363 results about "Transformation parameter" patented technology

The invention provides an industrial defect image generation system and method based on deep learning, and relates to the technical field of artificial intelligence. A defect form adaptive module, a physical attribute modulation module and a multi-mechanism fusion module are integrated in a defect image generation module; dynamically selecting a feature extraction unit according to the defect type label based on a pre-constructed generative network model, and generating a defect feature map; generating an affine transformation parameter based on the physical attribute vector, and performing channel-by-channel linear modulation on a defect feature map of a middle layer of the generative network model, so that a multi-scale defect feature map finally generated by the generative network model contains specified defect type features and physical attribute features; and fusing the multi-scale defect feature image and the defect-free background image to obtain a fused defect image. The problems that in the prior art, industrial defect image generation is insufficient in sense of reality, poor in controllability and poor in fusion effect are solved, the method can be used for data enhancement of industrial visual inspection, and downstream model performance is improved.

The invention belongs to the field of data processing, particularly relates to a standardized processing method, system and equipment for original engineering parameters of a flight simulator, and aims to solve the problems of serious field limitation, low processing efficiency and poor adaptive capacity in the prior art. The method comprises the following steps: analyzing a predefined configuration file, and dynamically generating a database temporary table matched with an original data file structure according to field structure information and a parameter mapping rule defined in the configuration file; directly loading original engineering parameter files into the temporary table by adopting a batch loading mechanism based on a file system path, and executing multi-file parallel import based on a task queue; and converting calculation logic in the parameter mapping rule into an executable query expression, and directly exporting a standardized parameter file after calculation is executed through the temporary table. According to the method, field limitation is broken through, efficient processing of mass data is achieved, instant updating of service logic is achieved through configuration rules, and the maintenance cost is greatly reduced.

The invention relates to the technical field of data processing and mode recognition, in particular to a bearing fault diagnosis method based on multi-scale feature fusion, which comprises the following steps: fusing multi-source data such as vibration, acoustic emission and rotating speed, performing angle domain resampling by using rotating speed data, generating a two-dimensional order spectrogram, and stacking to construct a three-dimensional working condition information tensor; a master-slave modulation heterogeneous neural network is adopted, high-dimensional spatial-temporal features are extracted through a main branch three-dimensional convolutional network, time sequence details are extracted from an original sequence through an auxiliary branch one-dimensional convolutional network, affine transformation parameters are generated, and dynamic modulation is achieved on the high-dimensional features; and the output state vector is mapped to a fault evolution knowledge graph, probability prediction is carried out through a graph attention network and by introducing a Monte Carlo discarding mechanism, a probability mean value is calculated as a fault classification result, and the diagnosis confidence is quantified by a probability variance. According to the invention, through multi-scale feature fusion and dynamic modulation, the problem of insufficient feature discrimination caused by scale mismatch under variable working conditions is solved.

The invention discloses a global registration algorithm for low-overlapping-rate point clouds, and relates to the technical field of point cloud registration, and the algorithm comprises the steps: constructing a multi-scale descriptor under a multi-radius neighborhood for key points in a source point cloud and a target point cloud, screening a rough matching set through employing an evolutionary stabilization strategy, and obtaining a first-order matching set; based on a corresponding propagation mechanism, aggregating each matching element in the first-order matching set into a global candidate set through a corresponding propagation operation; and according to the aggregated global candidate set, generating transformation parameters with the same number, screening an optimal transformation parameter through a quality function, performing coarse alignment on the point clouds, and performing fine optimization through a truncation-ICP (Inductively Coupled Plasma) algorithm. Therefore, the global registration algorithm for the low-overlapping-rate point clouds can effectively cope with the situation of missing part of the point clouds, has relatively high robustness, and improves the registration precision under the conditions of low resolution and low overlapping rate.

