155 results about "Spatial mapping" patented technology

This invention relates to the field of precision mechanical control technology, specifically to a method based on a magnetoelectric converter and linearity control. The method includes: acquiring raw magnetic induction data through a magnetoelectric converter; obtaining a three-dimensional magnetic field distribution through electromagnetic field spatial mapping and extracting the axial gradient vector field; constructing a virtual magnetic streamline model based on this to identify local disturbance regions where magnetic field distortion exceeds a threshold; performing multi-scale time-frequency analysis on the data in this region to separate high-frequency fluctuations caused by mechanical vibration and low-frequency drift components caused by thermal deformation; inputting the two components into a cross-coupled compensation decision network to generate high-frequency suppression commands and low-frequency compensation trajectories, which are then merged to form a comprehensive drive signal for compensation. This method can accurately locate the source of linearity disturbance and perform targeted decoupling control of errors from different physical sources, improving control accuracy and stability under complex operating conditions.

The application relates to the technical field of space optical communication, and discloses a MIMO optical transmission system for a cluster unmanned aerial vehicle and a method thereof, which comprises a flight control computer, a transmitting array, a receiving array and a baseband processing unit. The baseband processing unit acquires the three-dimensional position and attitude matrix of a transceiving node, carries out coordinate system transformation, screens out candidate transmitting arrays satisfying geometric alignment conditions, reconstructs an aerodynamic wake turbulence field in combination with a motor rotating speed parameter, carries out integral of refractive index disturbance along a path to eliminate arrays blocked by air flow, drives a flight control loop to perform attitude biasing when the effective array is limited, carries out channel optimization, then splits data into spatial mapping bits and light intensity modulation bits, selects a specific array to send optical pulses, and the receiving end uses a lens to carry out spatial isolation and focusing on incident light beams, and demodulates and restores data in reverse according to the photoelectric current characteristics. The application realizes feedforward avoidance of complex air flow interference and dynamic optimization of limited channels, reduces line transmission crosstalk, and guarantees communication quality.

The present application relates to the field of smart city environment management, and more particularly to a carbon emission information data analysis method and system based on CIM, which overcomes the drawback of ambiguous spatial location of emission data in traditional methods by implementing precise coding matching and binding of CIM spatial units and carbon emission data. The above spatial characteristics and real-time dynamic data are input into a strength analysis model based on machine learning. Personalized intelligent evaluation is achieved. After identifying high-emission units, the method can use the established precise spatial mapping for reverse intelligent tracing, quickly locating and quantifying specific activities and sources causing high emissions, and achieving precise diagnosis. Based on the clear diagnosis conclusion, further combined with the knowledge base and optimization algorithm, customized and quantifiable emission reduction strategies are automatically generated. Finally, differentiated results are pushed to different roles.

This invention relates to the field of defect detection technology, specifically to a high-voltage cable defect location system and method based on a two-dimensional ultrasonic array. The system includes a signal main peak acquisition module, a lateral deviation calculation module, an excitation timing optimization module, a main peak spatial focusing module, and a focusing area control module. This invention utilizes a multi-element spatial collaborative acquisition method of a two-dimensional ultrasonic array. It constructs a signal spatial mapping relationship through the main peak timing characteristics, combines path difference parameters to achieve fine analysis of the propagation characteristics of multi-source signals, optimizes the excitation timing and array spatial response structure by adjusting the excitation sequence, periodically determines spatial convergence indices, identifies the main peak focusing state of the defect area, and dynamically adjusts the element weight distribution to improve the signal response capability of specific spatial areas. The spatial location of the defect area is thus clearly distinguished, effectively improving the resolution and accuracy of multi-point defect location in complex cable structures.

This invention discloses an uncertainty-based method for three-dimensional reconstruction and intelligent sampling of underground space, comprising the following steps: Step 1: Constructing a multi-source heterogeneous data acquisition system and performing data preprocessing and spatiotemporal registration; Step 2: Extracting multimodal heterogeneous features and unifying spatial mapping based on deep learning; Step 3: Constructing a high-fidelity three-dimensional reconstruction volumetric model of underground space based on adaptive octree subdivision; Step 4: Constructing a high-fidelity intelligent sampling database containing a Bayesian dynamic update layer; Step 5: Performing uncertainty assessment-driven sampling-assisted decision-making to update the database. This invention solves the problems of insufficient depth of multi-source data fusion, low fidelity due to rigid three-dimensional modeling mesh, and lack of an uncertainty-based active sampling feedback mechanism in the prior art, achieving accurate characterization of complex strata and pollution distribution and intelligent feedback of sampling decisions.

