1969 results about "Spatial relationship" patented technology

The invention discloses a PCBA circuit board welding spot detection method based on multi-modal data fusion, and relates to the technical field of electronic manufacturing quality detection.The PCBA circuit board welding spot detection method comprises the steps that a distributed sensing network is constructed, multi-modal data are collected, welding spot information is obtained in an omnibearing mode, and time-space alignment of the multi-modal data is carried out; performing feature extraction on the multi-modal data, dynamically weighting each modal feature through an attention mechanism, and highlighting key defect characterization; a welding spot spatial topological graph is constructed by using a graph neural network, and a spatial relationship between welding spots is modeled. By integrating optical, X-Ray, thermal, mechanics, electricity and other multi-dimensional data, the information limitation of single-mode detection is broken through, the complementation of different mode data is utilized, the attention mechanism is combined to dynamically weight each mode feature, the complex defect is accurately identified, the graph neural network is utilized to model the welding spot space topological relation, the associated defect is further captured, and the defect detection accuracy is improved. And the defect classification accuracy is improved.

The invention discloses a territorial space layout adjustment decision method based on planning conflict identification, and belongs to the technical field of territorial space planning and management. Comprising the following steps: constructing a space-time conflict feature vector library containing multi-dimensional attributes; generating a semantic knowledge graph containing a causal relationship, a sequential relationship, a spatial relationship and an affiliation relationship; accurate prediction of influence intensity and duration of the conflict state along with time lapse is realized; trend analysis and risk level evaluation of a cross-scale conflict propagation path are realized; automatically identifying dominant conflicts in the current layout and potential conflicts based on propagation prediction, and intelligently classifying composite conflicts by using fuzzy clustering; a staged differentiation conflict intervention strategy recommendation scheme is automatically generated; solving a territorial space layout adjustment scheme; judging whether the layout adjustment scheme meets a preset conflict relieving standard or not;

The invention discloses an artificial board surface defect intelligent detection method and system based on machine vision, and particularly relates to the technical field of artificial board surface defect detection. An artificial board surface image is collected, image preprocessing, feature extraction, defect segmentation and classification recognition are carried out through a deep learning algorithm, a multi-scale convolutional neural network is adopted to carry out feature extraction on the image, a shallow convolutional layer captures small defect details, a deep convolutional layer recognizes global features of large defects, and a multi-scale convolutional neural network is adopted to carry out feature extraction on the image. Accurate defect segmentation is carried out through a Mask R-CNN model, a redundant frame is removed in combination with a non-maximum suppression algorithm, the classification problem of adjacent defects is corrected by using an error correction algorithm in combination with the spatial relationship and morphological characteristics of the defects, and defect information is fed back to a production line control system in real time; and defective products are automatically removed or production process parameters are automatically adjusted, so that the automation level of a production line is effectively improved, the product quality is optimized, and human intervention and production cost are reduced.

The invention provides a building equipment operation data intelligent analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: extracting historical positioning data in a space calibration interval, and calculating a position offset vector set between a predicted trajectory point and an actual trajectory point according to the trajectory point on a predicted motion trajectory; taking the weighted sum of squares of minimizing the position offset vector as an optimization target to obtain a three-dimensional space transformation parameter optimization model; according to the optimization model, a quasi-Newton optimization algorithm is adopted to solve the optimization model, and a final calibration parameter set is generated; and based on the final calibration parameter set, correcting the predicted motion trajectory, performing spatial relationship analysis on the corrected trajectory and a spatial risk field, calculating a collision risk probability and an accident severity level, generating a comprehensive risk score, and triggering a response action according to a preset threshold value. The construction safety level and the management efficiency are improved.

The invention relates to the technical field of anomaly detection, in particular to an intelligent gas hidden danger detection system which comprises a leakage monitoring module, a time difference extraction module, a direction positioning module, a color temperature analysis module and a risk aggregation module. According to the method, the time and space relation of gas concentration response nodes is extracted, a leakage diffusion path is constructed, and a dominant propagation direction is identified, so that a leakage trend region has a clear positioning basis, regional brightness and hue changes in an image frame are combined, the time sequence stability proportion is counted, and a cross-modal trend verification channel is established; the defect of a single sensing dimension is effectively made up, in the image and trend direction matching process, abnormal area numbers are recognized through stable proportion difference value changes, intersection confirmation is carried out on trend areas, the accuracy and spatial directivity of hidden danger marking are remarkably improved, the risk recognition chain is more complete through cooperative calculation of multi-source data, and the accuracy of hidden danger marking is improved. And the method has higher linkage response capability and dynamic judgment depth.

