by the laser radar and the ore rock segmentation map, the point cloud is projected to a fitting plane, sparsity screening is carried out, and a high-density target rock point cloud is obtained; and calculating a calibration coefficient corresponding to the rock lumpiness. In this way, the accuracy of ore rock lumpiness calculation can be improved.

The embodiment of the invention provides a hand-eye calibration method, device and system based on a genetic algorithm. The method comprises the steps that point cloud data collected by a line structure light sensor are acquired for multiple times, and corresponding feature points are determined; determining the coordinate of each feature point in a flange plate coordinate system and the coordinate of each feature point in a sensor coordinate system; determining an initial value of a hand-eye calibration matrix through an ICP algorithm based on the coordinates of the plurality of feature points in the flange plate coordinate system and the coordinates of the plurality of feature points in the sensor coordinate system; and taking the initial value of the hand-eye calibration matrix as a priori solution of a genetic algorithm, and completing the solution of the optimal hand-eye calibration matrix through multiple iterations. According to the method, the calculation efficiency is guaranteed, high-precision global optimization is achieved, then it is ensured that the robot system completes complex operation tasks based on the accurate coordinate conversion relation, and the production quality and efficiency are improved.