A method for handling a mapping information enabling to map a space. A first node obtains first mapping information from a first space. The first node also obtains other information indicating one or more indicators of performance of the communications system. The first node then adapts one or more first characteristics of the first mapping information for transmission to a second node. The adapting is based on the obtained other information. The adapting is performed so that an ability of the adapted mapping information is retained within a time constraint. The ability is to be at least one of: i) merged with second mapping information from a second space at least partially overlapping with the first space, and ii) used for localization. The first node then sends the adapted information to the second node.

This invention proposes a spatial mapping method and system for new bamboo shoot yield based on satellite-UAV fusion. Addressing the problem of remote sensing observation gaps caused by the underground growth of bamboo shoots, this invention constructs a multi-scale fusion framework of "satellite identification of potential high-yield areas + UAV inversion of old bamboo density." First, a new bamboo yield prediction model is constructed using old bamboo density and topographic factors as variables through ground-based sample plot surveys. Second, indices such as NDVI and LSWI are calculated using time-series imagery, and a decision tree model is used to identify potential new bamboo germination areas in high-yield bamboo forests. Then, based on high-resolution UAV imagery, a region growing algorithm is used to extract individual old bamboo plants and invert the spatial distribution of old bamboo density. Finally, using the potential areas as masks, old bamboo density and topographic factors are substituted to generate a spatial distribution map of new bamboo yield. This invention enables large-area, low-cost, and high-precision mapping of new bamboo shoot yield, providing core technical support for precise bamboo forest management.

The application discloses a kind of high slope stability evaluation and early warning method based on multivariate monitoring data fusion, belong to slope engineering monitoring early warning technical field.For the problems of existing monitoring data source isolation, early warning lag and high false alarm rate, the method includes: real-time parallel acquisition of the multi-source asynchronous monitoring data of slope;Data are time-synchronized and spatially mapped to align, construct a unified spatiotemporal coordinate system under the fusion dataset, and extract the spatiotemporal feature vector;The feature vector is input into the fusion evaluation model, and a multi-dimensional fusion stability index is obtained;Based on the real-time sequence and change trend of the index, combined with the multi-level linkage trigger mechanism, dynamic release of graded warning signal;According to the new monitoring data and engineering response, the fusion evaluation model is periodically or triggered to update.The method realizes the deep fusion and dynamic evaluation of multi-source data, improves the early warning accuracy and timeliness, and can be widely applied to the safety monitoring and early warning of high slope construction period.

The application discloses a kind of non-rigid space mapping-based multi-camera multi-target tracking methods without calibration, it is related to computer vision and intelligent video analysis technical field.The method includes: obtaining the position of target bounding box in each camera and the corresponding appearance feature representation;Extract the effective area that can be used for geometric modeling, generate binary effective area mask;Get dense displacement field and spatial correspondence confidence;Fixed step is constructed on the source camera image Sampling grid, the grid point is screened, and the high-confidence control point set from source view to target view is formed;According to the control point set, establish the cross-camera space mapping relationship under non-calibration condition;The spatial geometric similarity of target bounding box in different cameras is calculated, and the cross-camera target association is carried out by combining the appearance feature similarity, to realize the multi-camera multi-target tracking under non-calibration condition.The method can establish the cross-camera space mapping relationship, realize stable multi-camera multi-target tracking.

This invention relates to the field of image analysis technology, specifically a deep learning-based method for detecting surface defects in tab adapters. The method includes acquiring the three-dimensional geometric curvature parameters of the bending region through multi-view polarization imaging; decoupling specular reflection and diffuse structural features using a photometric distribution model to resolve high-brightness reflection interference; nonlinearly projecting the surface texture onto a two-dimensional plane using a local tangent space mapping matrix to generate an isotropic corrected texture map to compensate for geometric distortion; identifying suspected defect points that disrupt flow continuity through singular value decomposition of the anisotropic structure tensor; and introducing stress distribution priors and epipolar geometric constraints for multi-view spatial consistency verification to effectively filter lighting artifacts. The verified region and corrected texture are fused and input into a deep recognition network with a spatial attention mechanism to analyze the surface defect image. This invention achieves accurate analysis of surface defect images through deep coupling of three-dimensional geometry and a photometric physical model.