The invention discloses a multi-modal remote sensing image registration method based on domain self-adaption and an optimizer set algorithm, and relates to the field of image processing, and the method comprises the steps: respectively extracting the feature vectors of a reference image and an image to be registered, carrying out the feature matching of the feature vectors according to the class label containing conditions of the feature vectors, and obtaining a registration result; obtaining a feature matching vector based on a matching result; estimating transformation parameters corresponding to the affine transformation model by using a random sampling consistency algorithm, and optimizing the affine transformation model according to an estimation result and similarity measurement; and performing spatial transformation on a to-be-registered remote sensing image by using the optimized affine transformation model to realize spatial alignment between the reference image and the to-be-registered image. According to the method, firstly, the feature points of the multi-modal remote sensing image are extracted, the difference of feature vectors is reduced by using a domain adaptive method, the precision and robustness of feature matching are improved, transformation parameters are optimized through an optimizer set algorithm, and search is prevented from falling into local optimum.

The invention relates to the technical field of water quality prediction. The pollution risk prediction method and device based on multi-source water quality data fusion, the equipment and the medium are provided, and the method comprises the following steps: carrying out multi-source collaborative acquisition processing on hydrological data, meteorological data and real-time water quality monitoring data to generate a time-space association data set; carrying out pollution scene feature coupling analysis processing on the space-time correlation data set to generate a pollution type identification matrix and a dynamic driving parameter; collaborative activation processing of the process modules is carried out through a scheduling engine, and a target simulation module topology network is generated; performing adaptive parameter configuration processing on the topological network of the target simulation module to generate a migration and transformation parameter tensor; performing dynamic evolution processing on the migration and transformation parameter tensor to generate a risk early warning hologram spectrum; and carrying out multi-dimensional parallel rehearsal processing on prevention and control measures to generate a treatment efficiency quantitative report so as to improve the pollution characteristic analysis precision and optimize the accuracy of a prediction result.

The invention relates to the field of assembly automation and flexible manufacturing, and discloses an assembly method for a prefabricated firewall of a transformer substation, and the method comprises the following steps: setting a plurality of space positioning reference points at the edge of a wallboard; acquiring three-dimensional coordinate data to form a first point set and a second point set; calculating an initial rigid body transformation parameter between the wallboards according to the first point set and the second point set; establishing an energy functional for describing a total error; carrying out minimization solution on the energy functional by adopting a variational method to obtain an optimal disturbance vector field; according to the optimal disturbance vector field, a plurality of fine adjustment actuators of the wallboard are controlled, posture fine adjustment is conducted, and physical assembly connection of the wallboard and the target wallboard is completed. According to the invention, accurate attitude estimation and error correction in the wallboard assembly process are realized; the robustness and geometric fitting capability of the assembly process are improved; the assembly precision and efficiency are remarkably improved, and the problem that traditional redundancy control distribution is unstable is solved.

The invention discloses a multi-modal image registration method and system based on deformation adaptation and computer equipment, and the method comprises the steps: collecting a plurality of groups of multi-modal images, carrying out the gray standardization, and constructing a diversified registration data set; building a registration network model comprising a pyramid coding module, a deformation adaptive module, a cross-modal interaction module and a registration parameter estimation module; inputting an image pair into the modules in sequence, respectively extracting basic feature mapping, deformation feature mapping and interaction enhancement feature mapping, and finally outputting an estimation conversion parameter matrix; a training process is supervised through a preset loss function, optimal network parameters are selected, and a trained registration model is obtained; in practical application, an image pair to be registered is input into the trained model, a conversion parameter matrix is obtained, and image registration is completed. The multi-modal image registration performance can be effectively improved, and the method still has good robustness and adaptability especially under the condition that serious geometric distortion and significant modal difference exist.