The invention relates to a gated multi-graph convolution perception modeling method for traffic flow prediction. The method integrates multi-graph structure construction, gating graph convolution and time feature extraction, and aims to solve the problems of strong time fluctuation and heterogeneous spatial relationship in traffic data. The method comprises the following steps of: firstly, respectively constructing a geographic map and a semantic map according to the maximum mutual information measurement between the spatial distribution information of a sensor and historical traffic data; and then, designing a dual-adaptive gating graph convolution module, and dynamically adjusting an information propagation path of a multi-graph structure by introducing an attention mechanism and a gating factor, thereby improving the modeling performance of the model on spatial isomerism dependence. On the time dimension, a time sequence interactive sensing module is constructed in combination with multi-scale causal convolution and an attention mechanism, time dependence characteristics of a short period and a long period are captured, and fusion and expression of time characteristics are completed. According to the method, the modeling precision and stability of the traffic prediction model in a complex traffic scene can be effectively enhanced, and the method has relatively high practical application value.

The invention relates to the technical field of power detection, and discloses a vegetation risk hidden danger detection method and system based on sparse point cloud segmentation, and the method comprises the steps: collecting image data of a monitoring region at different angles, generating sparse point cloud data, and carrying out the preprocessing of the sparse point cloud data; and performing point cloud segmentation on the sparse point cloud data through a neural network, dividing the monitoring region according to region types, analyzing a spatial relationship between vegetation and power equipment in a target region, performing risk assessment, and generating an early warning signal in combination with multi-modal data. According to the method, the sparse point cloud data is precisely segmented, the three-dimensional space characteristics of the tree are extracted, and the potential risk of the tree and the power transmission facility can be dynamically monitored in real time by precisely calculating the space relation between the tree and the power transmission facility; according to the multi-modal fusion data, multi-level early warning information is generated, so that the efficiency and precision of power transmission line inspection are greatly improved, and the pre-judgment and timely treatment of hidden dangers are realized.

The invention relates to the technical field of vision, in particular to an object monitoring method based on a multi-camera joint calibration technology and a monitoring camera thereof. The method comprises the following steps: driving all cameras to synchronously shoot a calibration reference object with known geometric characteristics, and solving internal and external parameters of each camera and an accurate space pose relationship between the internal and external parameters by utilizing shot images to form a joint space relationship chain; controlling the camera array to synchronously shoot a target object, and correspondingly converting the two-dimensional image points acquired by the cameras into spatial point coordinates in a unified three-dimensional coordinate system by using the joint spatial relation chain to generate three-dimensional point cloud data of the target object; and processing the generated three-dimensional point cloud data, extracting key geometric features of the target object in real time, performing comparative analysis on the extracted features and a preset standard or a historical state, and outputting a state monitoring result of the target object. According to the invention, the influence of temperature drift and mechanical vibration on the measurement precision is effectively suppressed, and the stability of long-term monitoring of an industrial field is guaranteed.

The invention discloses a road slope modeling method based on an unmanned aerial vehicle inspection route, and relates to the technical field of unmanned aerial vehicle inspection. The method comprises the following steps: measuring the road slope length, the landform and the geological complexity, and selecting an adaptive unmanned aerial vehicle type and sensor combination according to the road slope length, the landform and the geological complexity; planning unmanned aerial vehicle surface inspection and shallow geological exploration routes, ensuring space matching, and synchronously obtaining high-resolution images and ground penetrating radar data; and processing image data through a photogrammetry technology, generating a digital elevation model and an orthophoto map, and combining to construct a three-dimensional model of the earth surface. Geophysical inversion is carried out on the ground penetrating radar data, reflection features are extracted and projected to the ground surface three-dimensional model, and space correlation between the ground surface and a shallow layer structure is established; the reflection features are digitalized and geometrically reconstructed into three-dimensional surface elements, the three-dimensional surface elements are embedded into an earth surface model to form a three-dimensional geologic body model, time dimensions are introduced, and a dynamic evolution model is constructed. According to the dynamic evolution model, the dynamic analysis of the slope stability is realized.