The present application relates to the technical field of nursing information management, in particular to a call distribution method and system based on nursing needs, comprising the following steps: collecting bedside heart rate, blood oxygen and blood pressure measurement values and constructing an abnormal trigger identifier based on a threshold value, calling a Boolean logic function to generate an emergency code for a patient bed call, calculating the remaining time of a single item based on the task start time to obtain a task load value, generating a response candidate path based on the spatial mapping node and the topological connection relationship, and performing multi-dimensional weighted comparison to generate a nursing call task distribution sequence. In the present application, by real-time monitoring of abnormal signs and establishing a rigorous emergency classification, automatic classification of call requests is realized, dynamic assessment of resource load and passing cost weight is performed, and multi-dimensional weighted logic is used to optimize the task distribution sequence, solving the problems of information perception lag, unreasonable response sequence and resource scheduling imbalance in the traditional mode, and achieving the improvement of nursing response speed, utilization rate and accuracy in the ward.

Some aspects of the present disclosure relate to systems and methods for performing automated tasks using a robotic system in a human-centric environment. According to a first aspect of the present disclosure, hierarchical object identification is used to generate a contextual model of the environment around the robotic system. According to a second aspect of the present disclosure, a semantic understanding of a task is determined in response to a user query. According to a third aspect of the present disclosure, a task-specific controller is used in combination with a general locomotion controller to execute task or sub-task specific processes in connection with completing a query. One or more aspects of the present disclosure may be used in combination with each other and / or may be used with additional systems and processes for performing automated tasks in a human-centric environment.

This invention discloses a temporal confidence-based dynamic target image recognition method mounted on a sight, specifically relating to the field of computer vision technology. It achieves label-free automatic calibration of the reference benchmark by constructing a normalized virtual pixel coordinate system; it employs a spatiotemporal dual-path parallel architecture to extract target points and calculate semantic matching scores; it establishes a target state monitoring sequence and constructs a temporal confidence evolution model; when encountering instantaneous physical occlusion, it performs nonlinear confidence decay and combines state prediction to maintain tracking, effectively avoiding tracking link interruptions; it extracts the elliptical constraint parameters of the reference image in real time, dynamically updates the homography mapping matrix, compensates for perspective projection distortion of the imaging surface caused by high-dynamic transient disturbances, and achieves sub-pixel-level spatial accuracy correction mapping. This invention solves the technical problems of target recognition interruption and large spatial mapping deviation caused by jitter distortion and short-term occlusion in high-dynamic image acquisition environments, improving the continuity of target recognition.

This invention relates to the field of machining and intelligent control technology, and discloses a multi-process collaborative control system for machining equipment based on multi-source data. The system includes a spatial modeling module, a multi-source sensing and feature mapping subsystem, a reference calibration module, and a collaborative execution module. The spatial modeling module constructs a hierarchical partitioned spatial mesh model enveloping the workpiece and drives dynamic node growth. The multi-source sensing and feature mapping subsystem synchronously acquires operating status data, converts it into spatial attribute values, and stores them in voxel units. After the workpiece is transferred to a new process, the reference calibration module constructs a cross-process coordinate transformation matrix and performs pose transformation on the spatial attribute values ​​stored in the model. The collaborative execution module generates machining compensation increments based on the transformed spatial attribute values ​​and injects them into the CNC system. This invention solves the problem of physical feature discontinuity in multi-process flow, realizing spatial mapping and adaptive compensation from previous physical states to the current process.

This invention discloses a method and system for detecting defects in precision-diameter seamless steel pipes based on laser scanning, belonging to the field of non-destructive testing technology. The method includes: acquiring raw laser scanning data of the inner wall of the steel pipe containing distance, angle, and displacement information; constructing regular grid data through data conversion and spatial mapping; obtaining a reference cylindrical model through cylindrical fitting; performing coaxial error compensation and calculating radial residuals; expanding and correcting the residuals to generate an inner wall residual map; performing multi-scale decomposition on the inner wall residual map to obtain high- and low-frequency components; combining the two to complete defect identification and output the results. This invention solves the technical problems of coaxial error interference, difficulty in distinguishing defects from geometric deviations, and low accuracy in identifying minute defects in the inner wall defect detection of precision-diameter seamless steel pipes. It achieves accurate compensation for coaxial errors in the detection system, effectively separating the macroscopic geometric features and local defect features of the inner wall of the steel pipe, thereby meeting the technical requirements for quantitative and accurate judgment in precision testing.

