60 results about "Constraint algorithm" patented technology

The invention discloses a semi-supervised multi-view clustering method based on double-constraint concept decomposition, which relates to the technical field of machine learning, combines point constraint with pairwise constraint, constructs a double-constraint algorithm based on prior label information of a complementary supervision mechanism, and realizes maximum utilization and progressive propagation of limited supervision information. And based on a concept decomposition framework, breaking through the limitation of a traditional method on non-negativity of data, processing complex data distribution in combination with a kernel method, obtaining a consensus matrix of multi-view data fusion as low-dimensional representation of views, performing clustering by adopting a k-means algorithm, and outputting a final category division result. According to the method, effective processing of complex multi-view data is realized, finally, the performance of a multi-view clustering task is comprehensively improved, and the bottlenecks in clustering precision, data adaptability and supervision information utilization efficiency in the prior art are broken through.

The invention relates to the technical field of power grid digital checking, in particular to a checking method of a power grid digital design model, which comprises the following steps: S1, acquiring an initial parameter set of a to-be-checked power grid model; s2, performing data standardization processing on the initial parameter set; s3, constructing a three-dimensional topology verification model, and identifying abnormal nodes by using a spatial topology constraint algorithm; s4, mapping the real-time operation data to the three-dimensional topology verification model, and generating a dynamic parameter comparison matrix; s5, executing multi-dimensional check analysis, and generating a multi-dimensional difference report; s6, performing parameter replacement or topology reconstruction on the abnormal nodes, and outputting a final check result; according to the method, various power grid parameters are standardized in a unified manner, the accurate three-dimensional topology verification model is constructed, and automatic identification and quantitative correction of abnormal nodes are realized in combination with real-time data, so that the verification accuracy and operation reliability of the power grid model are remarkably improved.

The invention relates to the technical field of education and artificial intelligence, in particular to an intelligent teaching assistance system based on guided thinking, which comprises a heuristic guided teaching module which adopts a heuristic dialogue mechanism to replace direct answer provision and sets a constraint algorithm Constraint (T) = {1, if Count (Q in T) = 1 for'only one question is proposed each time '; the method comprises the following steps of: (1) calculating a formula (Q < in > T > = 0, if Count (Q < in > T > is not equal to 1), guiding students to autonomously think solutions, and automatically triggering knowledge summarization according to a completion signal detection algorithm CompletionScore = sigma (i = 1 to m) alphai * Match (keywordi, response) in answers of the students. According to the method, the understanding state of a student is automatically identified through a heuristic dialogue algorithm, personalized teaching content creation and dynamic teaching strategy adjustment are performed by utilizing the generation capability of a large model, and accurate course related knowledge retrieval is provided by combining a retrieval enhancement generation RAG technology, so that the limitation of directly giving answers to a traditional AI teaching system is broken through, and the teaching efficiency is improved. And real guided teaching is realized.

The invention provides a source load storage access optimization method and device based on user and power grid interaction external characteristics. The method comprises the steps of obtaining power grid user load data; extracting a plurality of multi-element load typical day scenes from the power grid user load data; carrying out weight calculation according to the user interaction external characteristics of each typical day scene; obtaining a user load portrait according to the user interaction external characteristics and the corresponding weights; constructing a source load storage double-layer collaborative planning model according to the user load portrait; and optimizing the source-load-storage double-layer collaborative planning model in combination with a normalized normal constraint algorithm, and generating a source-load-storage configuration scheme according to the optimized source-load-storage double-layer collaborative planning model.

The invention discloses a digital human-driven key point generation method and device and a medium, and the method comprises the steps: extracting a static spatial domain feature and a dynamic time domain feature of a standardized target voice through a constructed spatial domain-time domain double-flow feature decoupling model; according to a cross-modal dynamic attention fusion mechanism, performing dynamic weighted fusion on the static spatial domain features and the dynamic time domain features to obtain fusion features; according to a biomechanical constraint algorithm, trajectory operation is carried out on the fusion features, and a key point motion trajectory conforming to biomechanical constraints is generated; and through a joint loss function, calibrating the key point motion trail and the key point trail of the standardized target video, and generating a final face key point. According to the scheme, the synchronization precision of the digital human lip shape is improved by constructing a space-time domain double-flow feature decoupling architecture; the non-physical motion is eliminated through a biomechanical constraint algorithm, the track smoothness is improved, the phoneme and lip-shaped semantic association ability is enhanced through cross-modal fusion, and the generalization of roles is ensured.