The invention relates to the technical field of pneumoconiosis image detection, and discloses a pneumoconiosis-oriented chest X-ray image processing method based on an AI small shadow detection rate. According to the method, a basic mask is generated through an initial segmentation network, and then an accurate lung contour is obtained through correction of a hierarchical optimization unit. The multi-scale feature extraction module analyzes gray features in the contour and constructs an initial small shadow probability distribution map. A morphological structure analyzer identifies discrete shadow regions and marks suspicious shadow clusters, and a dynamic confidence feedback mechanism evaluates its credibility to generate an optimized feature map. A shadow cluster topological incidence matrix is established by the spatial relation modeling network and matched with a pneumoconiosis pathological feature library to screen a target shadow area. The three-dimensional reconstruction engine generates a small shadow volume density thermodynamic diagram, the hierarchical fusion module integrates the thermodynamic diagram and original image space coordinates, an enhanced distribution map is output, and finally structured diagnosis report data is generated.

The invention relates to the technical field of navigation planning, and discloses an unmanned aerial vehicle automatic inspection nest path planning method and system, and the method comprises the steps: carrying out the multi-source data fusion of a target inspection region, and obtaining space measurement environment data; analyzing an airspace limiting condition in the spatial measurement environment data, and determining a navigation path measurement constraint condition in combination with an inspection task demand of the target inspection area; performing gridding measurement on the target inspection area to identify candidate waypoints; according to the spatial position information, analyzing a spatial relationship of the candidate waypoints to construct an initial navigation network; performing multi-constraint evaluation on the initial navigation network to obtain constraint weights corresponding to the candidate waypoints, and integrating the candidate waypoints with the constraint weights higher than a preset weight threshold into a global optimal waypoint set; carrying out passable connection on adjacent waypoints in the global optimal waypoint set to obtain a navigation path; according to the invention, the efficiency of automatic inspection nest path planning of the unmanned aerial vehicle can be improved.

The described aspects and implementations enable online lane estimation and tracking in autonomous vehicle and driver assistance applications. In one implementation, disclosed is a method and a system to perform the method that includes receiving perception data indicative of driving lanes and reference objects in a driving environment of a vehicle, identifying target destinations corresponding to the driving lanes, determining candidate driving paths connecting a starting location with one of the target destinations, selecting a target driving path from the candidate driving paths based on geometric properties of each candidate driving path and a spatial relationship of a respective candidate driving path to the reference objects, and causing a motion of the vehicle to be modified based on the target driving path.

The invention relates to the technical field of surface defect recognition, and discloses an intelligent recognition method and system for surface defects of an autoclaved aerated concrete member. The method comprises the following steps: carrying out omnibearing shooting and spatial transformation matrix mapping on the surface of the autoclaved aerated concrete member to obtain a preprocessed image; performing texture feature extraction on the preprocessed image to obtain texture feature data; performing spatial feature analysis on the preprocessed image and the texture feature data to obtain spatial relation feature data; performing feature importance weighting and bidirectional cross fusion processing on the texture feature data and the spatial relation feature data to obtain comprehensive defect feature representation; and performing surface defect analysis on the autoclaved aerated concrete member based on the comprehensive defect feature representation, and outputting a surface quality comprehensive score and grade division result. According to the method, the problems of dead angles and image deformation existing in traditional single-view-angle detection are solved, and the completeness and accuracy of defect detection are ensured.

The invention relates to the technical field of building risk monitoring, in particular to an urban building safety evaluation method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a synthetic aperture radar image, an optical remote sensing image, a hyperspectral image, topographic data and building contour data of a to-be-evaluated region; based on the optical remote sensing image and the building contour data, identifying spatial form change information of each target building, and generating change parameters used for describing structure adjustment features; generating an interferogram based on the synthetic aperture radar image, extracting a corresponding deformation monitoring point, and generating a settlement parameter reflecting the displacement change of the building; based on the hyperspectral image and the topographic data, identifying spatial relationship information of the target building and the surrounding environment risk body, and generating external influence parameters; and based on the change parameter, the settlement parameter, the external influence parameter and the preset evaluation model, generating a safety evaluation result of each target building, thereby improving the efficiency of urban building safety evaluation.