A system for generating a real-time object-focused video using minimal camera arrays with pre-computed sports-field-optimized spatial mapping. The system positions virtual cameras to maintain tracked objects in focused, straight-ahead orientations while supporting one-dimensional movement between two cameras using geometric interpolation and two-dimensional movement within three-camera triangular configurations using barycentric coordinates. Computer spatial mapping with discretized depth information optimized for fast-moving object tracking in sports environments eliminates real-time depth calculation overhead, avoiding latency bottleneck and enabling ultra-low latency processing suitable for live sports broadcasting. The system includes predictive camera set switching using mathematical positioning variables, multi-object tracking capabilities with distributed processing frameworks, and intelligent 2D occlusion handling optimized for broadcast video output with parallax-induced occlusion management. Video synthesis techniques including adaptive geometric transformation, and multi-resolution image processing achieve rapid processing performance for live broadcasting applications while preventing discrete camera switching artifacts through continuous interpolation coefficients that eliminate abrupt perspective transitions.

This invention discloses a rapid target detection and localization method, apparatus, and unmanned aerial vehicle (UAV) system. The method includes: acquiring a two-dimensional scene image and scene depth information of a predetermined scene; processing the scene image using a target detection model to identify the target and its key features; solving for the target information and key feature information of the key features; obtaining the target's spatial three-dimensional attitude information using a target spatial mapping model based on the target information and key feature information; solving for the depth information of the target and key features relative to the UAV based on the scene depth information; and solving for the target's spatial pose information relative to the UAV based on the spatial three-dimensional attitude information and the depth information of the target and its key features relative to the UAV. This invention has the advantages of low computational complexity, high computational speed, and low resource consumption, and can improve the target recognition and localization efficiency of UAV systems and the mission endurance of UAVs.

The disclosure provides an image classification method and system based on GNF3Linear mapping function fine-tuning, relating to the technical field of large language models, comprising: obtaining image data to be classified; inputting the image data to be classified into an image classification model to output an image classification result; wherein the backbone network of the image classification model is a ViT-base model, in the linear mapping layer of the ViT-base model, the original Linear function is replaced by a GNF3Linear function, the GNF3Linear function keeps linear combination of input image features and weight matrix, and then realizes the calculation process of mapping from input space to output space through adjustment of the bias term, only changes the memory size of the intermediate variable stored in each component in the activation memory in the derivation process, and stores the segment label, mean and standard deviation of each component according to the set memory.

This invention discloses a method for calibrating the coordinate position consistency of the master and slave arms of a robotic arm, comprising the following steps: Step 1: Calculate the position difference and attitude difference between the master and slave arms; Step 2: Calculate the master-end target pose, slave-end feedback pose, and command target pose at the same sampling timestamp, and generate a valid synchronous motion sample sequence; Step 3: Generate a coordinate consistency deviation sequence based on the valid synchronous motion sample sequence; Step 4: Divide the coordinate consistency deviation sequence into time windows and input it into an improved TimesNet model; Step 5: Associate the deviation time-series representation sequence with a unified spatial mapping matrix and generate a calibration compensation parameter set; Step 6: Correct the unified spatial mapping matrix to obtain an updated synchronous motion sample sequence. This invention achieves coordinate consistency calibration of the master and slave arms through an improved TimesNet model.

This invention discloses a 5G base station deployment optimization method based on an improved quantum evolution algorithm, comprising: acquiring geographical data and defining initial conditions; constructing a multi-level candidate site pool; establishing an objective function that balances coverage performance and construction economy; performing population encoding based on qubit probability amplitude to achieve an equal-probability superposition state; obtaining discrete base station deployment schemes by observing the superposition state during quantum evolution, and ensuring that the schemes meet preset construction indicators through heuristic repair logic; guiding the population to evolve towards the optimal region by updating the probability amplitude of qubits based on an adaptive quantum rotation gate update mechanism; outputting the global optimal solution and performing spatial mapping to obtain the optimized deployment scheme. This invention, through the combination of differentiated modeling of heterogeneous networks and quantum search logic, can effectively reduce signal overlap redundancy between base stations and achieve optimal network coverage performance configuration with limited construction resources.