The invention provides a park integrated energy optimization scheduling method and system, and relates to the technical field of energy scheduling, and the method comprises the steps: obtaining the state information of a park integrated energy system; establishing an energy scheduling model according to the state information; determining a constraint condition and a target function of the energy scheduling model; solving the energy scheduling model through an epsilon-constraint algorithm under the constraint of a constraint condition by taking the minimization of the objective function as a target, and generating a scheduling solution set; processing the scheduling solution set in a fuzzy decision mode to generate an optimal scheduling solution; and taking the optimal scheduling solution as a scheduling scheme, and carrying out optimal scheduling on the park integrated energy system. The energy utilization efficiency can be improved, and the energy safety and flexibility of the park are enhanced.

The invention discloses an elderly electroencephalogram time sequence diffusion staging method based on intergenerational transfer learning, and belongs to the crossing field of biomedical engineering and machine learning. In order to solve the problems of poor adaptability of weak features and strong noise of the elderly electroencephalogram, dependence on large-scale labeled data and the like, an elderly specificity time sequence diffusion model is constructed. In the preprocessing stage, an improved ICA algorithm is adopted to separate myoelectricity artifacts, and multi-band features are extracted; the diffusion model is fused into an elderly exclusive noise library and physiological priori, denoising is performed through a time sequence U-Net inverse process, and N3 period weak delta wave features are enhanced in combination with an attention mechanism; a common disease constraint algorithm is introduced to construct a common disease-sleep correlation tensor, and a loss function is optimized. The small sample enhancement module generates pseudo samples conforming to elderly features, and semi-supervised training reduces labeling dependence; and stage conversion advanced prediction is realized by combining stage reasoning with a BiLSTM classification head and dynamic confidence correction. According to the method, the electroencephalogram staging accuracy of the old people is improved by 18%-22%, the accuracy in a small sample scene reaches 82%, the dynamic conversion prediction error is smaller than or equal to 10 seconds, and the method is suitable for home sleep monitoring and clinical auxiliary diagnosis of the old people.

This invention discloses a method, system, and device for measuring the orientation of nonwoven fibers, belonging to the field of nonwoven fabrics. The method binarizes a color image of the nonwoven fabric to extract the fiber skeleton. Based on this, a T-shaped corner detection algorithm is used to determine the intersection points of the fiber skeleton. A fiber length constraint algorithm is used to limit the length of the skeleton segments, and a fiber projection algorithm is used to calculate the projected length of the skeleton segments, thereby determining the orientation of each length-constrained skeleton segment. The T-shaped corner detection algorithm used in this invention can accurately handle fiber skeleton intersection points, the fiber length constraint algorithm can effectively avoid interference from long fibers on orientation measurement, and the fiber projection algorithm can accurately calculate the projected length of the fibers. These three algorithms together make the measurement of nonwoven fiber orientation more accurate and efficient, and are applicable to various types of nonwoven materials.

The application relates to the field of equipment operation state monitoring, in particular to a medical equipment operation state monitoring and adjusting system based on big data, which comprises a multi-source acquisition module, a federal preprocessing module, an equipment multi-feature extraction module, an analysis module, an optimal adjustment strategy module and a database, the multi-source acquisition module acquires operation parameters and generates adversarial samples; the federal preprocessing module embeds a fairness constraint algorithm to output an initial balanced data set; the equipment multi-feature extraction module extracts frequency domain and space-time features and integrates the features into a time feature sequence; the analysis module enables a random greedy algorithm according to a dynamic decision, inputs a processed feature subset into a bidirectional LSTM model to output equipment states; and the optimal adjustment strategy module combines Bayesian network probability reasoning and deterministic rules of a rule engine to generate an optimal adjustment strategy, so that the data robustness and fairness are improved, accurate equipment state determination and optimal strategy generation are realized, and the safe operation of medical equipment is ensured.

This application discloses a method, apparatus, and storage medium for printing imposition positioning, which improves imposition efficiency and accuracy while reducing the operational difficulty of imposition. The method includes: obtaining first imposition layout parameters for a selected printing press model from a limited dataset based on the selected model and task requirements; establishing a two-dimensional reference coordinate system based on the first imposition layout parameters, with the vertex of the printing plate of the selected model as the starting point; calculating the first reference position of a single row of code values ​​in the two-dimensional reference coordinate system according to a dynamic constraint algorithm; mapping the first reference position to a multi-row positioning network based on a topology expansion matrix; verifying whether the multi-row positioning network conforms to preset layout rules; if so, performing adaptive error compensation on the multi-row positioning network and outputting the final positioning result.