The invention belongs to the technical field of three-dimensional scene modeling, and discloses a dual-branch diffusion three-dimensional scene generation method based on a multi-modal semantic graph, which comprises the following steps of: firstly, receiving multi-modal data such as sketches, texts, automatic completion instructions and scene general knowledge, extracting features and fusing the features into a unified multi-modal semantic graph; utilizing a graph neural network and an attention mechanism to enhance semantic graph features, and optimizing physical constraints through a physical engine; complementing the missing visual modality and graph structure relationship; performing quality scoring on the scene based on semantics, spatial relationships and physical constraints; and finally, respectively generating a spatial layout and a geometric shape through a double-branch diffusion model, and ensuring the coordination of the layout and the shape. The method has the advantages of multi-modal information fusion, physical rationality guarantee, high structure complementation capability, high-quality score optimization and efficient generation process, and is suitable for three-dimensional scene modeling requirements in the fields of virtual reality, augmented reality, robots and the like.

The invention discloses a video monitoring method and system, electronic equipment and a storage medium, and relates to the field of video monitoring. According to the method, a three-dimensional digital twinborn scene is constructed according to the space coordinates of each camera in a target area, and a node relation matrix is constructed according to the space relation between the cameras; according to the three-dimensional digital twinborn scene and the node relation matrix, a dynamic vision field splicing algorithm is adopted, the real-time position and the action path of the target object are determined, and the people flow density of the target area is displayed; determining a behavior mode of the target object through skeleton key point detection and interaction behavior analysis, and predicting an action track of the target object according to the behavior mode; and evaluating an initial risk level of the action track, adjusting the initial risk level in combination with the people flow density to obtain a final risk level, and generating a disposal suggestion according to the final risk level. By implementing the technical scheme provided by the invention, comprehensive and accurate video monitoring and risk management of the target area are realized.

The invention discloses a network security malicious traffic tracing method based on a generative adversarial network, which comprises the following steps: collecting network traffic data, selecting a target traffic sample, extracting a protocol behavior, a communication structure and a time evolution characteristic of the target traffic sample, and carrying out semantic coding; mapping the multiple types of features to a unified embedding space through a multi-view fusion mode; constructing a manifold set based on known malicious samples, and generating an intermediate state sample sequence; constructing an attack context and path disturbance information in combination with the target sample, and generating a guide vector; inputting the guide vector and the random variable into a generator, and outputting a potential malicious sample representation; identifying the authenticity of the sample and evaluating the path similarity by using a discrimination network; and finally, constructing an attack path graph according to the similarity and the spatial relationship, and calculating a shortest path sequence to a known malicious sample to realize traceability identification of potential malicious behaviors. According to the invention, tracking analysis of malicious traffic sources is realized by using the generative adversarial network.

The invention belongs to the technical field of artificial intelligence image generation, and particularly relates to an image generation method and system based on style feature injection, and the method comprises the steps: carrying out the multi-dimensional feature analysis of a reference image specified by a user side through a pre-trained multi-level style extraction model, extracting a style feature vector, and carrying out the feature extraction of the style feature vector; the style feature vector comprises a color feature vector, a texture feature vector, a composition feature vector and an illumination feature vector; obtaining a text description input by a user side, and outputting triple information including a scene entity, an entity attribute and a spatial relationship; performing weighted fusion on the triple information and the style feature vector to obtain a style enhanced semantic embedding vector; and inputting the style-enhanced semantic embedding vector into a generative adversarial network for splicing, and outputting an image. The method has the effect of remarkably improving the similarity between the generated image and the reference style.

An artificial intelligence system based on spatial-temporal information pairs is provided by the present disclosure. By integrally deploying paired vision, auditory and olfactory acquisition devices, a device that can collect the data within a 720-degree area is constructed, and the multi-dimensional continuous spatial-temporal information pairs such as positions, morphologies, motion states, sounds and odors from the ambient environment of the acquisition device or a same spatial object in the environment are recorded in real time. These information pairs can not only contain spatial relationships and a clock attribute, but also contain rich label attributes, for example, identifiers of acquisition devices, and a name, a category and a behavior pattern of the spatial object.

The invention relates to the technical field of defect detection, in particular to a heat-conducting adhesive tape defect detection method and system based on machine vision, and the method comprises the following steps: obtaining a surface image of a heat-conducting adhesive tape, extracting a texture trend and marking an aggregation block, adjusting the trend and offset of a connecting line segment, and analyzing the angle change of a sideband node. And recognizing a skeleton trend reverse section and closing a boundary, and extracting a continuous direction changing region to obtain a defect structure image annotation layer. According to the method, the turning position in the sideband region is extracted by constructing the direction aggregation graph of the texture aggregation block, enhancing the direction identification of the direction of the regional texture, combining the grouping organization and coordinate offset processing of the line segment path, enhancing the continuous association of the image structure, and by means of the repeated distribution characteristic of the angle mutation node; and through a skeleton trend reverse paragraph and boundary closing mode, an identification range of a structure disturbance block is expanded, and coherent tracking and multi-level labeling of a structure abnormal region are completed by combining a spatial relationship between a contour trend difference and a turning point.