A computer-implemented method is provided for designing a processing path to process a part from a workpiece by a material processing system. The method includes receiving a template point cloud for a reference workpiece and a template processing path for processing a reference part from the reference workpiece. The method also includes generating a workpiece point cloud comprising a set of spatial points defining the workpiece and aligning the workpiece point cloud with the template point cloud to determine a spatial mapping between the workpiece and the reference workpiece and between the part on the workpiece and the reference part on the reference workpiece. The method further includes producing a robotic program tailored to the workpiece that includes a processing path about the part on the workpiece that is generated based on the template processing path and the spatial mapping.

This invention discloses an AI-based UAV inspection and identification data management system and method, relating to the field of UAV inspection data management technology. The invention constructs a distortion field using inspection images, UAV pose data, and single-point ranging values. It then generates a spatial mapping relationship between the defect area and the target device's 3D model, calculates the actual physical dimensions, corrects the original identification labels, and obtains updated defect attributes. By calculating the distortion degree of the spatial mapping relationship, it generates downgraded defect attributes for areas exceeding limits and calculates physical size evolution data by aligning the spatial mapping relationships at different time points. A dual-track data structure is constructed, storing the defect area and original identification label in the first data track, and storing the actual physical dimensions, updated and downgraded defect attributes, and physical size evolution data in the second data track, establishing a bidirectional pointer association. When responding to requests, the defect areas are sorted and statistically analyzed using the actual physical dimensions as the weight.

This invention relates to a multi-organ biological age and disease risk assessment system based on chest CT radiomics, belonging to the field of medical image computer-aided analysis technology. The invention aims to address the problem that a single global image age cannot characterize organ-specific aging and lacks a closed-loop clinical risk quantification mechanism. The technical solution includes: receiving chest CT radiomics features and demographic parameters of the subject through an input layer; performing spatial mapping using a feature layer; outputting a 10-dimensional biological age through a three-stage cascaded machine learning model in the age prediction layer; calculating the age acceleration rate through a bias correction layer; quantifying the disease risk multiple by coupling a Cox proportional hazards model through a risk assessment layer; and finally, generating clinical interpretation through a large language model driven by an intelligent reporting layer. This invention achieves low-cost and non-invasive organ-level aging assessment, accurately capturing allometric aging, and improving the efficiency of clinical risk stratification and the standardization of interpretation.

The application relates to the technical field of building three-dimensional modeling, and discloses a three-dimensional modeling information matching method in building operation based on AI, which comprises the following steps: extracting a native level bounding box tree for accelerated rendering in a three-dimensional modeling engine, mapping the hierarchical structure of the tree into a spatial matching potential field; driving a dynamic semantic node to complete gradient descent flow along the hierarchical bounding box tree, and determining a distribution matrix of the dynamic node to an initial grid model; calculating a Laplace differential coordinate array of a grid vertex to lock native high-frequency curvature features, combining position anchor point constraints to construct and solve a sparse linear equation set, and reconstructing an updated three-dimensional model; the application locks the curvature details of a model surface by using differential geometric features, makes the three-dimensional model maintain original geometric textures when responding to displacement anomalies, solves the problem of topological tearing caused in a dynamic updating process, and enhances the fidelity of spatial mapping.

The present application relates to the technical field of blasting construction, and in particular to a method for safety evaluation and analysis of harmful effects of metro tunnel blasting, comprising: based on the tunnel contour line, monitoring points are arranged along the axial and radial directions of the tunnel to obtain real-time blasting data of the metro tunnel blasting; according to the spatial position corresponding to the last blasting section, the damage range and damage intensity of each blasting are defined; according to the damage intensity of each monitoring point in the damage range, the damage intensity is processed according to the single-point blasting effect and the multi-point superposition blasting effect to construct the spatial mapping relationship corresponding to each monitoring point; based on the spatial mapping relationship of each monitoring point, the harmful effect state of each monitoring point is identified, and when the harmful effect state of the monitoring point is the activated state, the propagation path between the monitoring points is constructed; the data on the propagation path is coupled and correlated, and the output times of the monitoring points in the propagation path are used to generate the judgment level of the harmful effect. The accuracy and efficiency of the analysis of the harmful effects of blasting are improved.