The application provides a park comprehensive energy optimization scheduling method and system, and relates to the technical field of energy scheduling. The method comprises the following steps: obtaining state information of a park comprehensive energy system; establishing an energy scheduling model according to the state information; determining constraint conditions and an objective function of the energy scheduling model; under the constraint of the constraint conditions, solving the energy scheduling model by an epsilon-constraint algorithm to generate a scheduling solution set, processing the scheduling solution set by a fuzzy decision method to generate an optimal scheduling solution, and taking the optimal scheduling solution as a scheduling scheme to optimize the park comprehensive energy system. The application can improve energy utilization efficiency and enhance park energy security and flexibility.

The application discloses a semi-supervised multi-view clustering method based on double-constraint concept decomposition, relates to the technical field of machine learning, combines point constraints with pair constraints, constructs a double-constraint algorithm based on prior label information of a complementary supervision mechanism, realizes maximum utilization and progressive propagation of limited supervision information, breaks through the limitation of data non-negativity of a traditional method by relying on a concept decomposition framework, processes complex data distribution by combining a kernel method, obtains a consensus matrix of multi-view data fusion as low-dimensional representation of a view, adopts a k-means algorithm for clustering, and outputs a final class division result. The application realizes effective processing of complex multi-view data, finally comprehensively improves the performance of a multi-view clustering task, and breaks through the bottleneck of the prior art in clustering precision, data adaptability and supervision information utilization efficiency.

The invention discloses a general-inductance integrated waveform power distribution method for the Internet of Vehicles, and belongs to the technical field of Internet of Vehicles and general-inductance integration. Comprising the steps of constructing a communication and sensing integrated system architecture based on the Internet of Vehicles, designing a power distribution scheme based on a communication data rate index and a sensing Cramer-Rao bound, designing a constraint algorithm of multi-objective optimization, solving a communication and sensing performance boundary to obtain an optimal distribution strategy and the like. Aiming at the advantage that a delay-Doppler domain modulation technology has high robustness in a high-speed vehicle network scene, the invention provides an Internet of Vehicles communication and inductance integrated waveform power distribution method under limited power budget, and meanwhile, the communication downlink sum rate and speed sensing Cramer-Rao bound performance is improved; and an efficient and reliable sensing means is provided for intelligent traffic cooperative control.

The invention belongs to the technical field of shoreline identification, and provides a shoreline identification and optimization method based on a point cloud neural network in an intelligent ship. Point cloud data are collected through the laser radar and are not affected by illumination conditions. The problem that a visual algorithm is limited by weather and illumination is solved, and the method adapts to dynamic environments such as foggy days and tidal changes. The point cloud density is reduced through voxel grid filtering, the data processing amount is reduced, the Edge-Aware Point Net network is improved, characterization learning of dense features does not need to be carried out, the lightweight design of the network structure is achieved, the end-to-end processing delay time is shortened, the ship berthing real-time requirement is met, and the real-time bottleneck is overcome. Differentiation features of a natural shoreline and an artificial shoreline are adaptively processed through a double-path U-net full-convolution integral segment network, and the problem that a traditional method is poor in adaptability to different types of shorelines is solved; the normal vector consistency constraint is introduced into the dynamic constraint K-means algorithm, and the fitting precision of clustering to the complex shoreline form is improved.

The invention discloses a scientific investigation ship multi-support system integrated design method, and relates to the technical field of overall ships. The scientific investigation ship multi-support system integrated design method comprises two main parts, namely a spatial constraint algorithm suitable for scientific investigation ship support system integration and a hydraulic power source integrated design method. According to the scientific investigation ship multi-support system integrated design method, a multi-parameter spatial constraint algorithm is adopted in scientific investigation ship application for the first time, the efficiency and effectiveness of integrated design can be effectively improved, and it is ensured that multiple types of scientific investigation support systems are reasonably arranged and cooperatively run in a limited space; a hydraulic power source is integrally designed for a specific scientific investigation support system of a scientific research ship, pressure is flexibly supplied to subsystems according to working conditions, the guarantee requirements of various working conditions of a complex multi-support system can be effectively met, the system complexity is reduced, and the overall resource allocation of the scientific investigation ship is optimized.