The invention provides a block adjustment method and device for a multi-source remote sensing image, and solves the problem that the splicing precision and efficiency of the multi-source remote sensing image in a large area are low. All panchromatic images and a multispectral image corresponding to each panchromatic image are distinguished from the image data set, and connection points of the panchromatic images are extracted; carrying out connection point matching among all panchromatic images; performing connection point matching between the panchromatic image and the corresponding multispectral image; performing control point matching on all panchromatic images and a satellite remote sensing orthographic reference image; obtaining object space coordinates of each matching point; based on the object space coordinates of the connection points and the RPC parameters of the images corresponding to the connection points, resection is carried out, and error points and error images are removed; joint adjustment is carried out on the connection points and the control points, and transformation parameters and object space coordinates of the connection points of each image are calculated; and according to the object space plane coordinates after the connection points are corrected, obtaining the elevation based on the DEM, comparing the obtained elevation with the original elevation, and if the obtained elevation value does not change, correcting the original RPC parameter file.

The invention provides a credible permission mutual recognition method, device and system for cross-domain agents, and relates to the technical field of artificial intelligence security, and the method comprises the steps: receiving a call request from a source domain agent for executing a corresponding operation on a target domain agent; obtaining an affine transformation parameter set through a permission conversion model according to the space relation characteristics of a source domain permission space and a target domain permission space involved in the calling request, and finally screening out affine transformation parameters suitable for permission voucher mapping by utilizing cone volume measurement evaluation and security verification, and mapping an original permission voucher of the source domain agent from a source domain permission space to a target domain permission space by using the affine transformation parameter, and generating a mapped permission voucher which can be identified and verified by the target domain. According to the method, the permission space is geometrized, and affine transformation, space division and geometric calculation are utilized, so that the permission credible mutual recognition problem in cross-domain agent collaboration is effectively solved.

The invention discloses a small sample crack recognition method and system based on transfer learning, and the method comprises the steps: constructing an innovative and deep-coupled neural network architecture, and enabling a self-adaptive crack perception attention module integrated with a parallel asymmetric convolution kernel to recognize the linear geometric features of a crack, and generating an attention graph; then, the attention map is adopted to carry out pixel-by-pixel signal pre-modulation on an original input image, the enhanced image is sent to a pre-training MobileNetV4 backbone network integrated with a feature-level linear modulation layer, and channel-level dynamic adaptation of feature flow in the network is realized by learning affine transformation parameters; and finally, the extracted depth features are sent to a Gaussian naive Bayes classifier for classification, and depth geometric analysis is carried out on the attention map so as to realize interpretable fracture severity evaluation. According to the method, the accuracy, robustness and interpretability of crack identification are remarkably improved, and the technical bottleneck in a small sample scene is effectively solved.

The invention provides an image target intelligent tracking and positioning method and system under Beidou position constraint, and relates to the technical field of Beidou positioning, and the method comprises the steps: obtaining a multi-source heterogeneous data flow, constructing a multi-modal representation model, and building a coordinate system dynamic projection transformation relation; generating a search mask to limit a tracking range by using the Beidou position information; bayesian correction is carried out on a tracking result through double-domain consistency measurement; and dynamically updating projection transformation parameters and feature weights based on residual distribution. According to the invention, deep fusion of vision and Beidou information is realized, and the precision and robustness of target tracking in a complex scene are improved.

The invention discloses a safety auditing method and device supporting data owner replacement, equipment, a medium and a product, and relates to the field of auditing, and the method comprises the steps that if the data ownership is changed, an original data owner calculates a trap door parameter value based on a random number j and a private key of the original data owner; the new data owner calculates a conversion parameter value according to the trap door parameter value and a private key of the new data owner; the cloud server calculates the label of each data block after the data ownership is changed according to the conversion parameter value, the reciprocal of the random number j and the label of each data block before the data owner changes; the cloud server generates auditing evidences according to the labels of the data blocks and the inquiry information; the third-party auditor carries out safety auditing according to the auditing evidence and the inquiry information, and after the data ownership is changed, it can be ensured that the changed data owner can still ensure the continuity of data integrity verification under the condition that all the data are not downloaded.