The application discloses a mine area vegetation recovery identification method and equipment based on land cover dynamics and belongs to the technical field of ecological monitoring. According to the plant community grade, the mine area observation image is decomposed into multiple pixels, and long-time sequence vegetation change grade data are constructed. A vegetation grade dynamic change index system is constructed, the vegetation is divided into several grades, the vegetation grade change break point quantity in the target time and space, the maximum grade span of the vegetation grade change and the vegetation grade duration are calculated. The break point quantity, the maximum grade span and the vegetation grade duration of the vegetation grade change are analyzed item by item, the vegetation grade stability index is calculated and spatial mapping is carried out. The discrimination standards of different types of vegetation are determined, and the spatial distribution and the recovery stage of the vegetation natural recovery are identified. The application is simple in arrangement, convenient to use and capable of quickly identifying the recovery area.

This invention discloses an integrated method for predicting karst collapse areas and individual karst structures, relating to the field of karst geological hazard prediction technology. The method includes the following steps: constructing karst evaluation units and obtaining their characteristic factors; constructing an area susceptibility prediction model to score the karst evaluation units based on area susceptibility, and obtaining the factor contribution vector of each karst evaluation unit based on an interpretable machine learning method; identifying potential karst collapse entities based on area susceptibility scores, factor contribution vectors, and karst geological data, and obtaining entity feature vectors; constructing an entity collapse hazard prediction model based on the structural, hydrological, and load characteristics of potential karst collapse entities and outputting entity hazard indicators; normalizing the area susceptibility scores and entity hazard indicators using a unified scaling factor, and generating integrated karst collapse area-individual prediction results based on the spatial mapping relationship between evaluation units and entities. This invention can simultaneously achieve regional screening and refined entity analysis.

This invention relates to a method for diagnosing error sources in the manufacturing process of precision micro-connector molds. Addressing the challenge of integrating multi-source heterogeneous detection data and attributing errors during the manufacturing process, it proposes a method based on semantic fingerprint extraction and incremental fusion of knowledge graphs driven by multi-source raw data. Through industrial Ethernet, dedicated communication protocols, and optical character recognition, data from CNC machining, coordinate measuring machine (CMM), electrical discharge machining (EDM), and maintenance fault logs are collected in real time, and data feature compression, geometric fingerprinting, material response analysis, and semantic phrase standardization are performed. The core solution automatically constructs and evolves a knowledge graph through low-dimensional semantic fingerprint spatial mapping and anchor point expansion mechanisms, enabling real-time location of manufacturing error sources and the formation of causal chain attribution reasoning. This method achieves dynamic identification and ontology fusion of novel error patterns, improving the efficiency of anomaly diagnosis and the self-evolutionary capability of knowledge in the mold manufacturing process.

The application provides a wharf structure multi-source data processing method and system based on digital twinning, applied to the technical field of digital twinning, through acquiring multi-source data of the wharf structure, and time stamp alignment of the multi-source data; through constructing a three-dimensional digital model, and establishing a spatial mapping relationship between data points in the multi-source sensing data sequence and structure units in the three-dimensional digital model; through calculating the internal stress state of the steel bundle, the fine perception of the internal stress state of the structure is realized; through evaluating the fatigue damage accumulation degree of the steel bundle according to the internal stress state, the internal stress state and the fatigue damage accumulation degree are rendered to the three-dimensional digital model for display, and a warning signal is generated when the fatigue damage accumulation degree exceeds a preset threshold, therefore, the application has the advantages of improving the accuracy and early warning capability of wharf structure health monitoring.

This invention discloses a method for constructing a spatiotemporal correlation model of multimodal data for the entire CNC machining process, relating to the field of CNC machining data correlation modeling. The method includes: first, discretizing the geometric model of the part into a point cloud and dividing it into multiple granularities based on points, features, and overall geometry; then, constructing feature-level granularity point cloud entities based on tool position sequence; next, associating process data based on machining features, monitoring data after time alignment based on spatial mapping, and detection data based on spatial positioning to point cloud entities of corresponding granularities; finally, achieving the fusion of multimodal, multi-scale, and multi-granular data such as geometry, process, monitoring, and detection under a unified spatiotemporal coordinate system through mutual referencing of entity unique identifiers and attributes. This invention solves the problem of unifying and associating multi-source heterogeneous machining data, providing technical support for constructing high-quality machining datasets and industrial large-scale model applications.