The invention relates to the technical field of intelligent layout of building and mechatronics, and particularly provides an intelligent layout generation method and system for a public washroom based on a generative algorithm, and the method comprises the steps: obtaining multi-modal data of a public washroom space of which the layout is to be generated; analyzing the multi-modal data and constructing a multi-relation graph of the public washroom space; based on the constraint of the multi-relation graph, generating a Top-K set of nearly feasible layout first draft by adopting a submerged space diffusion model; taking the nearly feasible layout first draft as an initial state, constructing a Markov decision process of reinforcement learning, and performing strategy fine adjustment through a constraint PPO algorithm to obtain an optimized compliance layout scheme; and performing clause-level recheck and topology consistency check on the optimized compliance layout scheme. According to the method, a Top-K set of nearly feasible layout first draft is generated under a rule mask through a condition generation model, then a lightweight evaluator is used for driving an RL strategy to carry out multi-target iteration, and a clause-geometry-physics consistent automatic checking and repairing mechanism is formed.

The invention discloses a method, a device and a system for planning a transportation route of a large cargo. The method comprises the following steps: inputting and loading parameters of a transportation vehicle, cargoes and a transportation scene; performing path planning based on a multi-constraint algorithm to generate a feasible path; performing three-dimensional dynamic simulation on the feasible path; verifying whether the three-dimensional dynamic simulation is passed or not; if not, adjusting the parameters or manually modifying the path and then returning to the previous step; and if yes, outputting a path and a simulation report. According to the invention, comprehensive parameters of transport vehicles, goods and transport scenes are stored and updated in real time through the data support layer, so that planning deviation caused by data missing or lagging is avoided; by means of constraint matching analysis and multi-constraint path planning of the processing layer, feasible paths meeting the requirements of the height, the gradient, the turning radius, the bearing capacity and the like can be screened out, the scheme does not need to be repeatedly adjusted to adapt to actual transportation conditions, and time and labor cost are saved.

The invention discloses a precision adjustment method and device for a molecular dynamics constraint algorithm, a chip and a medium, and the method comprises the steps: enabling a GPU chip to determine the maximum tolerance values corresponding to all atoms in a protein molecule system under the current time step length of executing the molecular dynamics constraint algorithm on the protein molecule system; selecting a target precision kernel function according to a comparison result between the maximum tolerance value and a preset tolerance threshold value, executing molecular dynamics constraint algorithm operation on the protein molecule system according to the target precision kernel function, and then updating each atomic position; judging whether the molecular dynamics constraint algorithm is converged or not under the current time step length; and if not, updating the tolerance threshold value, and returning to execute the operation of determining the maximum tolerance values corresponding to all atoms in the protein molecule system until the algorithm converges. According to the technical scheme of the embodiment of the invention, the execution performance of the GPU chip on the molecular dynamics constraint algorithm can be improved.

The invention discloses a public building operation and maintenance management method based on multi-modal AI and deep reinforcement learning, and the method comprises the steps: collecting multi-source data, and carrying out the preprocessing and spatial time series data division; building three-dimensional space is used as a root node based on an octree algorithm, self-adaptive voxels are constructed based on information entropy of multi-source data except historical operation and maintenance data, and each self-adaptive voxel is used as a graph node to construct a building space topological graph; constructing a space-time heterogeneous graph convolution fusion network including a GCN network and an LSTM network, inputting the building space topological graph into the GCN network to obtain a spatial feature vector, and inputting the non-spatial time series data and the spatial feature vector into the LSTM network to obtain a building holographic state vector; and generating an optimal equipment control strategy based on the building holographic state vector through a multi-target spatial constraint DDPG algorithm. Intelligent and automatic operation and maintenance management is realized, the total energy consumption of the large public building is reduced, and the operation and maintenance efficiency and reliability of the building are remarkably improved.

The invention provides a multi-mode cooperative control method and system for an oral surgery robot, and belongs to the technical field of medical robots. The method comprises the steps that operation planning and space registration are completed, and a drill bit and an optical tracking ball are rigidly fixed to the tail end of a mechanical arm; a manual mode is started for coarse positioning, the mechanical arm is dragged, the optical navigator tracks the pose in real time and visualizes deviation, and the mechanical arm provides force assistance; after the drill bit reaches the preset range of the target area, the drill bit is switched to an automatic mode, and fine adjustment is completed through hand-eye calibration, probe calibration and closed-loop control; then switching to a cooperation mode, locking a preset direction dimension by the mechanical arm, controlling the implantation depth, and ensuring that a motion path is stable by the central processing unit through a dynamic constraint algorithm; and after the drill bit reaches the planned depth, the mechanical arm locks the depth direction, and the cooperation mode ends. According to the invention, the problems of insufficient precision and poor flexibility of the traditional technology are solved, and the positioning precision, the operation safety and the clinical execution efficiency of the oral surgery are improved.