The invention provides a non-contact measurement-oriented three-dimensional point AI registration and tolerance analysis method and system, and relates to the field of three-dimensional point AI registration and tolerance analysis, and the method comprises the steps: synchronously collecting point cloud data through multiple sensors, and carrying out the adaptive preprocessing, and obtaining a preprocessed point cloud pair; constructing a multi-scale geometric descriptor by using a local curvature and a normal vector, and performing multi-scale hierarchical feature extraction to obtain an enhanced point feature set; calculating a bidirectional matching probability matrix by using an optimal transmission theory, screening high-confidence point pairs through spatial compatibility constraint to obtain a high-confidence matching point pair set and a matching score, performing transformation matrix estimation and quantification uncertainty, and outputting optimal rigid body transformation and a covariance matrix thereof; self-adaptive optimization is executed in a layered mode, the search range is dynamically adjusted, transformation parameters are verified in a cross-scale mode, and a convergent accurate registration result is output. The method is used for overcoming the defect that in the prior art, low-overlapping-rate registration precision is low.

The application discloses a similarity optimal on-board image registration method based on inertial navigation data fast convergence, and comprises the following steps: constructing a projection transformation model between an image transformation matrix, a to-be-registered image and a reference image; obtaining the projection transformation matrix according to the field of view optical axis position and three-angle offset of the satellite at the imaging moment and coarse registration; performing global coarse matching by using the projection transformation model and the projection transformation matrix to obtain a coarse matching image; decoupling the image transformation matrix according to a mathematical model to obtain a to-be-solved transformation parameter, substituting the to-be-solved transformation parameter into the image transformation matrix to obtain an optimal transformation matrix, and applying the optimal transformation matrix to the to-be-registered image to obtain a fine registration image; and calculating the similarity of the fine registration image and the reference image according to a local normalized image similarity measurement algorithm to obtain an optimal matching result. The application has the advantages of meeting the on-board calculation capacity requirement, meeting the image processing precision requirement and meeting the on-board storage resource requirement.

The invention provides a video fusion method and device based on a three-dimensional scene and a storage medium, and belongs to the technical field of video surveillance, and the method comprises the steps: obtaining video frame data, including a video frame image and a corresponding timestamp, collected by a camera installed on sports equipment, obtaining space-time information of the sports equipment, comprising geographic data and corresponding timestamps; extracting image feature points of each video frame image, and constructing a mapping relation between the image feature points and the geographic data; based on the mapping relation, the pose data of the camera is iteratively solved by minimizing a re-projection error, and an objective function corresponding to the re-projection error comprises strong constraint optimization of geographic data; and determining a conversion parameter between a first coordinate system of the video frame image and a second coordinate system of a pre-constructed three-dimensional scene model based on the pose data to obtain a video fusion parameter, so as to fuse the multi-frame video frame data with the three-dimensional scene model, thereby improving the fusion effect of the three-dimensional scene and the video.

The invention discloses a self-adaptive spatial transformation network for cross-medium optical distortion, and solves the problem that the existing spatial transformation network is difficult to consider both global transformation and local nonlinear optical distortion. The network is integrated in a target detection model, and the method comprises the steps that firstly, a parameter prediction network synchronously outputs affine transformation parameters and a water surface fluctuation physical parameter diagram; then, the local distortion grid generation network generates a distortion sampling grid based on the parameter diagram and a predefined water surface fluctuation physical model; meanwhile, the grid generator generates an affine sampling grid according to the affine transformation parameters; then, a grid fusion device fuses the two grids to generate a final sampling grid; and finally, the sampler samples the input image or the feature map according to the final sampling grid to obtain an output image or a feature map. Through a space transformation mechanism guided by physical prior, optical distortion caused by water surface fluctuation can be effectively relieved, and the accuracy and robustness of a target detection model in a cross-medium underwater scene are remarkably improved.