The invention discloses a long-term traffic flow prediction method and system based on a multi-scale spatial-temporal model, and the method comprises the steps: obtaining and preprocessing the traffic flow real-time data of multiple road sections, and obtaining the preprocessing data; constructing a model containing multi-scale convolutional network feature extraction and space-time modeling capability; extracting local features through the model; performing node importance coding and time sequence coding on the local features, and optimizing an attention mechanism based on a node space relationship; linear projection is carried out on the coded local features, high-order features are extracted, and model parameters are optimized based on the high-order features; and realizing feature information interaction through an attention mechanism of the optimized model, so as to obtain a future long-term traffic flow prediction result based on high-order feature mapping. The model is combined with structure coding and a dynamic attention mechanism, so that the traffic flow prediction precision, the training efficiency and the spatial topology perception capability are remarkably improved, and more accurate traffic data prediction support is realized.

The invention belongs to the technical field of photovoltaic power prediction, and particularly relates to a distributed photovoltaic power prediction method, which comprises the following steps of: acquiring multi-source information of a plurality of photovoltaic power stations; constructing a gridding geographic space model based on the collected geographic position information; mapping the geographic position information of each photovoltaic power station to a corresponding space grid unit; for each space grid unit, fusing multi-source information of all photovoltaic stations in the grid, and constructing grid-level multi-source features including meteorological features, power features and geographic position features; and the grid-level multi-source features are inputted into a space-time Transform model which is already trained, and the space-time Transform model outputs a photovoltaic power prediction value. According to the method, the photovoltaic power prediction value of the target at the future moment is obtained through joint modeling of the time sequence dependence and the spatial relationship, so that the prediction precision and stability of the future power change trend of the distributed photovoltaic power station are improved.

The invention provides an automatic content typesetting method based on AI recognition, and relates to the technical field of artificial intelligence and digital content process.The method comprises the steps that text data and image data are received, and the overall category, the element type and the spatial position in an image are recognized through a computer vision model; meanwhile, the unit type, importance and semantic association of the text are analyzed through a natural language processing model, a content relation graph fusing semantics and the spatial relation is constructed, constraint solving is conducted through a dynamic layout optimization engine on the basis of the content relation graph in combination with semantic association constraint, environment constraint and aesthetic index constraint, and the content relation graph is obtained. And an automatic typesetting scheme considering content relevance and visual aesthetic feeling is generated. According to the method, the problems of content relevance splitting and aesthetic defects in traditional typesetting are solved, and intelligent collaborative layout of multi-modal content is realized.

The invention provides a smart city space information management method and system based on live-action three-dimensional, and relates to the technical field of three-dimensional management, and the method comprises the steps: obtaining multi-source heterogeneous space data, and fusing the data to construct a high-precision live-action three-dimensional model; carrying out space element identification and establishing a dynamic space-time relation network in combination with a knowledge graph; intelligent reasoning and prediction are realized based on a graph neural network; performing space division by adopting an octree algorithm to construct a database; and establishing an influence evaluation model and providing an intelligent space information service. The spatial data fusion efficiency is improved, the spatial relationship analysis capability is enhanced, and the intelligent level of urban management is improved.

The invention relates to the field of data traceability, and particularly discloses a natural resource data dynamic monitoring traceability method and system, and the method comprises the steps: building a multi-tense database, automatically writing real-time data into a corresponding partition according to a timestamp, and carrying out the incremental updating of a workflow; data confidence coefficient weights are introduced into SQL screening statements according to attribute tracing logic, and data legality is judged through weighted logic; constructing a land class evolution state transition matrix, determining a legal change path and an illegal path, and carrying out path deduction on attribute data missing to judge whether the attribute data missing is illegal change or not; and detecting land type mutation according to NDVI index change of a target remote sensing image, forming a three-dimensional tracing database of attribute data, space evidence and business files according to the attribute data and the spatial relationship, and dynamically monitoring and tracing natural resource data. The problems that the legal source of the construction land cannot be accurately traced due to land judgment errors and real-time data analysis and traceability are difficult in the prior art are solved.