The application discloses a kind of complex equipment system visual simulation method and system based on digital twinning, comprising: S1, obtains multi-source observation data and control data;S2, constructs digital twinning mechanism model, establishes state mapping relationship;S3, constraint perception hash addressing mechanism is introduced by Instant-NGP algorithm, and multi-resolution hash coding is executed;S4, state deviation is calculated;S5, input improved 4DGS model, introduce mechanism residual error gate projection mechanism, and execute gate projection;S6, based on SDS constraint algorithm, introduce feasible region constraint calculation of distillation gradient, and execute gradient update;S7, execute consistency determination, and output visual simulation result.The application realizes the high consistency, high stability and high real-time visual simulation output of complex equipment system under complex working condition, strong interference and multiple constraint conditions.

The invention discloses an execution method and device of a molecular dynamics constraint algorithm, a chip and a medium. The execution method comprises the steps that a GPU chip sequentially determines a molecule as a current molecule in a plurality of substance molecules to be processed; obtaining an atomic coordinate and an atomic velocity corresponding to the current molecule, and executing a molecular dynamics constraint algorithm operation on the current molecule according to the atomic coordinate, the atomic velocity and a plurality of predetermined target parameters; the multiple target parameters comprise atomic position correlation parameters and PV values; judging whether operation on all the molecules is completed or not; and if not, returning to execute the operation of sequentially determining one molecule as the current molecule in the plurality of to-be-processed substance molecules until the operation on all the molecules is completed. According to the technical scheme of the embodiment of the invention, the execution efficiency of the GPU chip on the molecular dynamics constraint algorithm can be improved.

The invention relates to an intelligent link sensing platform design and implementation method based on fragment retransmission, and belongs to the technical field of data transmission and communication. According to the method, the link state is acquired and perceived through the steps of fragment identifier implantation, fragment state feedback, multi-dimensional index calculation, index real-time reporting and the like; a link state is input, a double-constraint algorithm is used for calculating the size of an optimal fragment, and after whether a fragment redundancy strategy is adopted or not is decided, a transmission strategy is formed and issued; a transmission strategy is received and analyzed, a retransmission strategy is executed through data fragmentation processing and packet loss fragmentation detection, a redundancy supplementary retransmission mechanism is adopted, retransmission failure is avoided, and retransmission parameters are fed back; and carrying out iterative optimization on the strategy parameters by using a Q-Learning reinforcement learning algorithm, and carrying out strategy issuing through a gray level issuing strategy. According to the invention, the transmission efficiency can be improved, and stable and efficient data transmission service can be provided in a complex and changeable network environment.

The application discloses a kind of robust prestack seismic inversion methods and devices based on coherence constraint algorithm, belong to unconventional oil and gas exploration and development field, comprising: through the extraction of seismic coherence attribute of centrifugal window of space transformation;Based on the approximate formula of azimuthal anisotropy reflection coefficient, the forward operator of fracture reservoir is deduced and constructed, and the positive problem of prestack seismic inversion prediction is clear;Based on the prestack seismic inversion prediction positive problem, under the Bayesian framework, the coherence attribute is added to the objective function of inverse problem as the prior information of seismic inversion prediction, and stable prestack and poststack joint seismic inversion prediction is carried out.The application not only considers the influence of large-scale fracture in post-stack, but also considers the azimuthal anisotropy characteristics caused by small-scale fracture, which can greatly improve the stability of anisotropy parameter inversion of fracture reservoir, and has better predictability for small-scale fracture associated with large-scale fracture.

This invention discloses an integrated design method for multiple support systems on a research vessel, relating to the field of shipbuilding technology. This method comprises two main parts: a spatial constraint algorithm applicable to the integration of support systems on a research vessel and a hydraulic power source integration design method. This method, for the first time applied to a scientific research vessel, utilizes a multi-parameter spatial constraint algorithm, effectively improving the efficiency and effectiveness of the integrated design and ensuring the rational layout and coordinated operation of multiple research support systems within a limited space. Furthermore, the integrated design of the hydraulic power source, tailored to the specific research support systems of a scientific research vessel, allows for flexible pressure supply to each subsystem under different operating conditions, effectively addressing the support needs of various operating conditions in complex multi-support systems, reducing system complexity, and optimizing the overall resource allocation of the research vessel.