The invention discloses a high-fidelity splicing method and system for an unmanned aerial vehicle image, and the method employs a dual-stage processing strategy, achieves precise positioning through coarse-fine two-stage alignment, calculates an overall transformation parameter through global estimation to complete coarse alignment, refines non-rigid offset through local correction, and achieves high-fidelity splicing of the unmanned aerial vehicle image. The feature weight is dynamically adjusted in combination with self-adaptive enhancement, and geometric smooth constraint is used for limiting regional displacement; smooth weights with continuous boundaries and the weight sum being 1 are generated based on the shape of the overlapping region, images are decomposed to different frequency layers for layer-by-layer fusion, and the system comprises an image acquisition unit, a dual-stage processing unit and an output display unit and can be deployed on the end side of an unmanned aerial vehicle or a ground station. The method is high in alignment precision, can process large parallax and non-rigid deformation, is traceless in fusion, is high in structural fidelity, and meets the real-time processing requirements of airborne or edge equipment.

The invention relates to the technical field of image processing of financial and medical scenes, and discloses an image-to-video generation method and device, computer equipment and a storage medium, and the method comprises the steps: receiving an input image, and obtaining a corresponding multi-scale depth map; extracting spatial-temporal feature sequences of the input image and the corresponding multi-scale depth map, and performing coding and dynamic modeling to obtain six-degree-of-freedom motion transformation parameters; performing continuous frame transformation and synthesis on the input image by using the six-degree-of-freedom motion transformation parameter to generate an initial output video sequence; performing authenticity discrimination on a global time sequence level and a local space-time detail level by using the initial output video sequence to generate an authenticity score; and iteratively executing optimization processing on the input image until the authenticity score reaches a preset threshold value, and generating a final output video sequence with high space-time coherence. The method has the advantage that the space-time consistency and the reality sense of the generated video are remarkably improved.

The embodiment of the invention provides a program conversion method and a compiling device, and relates to the technical field of computers, in the method, an IR file of a GPU program is obtained to serve as a to-be-converted file, for a function parameter of any function in the to-be-converted file, the function parameter is converted into a function parameter matched with a CPU, and the converted parameter is obtained. And determining the IR statement having data dependence with the converted parameter as a statement to be adapted. And for any statement to be adapted, adjusting the statement to be adapted based on the parameter type of the converted parameter and the thread execution mask of the CPU. And compiling the adjusted to-be-converted file to obtain an executable file adaptive to the CPU. The GPU programs written by different high-level languages can be converted into the IR file, so that the conversion is carried out from the dimension of the IR file, and the support for different high-level languages can be realized. Therefore, the limitation is lower.

The invention relates to methods for providing geodetic datum transformation information applicable to a region of the Earth, for use by a navigation satellite system (NSS) receiver and / or a processing entity capable of receiving data therefrom. In one embodiment, the method comprises: generating (s10a) similarity transformation parameters and, for each of a plurality of reference stations in the region, post-fit transformation residuals applicable to the reference station; and sending (s20a) the transformation parameters and output residuals applicable to output points, wherein an output residual is one of the post-fit transformation residuals or derived therefrom; and the distribution of the output points in the region depends on the distribution of the reference stations. The invention also relates to variants of the above-mentioned method, to methods for receiving and processing the geodetic datum transformation information, and to systems, computer programs, and computer program products for carrying out such methods.

The invention provides an automatic trapezoid correction method and system for a projector and a medium. The method comprises the following steps: acquiring attitude information of the projector and spatial information of the projector and a projection surface; under the condition that optical emission parameters of the projector are known, a projection geometric model is established based on the attitude information and the space information, and transformation parameters used for projector trapezoidal correction are generated accordingly. And controlling the projector to execute a trapezoidal correction action based on the transformation parameter. And controlling the projector to project a preset calibration pattern, acquiring an image of the calibration pattern by adopting the image acquisition device after the trapezoidal correction action is executed, extracting image features, and acquiring geometric parameters corresponding to the actual projection picture based on the image features. And determining a calibration error based on the difference between the geometric parameter and the transformation parameter, and generating a compensation parameter according to the calibration error. And writing the compensation parameter into the projector so as to compensate the transformation parameter when the projector performs trapezoidal correction. Through the steps, the projector generates a corresponding correction coefficient.