The invention belongs to the technical field of nondestructive testing, and discloses a vector coherence factor-based thick-wall austenitic stainless steel welding seam full-focus imaging method, which comprises the following steps of: acquiring full-matrix data through a TRL area array probe, constructing an imaging area, calculating a spatial relationship, optimizing a propagation path in combination with a Fermat principle, and obtaining a full-focus imaging result; the method comprises the following steps of: firstly, extracting an instantaneous phase of an ultrasonic signal to construct a vector coherence factor, reconstructing an imaging data matrix according to the coherence factor, and obtaining a phase coherence weighted full-focusing imaging result, so as to ensure that each pixel point can obtain an optimal propagation path, thereby generating a full-focusing image data matrix by adopting a point-by-point delay superposition technology; the method can effectively improve the signal-to-noise ratio and positioning and quantification precision of the thick-wall austenitic stainless steel weld joint detection image, and has good engineering application value.

The present invention relates to the technical field of algae proliferation risk prediction. Disclosed is a machine learning model-based algae proliferation risk prediction method. The present invention comprises collecting water quality data, geographic information and historical algae proliferation event data and preprocessing the collected data. In the present invention, a graph structure between monitoring points is constructed on the basis of spatial features, each monitoring point is represented as a node in a graph, and a relationship between nodes is represented as an edge, so that spatial dependency between monitoring points can be effectively captured. The weight of each edge is defined as the reciprocal of the distance between the nodes, so that the model can more accurately reflect the effect of adjacent monitoring points on each other. By constructing the graph structure to represent the relationship between the monitoring points, large-scale spatial data can be efficiently processed. A graph neural network is capable of performing a convolution operation on a graph structure, effectively aggregating information of adjacent nodes, thereby improving the spatial relationship expression capability and the model prediction accuracy.

A computing system is disclosed for generating and optimizing schema-conformant object models. The system obtains input data from multiple sources in structured, semi-structured, or unstructured formats and processes the data using parsing logic to derive a structured rule set defining spatial relationships and constraint conditions. The system generates schema-conformant object representations comprising parametric assemblies, applies the constraint conditions using machine-executed resolution logic, and produces candidate object models through iterative modification of spatial components, dimensional parameters, and adjacency relationships. Performance values are computed for the candidate models using quantitative evaluation of spatial efficiency, material usage, and constraint compliance. An optimized object model is selected based on the computed performance values and may be used to generate construction project plans including technical drawings, material specifications, and scheduling instructions.

The invention relates to the technical field of satellite remote sensing application, and discloses a crop classification method based on a multi-source satellite image, and the method comprises the steps: firstly obtaining multi-temporal satellite image data, extracting spectral reflectivity characteristics, generating a matrix, and constructing a classification model set; setting a feature parameter category set, and establishing a feature fusion correlation model based on the obtained texture, vegetation index parameters and spatial resolution data of the historical image; extracting spectrum and texture feature parameters of a crop area in the current image, and generating an optimized feature set in combination with model optimization; updating classification logic based on the optimized feature set and the classification model, generating a target classification scheme and calibrating a spatial relationship; and finally, acquiring crop growth cycle data, and establishing a time sequence feature association rule to optimize a crop type spatial distribution map. According to the method, through multi-source feature fusion and dynamic optimization, the crop classification precision and reliability are improved, and the method is suitable for a precision agricultural management scene.

The invention discloses a light beam vector calibration method for an on-machine measurement system of a spectral confocal sensor. The method comprises the following steps: establishing a coordinate system of the on-machine measurement system; calibrating the pose of the sensor; after calibration, a sensor is used for conducting standard ball surface scanning at a plurality of positions, and the zero point coordinate of the sensor at each position and the corresponding distance value measured by the sensor are recorded; establishing a calibration model and a nonlinear equation set according to the coordinate space relationship of the measurement points, and constructing a complete nonlinear optimization objective function to calibrate a light beam vector; and solving the target function by using a hybrid optimization algorithm, and finally obtaining an accurate sensor beam vector and a standard ball center coordinate. According to the light beam vector calibration method, the geometric model with the direction consistency constraint is introduced, the genetic algorithm and the L-M algorithm are used in a combined mode, the stability, robustness and calculation precision of the calibration method are improved, and the method is suitable for sensor pose calibration scenes in various on-machine measurement systems.