The invention discloses a medical report generation method based on anti-factual reasoning, which comprises the following steps of: inputting a given original medical organ image into a large language model after modal conversion mapping of a visual encoder and a connector, and predicting the output of a lexical element at the current moment to obtain original probability distribution; generating an attention distribution diagram according to the original probability distribution, dividing the attention distribution diagram into a key visual information area and an irrelevant visual information area, and applying Gaussian noise disturbance to the irrelevant visual information area to construct an anti-fact sample; obtaining anti-fact probability distribution by the same method; obtaining comparison probability distribution according to the difference between the original probability distribution and the anti-fact probability distribution, designing a self-adaptive rationality constraint algorithm, optimizing distribution, enhancing the confidence degree of the model to direct decision, designing an anti-fact sample construction strategy and an anti-fact comparison decoding strategy, improving the attention degree of the model to a key visual area, and improving the accuracy of the model. Attention on irrelevant areas is inhibited, and illusion of a medical report generation model can be effectively relieved.

The invention provides an oocyte quality classification method and device, computer equipment and a storage medium, and relates to the technical field of biology. The method comprises the following steps: acquiring an oocyte image and clinical information of a donor; processing the oocyte image through a residual neural network model to obtain a region of interest in the oocyte image; calculating key features in the oocyte image based on the region of interest and a mathematical constraint algorithm; extracting local features in the oocyte image based on a multi-scale parallel large convolution kernel module and an enhanced parallel attention mechanism module; the clinical information is coded based on a one-hot coding network; based on a multi-scale attention mechanism module, mapping the key features, the local features and the clinical information to the same feature domain to obtain fusion features; and classifying the fusion features of the same feature domain to realize oocyte quality classification.

This invention discloses a fault-constrained Dal-UNet stratigraphic surface reconstruction method, applicable to the fields of information processing and oil and gas exploration. Existing deep learning reconstruction algorithms, based on supervised learning, require extensive data and manually labeled training data for pre-training, resulting in high economic and time costs. Data quality directly impacts algorithm performance, and in geological studies with relatively scarce data, supervised deep learning algorithms are prone to overfitting, making them even more difficult to apply directly to stratigraphic surface reconstruction tasks involving complex geological structures with faults. This invention utilizes the Dal-UNet network to capture hidden features from limited and sparse stratigraphic surface data and combines it with a fault-constrained algorithm to reconstruct complex stratigraphic surfaces containing faults, effectively restoring the true topography.

The invention relates to the technical field of document processing, and discloses a document content restoration method and system based on artificial intelligence, and the method comprises the steps: collecting document image information as an input source through a camera, and then carrying out the page scanning of the preprocessed document image information through a multi-modal layout analysis model, positioning various layout elements, obtaining physical layout parameters, deducing a geometric constraint relation through a geometric constraint algorithm, and generating a structured layout model; carrying out multi-language character recognition, picture graph classification and metadata extraction, formula format conversion and table structure detection and reconstruction, and outputting corresponding structured data; distinguishing different semantic blocks and constructing a tree hierarchical structure by combining a context semantic encoder with a conditional random field and text features, and extracting and restoring text fonts and paragraph format styles; and reading the basic metadata of the document, and restoring and complementing the associated information of the document elements based on the large model to complete the information improvement work. And efficient and accurate restoration of the document content is realized.

The invention relates to the technical field of three-dimensional data processing, in particular to an assembly segmentation and three-dimensional reconstruction method and electronic equipment, and the method comprises the following steps: S1, a preprocessing module; s2, a registration module; s3, segmenting the module; s4, a post-processing module; s5, reconstructing a module; according to the method, firstly, high-quality point clouds are acquired and preprocessed through a dynamic structured light camera; stable and accurate alignment of the scanning data and the model is realized by adopting a fine registration strategy of coarse registration and a normal vector constraint ICP algorithm; part-level segmentation is carried out based on a part generation extension envelope box, and a high-precision part point cloud consistent with an original model in shape is obtained by propelling front surface reconstruction; according to the method, a complete technical link from scanning to a part-level digital model is constructed, reflective interference and registration errors are effectively overcome, a reliable high-precision data basis is provided for geometric modeling and simulation of complex products, and the method has remarkable technical value and application prospects.