The invention discloses a topology reconstruction method and system for a filter coupling matrix, a terminal and a medium, and the method comprises the steps: obtaining a random coupling matrix and a random capacitance matrix of a target topology, and obtaining a transformation matrix according to the random coupling matrix and the random capacitance matrix; if the target topology contains frequency-dependent coupling, singular value decomposition and rotation matrix decomposition are carried out on the transformation matrix to obtain a product of a scaling matrix and a product of a rotation matrix, and a reconstruction path is obtained according to the product of the scaling matrix and the product of the rotation matrix; establishing an objective function, and performing gradient derivation according to the objective function and the reconstruction path to obtain an analytic gradient; and adjusting transformation parameters according to the analytic gradient and the target function to obtain a target topological coupling matrix. According to the invention, the calculation efficiency of the frequency-dependent coupling-containing target coupling matrix in the target topology can be improved.

The invention relates to the technical field of deep learning, in particular to a federal learning collaborative modeling method for energy efficiency optimization of a central air-conditioning system, which comprises the following steps: acquiring data of a system operation state, calculating to obtain real-time flow data, and calculating a real-time load value by combining the real-time flow data with water supply and return temperatures; inputting the environment data into the reference generation model to obtain a load mode; inputting system operation data into the energy efficiency model to generate a dynamic remodeling coefficient; through a parameter transformation network, taking the dynamic remodeling coefficient as a transformation parameter of the network, and generating a load prediction curve; based on the load prediction curve, solving is carried out through an optimal control algorithm, a cooperative control instruction is generated, and the operation frequency is adjusted through the control instruction; using the dynamic remodeling coefficient as a label, training an energy efficiency model and generating an update parameter; and the server aggregates the parameters to generate a global optimization model, then distributes the global optimization model, and iteratively updates the energy efficiency model. According to the method, system energy efficiency optimization is realized through federated learning collaborative modeling.

The invention provides a panoramic image splicing method and device and electronic equipment, and the method comprises the steps: obtaining a plurality of images and metadata thereof, carrying out the preprocessing of the plurality of images according to the metadata, and obtaining a plurality of preprocessed images; extracting image key points and feature descriptors of the image key points from the plurality of preprocessed images through a SuperPoint neural network model; performing approximate nearest neighbor search matching on the image key points according to the feature descriptors of the image key points to obtain a plurality of initial matching pairs; eliminating matching pairs with spatial consistency smaller than a threshold value from the plurality of initial matching pairs through a random sampling consistency algorithm to obtain final matching pairs; based on the final matching pair, performing global binding adjustment on camera transformation parameters corresponding to the plurality of images through a nonlinear optimization algorithm to obtain alignment parameters, and transforming the plurality of images according to the alignment parameters to obtain a plurality of transformed images; and performing multi-band fusion rendering on the plurality of transformed images to obtain a panoramic image.

The invention relates to the technical field of multi-mode registration, and discloses a long-distance space registration method and system for a radar photoelectric sensor, and the method comprises the steps: obtaining an initial conversion matrix between a radar coordinate system and a photoelectric sensor coordinate system; preprocessing a target image acquired by a photoelectric sensor in a preset long-distance range to generate photoelectric image feature information; the radar point cloud is mapped to a photoelectric image plane based on the initial conversion matrix, a visual consistency error is calculated based on photoelectric image feature information, and a digital elevation map is utilized to match elevation information of the radar point cloud to calculate an elevation consistency error; and constructing a joint optimization objective function containing dynamic weight distribution of the visual consistency error term and the elevation consistency error term, and solving optimized conversion parameters by minimizing the joint optimization objective function to realize long-distance spatial registration. The method does not need to depend on long-distance target deployment, the implementation cost and the operation difficulty are reduced, and the engineering practicability and the real-time performance are both considered.

The invention provides an image automatic exposure method and device, a terminal and a storage medium. The image automatic exposure method comprises the following steps: acquiring a first image, a second image and exposure parameters of the first image and the second image; respectively carrying out a preset number of Gamma transformations on the first image and the second image to obtain a first type of images and a second type of images; determining brightness information entropies of the first type of images and the second type of images with the same transformation parameter and a first transformation parameter corresponding to the maximum brightness information entropy; determining a first update exposure based on the exposure parameter and the first transformation parameter of the first image, and determining a second update exposure based on the exposure parameter and the first transformation parameter of the second image; a joint exposure amount is determined based on the first updated exposure amount, the second updated exposure amount, a first brightness mean value of the first image, and a second brightness mean value of the second image. The method can improve the feature richness of input images of algorithms such as feature matching and depth estimation, and further improves the quality of the algorithms.

The invention provides flexible device alignment detection equipment and a detection method thereof. The flexible device alignment detection equipment comprises a jig platform, a shaping mechanism, a main detection module and a control module. The bearing plane of the jig platform is provided with a positioning reference edge to establish a workpiece coordinate system; the main detection module obtains an unshaped first state reference image and a shaped second state reference image. The control module matches the two-state reference features under a workpiece coordinate system, obtains feature coordinates and solves initial plane rigid body transformation parameters; inversely mapping the second state feature coordinates to the first state to obtain a residual deformation quantity vector, and reconstructing a residual deformation field; and determining and normalizing an adaptive weight coefficient according to the residual modulus value, and solving by adopting weighted least squares to obtain a global alignment deviation of a shaping state relative to a workpiece coordinate system. The equipment can improve the robustness and stability of alignment calculation when in-plane deformation exists.

The invention belongs to the field of mechanical arm control, and relates to a method and device for controlling a mechanical arm by utilizing double-macro lens constraint, and the method comprises the following steps: carrying out system calibration on a double-macro endoscope and the tail end of the mechanical arm to obtain transformation parameters; acquiring an image of a target object in the working space to obtain an original image; performing preprocessing and target detection on the original image to obtain a preprocessed image containing target object features; performing attitude estimation on the preprocessed image to obtain pose information of the target object in a lens coordinate system; a hierarchical iterative approximation strategy of visual feedback is adopted, and a mechanical arm is controlled to drive a double-macro endoscope to gradually approach a target object; the pose information of the target object under the lens coordinate system is converted into a base coordinate system of the mechanical arm; based on the pose information of the target object under the base coordinate system, a mechanical arm joint rotation angle instruction is solved and generated, and a mechanical arm is controlled to complete grabbing operation on the target object; therefore, the fine operation requirement of the manipulator in a narrow working space is met.

The invention provides an animation production method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the steps that a skeleton hierarchy is constructed, the skeleton hierarchy comprises a main skeleton and at least one auxiliary skeleton, the main skeleton is a parent skeleton of the auxiliary skeletons, and global transformation parameters of the auxiliary skeletons in a world coordinate system are obtained; the skeleton can be formed by superposing global transformation parameters of the main skeleton in a world coordinate system and local transformation parameters of the auxiliary skeleton relative to the auxiliary skeleton in the world coordinate system; the preset 2D graphic element is bound to the auxiliary skeleton, and the target animation is generated according to the auxiliary skeleton and the preset basic animation, so that differential motion control based on the hierarchical skeleton is realized, the physical motion law of the real world is simulated, the motion freedom degree of the preset 2D graphic element in the target animation can be improved, and the user experience is improved. Diversity of motion trails is achieved, manufacturing efficiency can be improved, and manufacturing complexity can be reduced.