279 results about "Boundary detection" patented technology

The invention belongs to the technical field of artificial intelligence, and relates to an artificial intelligence-based electric energy quality disturbance identification and positioning method, which comprises the steps of constructing an electric energy quality disturbance signal data set, performing segmented preprocessing on electric energy quality disturbance voltage data, enhancing time-frequency joint features and encoding disturbance sensitive areas. And constructing a deep learning model for power quality disturbance identification and positioning, and identifying and positioning the power quality disturbance. According to the invention, through adaptive denoising processing, boundary detection and multi-resolution time-frequency feature extraction, the identification precision and positioning precision of power quality disturbance are significantly improved; self-adaptive wavelet denoising and dynamic segmentation are combined, noise interference is effectively suppressed, and the edge characteristics of voltage sudden change points are kept; according to the dual-task sharing network, disturbance identification and positioning tasks are cooperatively optimized, so that the network can consider disturbance classification and time positioning at the same time; and through Bayesian reasoning, the system can output confidence estimation, provides credibility quantification of identification and positioning results, and effectively improves the reliability of the system.

The invention belongs to the technical field of document content extraction, and particularly discloses a document content extraction method and system based on multimodal model collaboration, a terminal and a medium. Comprising the following steps: identifying the type of an input to-be-processed document, and judging the document type; on the basis of the type identification result, calling a multi-modal model to analyze the document content, and outputting space coordinates, visual features and semantic features of document elements; generating a content sequence according with a reading habit through a semantic sequence reconstruction algorithm; paragraph boundary detection, paragraph recombination and semantic association modeling of charts and texts are completed based on the multilayer attention network and the graph neural network; grammar error correction, format optimization and title hierarchy generation are carried out by using a large language model and a hierarchical classification network; and converting the identification result into a structured output file. According to the method, the processing requirements of different types of documents can be considered, and high-precision analysis and efficient output are realized under the scenes of complex layouts, multiple languages and formula tables.

The invention relates to the technical field of industrial production intelligent process control, in particular to an AI-based electrolytic manganese process production quality intelligent prediction method and system, and the system comprises a drift calibration module, a structure matching module, a process parameter inversion module, a path correction module and a deviation verification module. According to the method, dynamic compensation of deviation between input parameter drift and output parameters is achieved through the response relation between input and output of all links, the actual influence degree of fluctuation of all the parameters on the target control parameters and product quality is effectively recognized according to correlation matching of the periodic change of the production process and the fluctuation frequency of the target control parameters, and the accuracy of the product quality is improved. Potential interference of key process parameters on stability of each process is locked in time, diffusion starting time and space of impurities are clearly traced through path judgment of residual liquid concentration trend and raw material adding position, and migration path parameters are further adjusted based on ion retention behaviors of boundary detection points.

The invention relates to a distributed trajectory planning method and system for an unmanned aerial vehicle cluster in an obstacle environment. The method comprises the steps of initializing a system, constructing a local Euclidean symbol distance site map, and realizing real-time interaction of state information of the unmanned aerial vehicle. An initial collision-free path is generated using jump point search. And solving an optimal control point through an L-BFGS algorithm through multi-constraint trajectory optimization in combination with a load swing dynamics model, four-rotor dynamics limitation, cluster collision avoidance and environment obstacle avoidance requirements. And a dynamic time redistribution strategy is adopted, and the track time interval is adjusted according to speed and acceleration overrun conditions. The method further comprises an adaptive re-planning mechanism, and local target points are updated in real time and adjacent aircraft collaborative optimization is triggered based on local map boundary detection and quadrotor track safety detection. According to the method, efficient, safe and stable trajectory planning of the hanging load quad-rotor unmanned aerial vehicle cluster in a complex environment is realized, the requirement of autonomously and efficiently completing tasks is met, and the task execution efficiency and safety are improved.

The invention belongs to the technical field of remote sensing image intelligent processing and agricultural information extraction, and particularly relates to a remote sensing large model pre-training and multi-granularity boundary supervision natural field type extraction method, which comprises the following steps: firstly, pre-training a model on a large-scale space-time spectrum remote sensing data set, and combining anchor point sensing mask and geographic information coding; secondly, inputting the multi-scale features into a multi-branch structure sensing network, and outputting a semantic segmentation prediction map through high and low resolution double input and dynamic attention fusion; generating a multi-scale field boundary label through morphological operation, and outputting a boundary prediction map through multi-task supervision after domain enhancement of features by a frequency space double-domain enhancement module; and finally, aligning the two images and performing pixel-level operation to obtain a high-precision extraction result. Through the method, the global classification error during cross-region migration is greatly reduced, the method is adaptive to a low-pixel wide ridge, the boundary detection value and the closure rate of a small-scale field are improved, and the extraction precision of a large field and a small field is considered.

The invention discloses an image edge cutting method and device based on boundary detection, equipment and a storage medium, and relates to the technical field of image boundary detection, and the method comprises the steps: carrying out the preprocessing of an original image, and obtaining a target image; determining a scene type of the target image; based on an edge detection strategy corresponding to the scene type, performing edge detection on the target image to obtain an edge image; performing straight line detection on the edge image to obtain a plurality of candidate straight line segments; and based on a boundary positioning rule, determining a target boundary line segment from the plurality of candidate straight line segments, so as to carry out edge cutting on the original image according to the target boundary line segment. According to the invention, a more reliable image edge cutting effect can be generated.

The invention discloses a sign language translation model, a sign language translation system and a sign language translation method based on full-modal alignment. The sign language translation method comprises the steps of extracting multi-modal features of hand, face and body postures from an input video and performing preliminary fusion; deep secondary fusion and alignment are carried out through multi-scale time sequence coding and a cross-modal collaborative attention mechanism, and a spatial-temporal feature sequence of full-modal alignment is generated; performing boundary detection and dynamic segmentation on the feature sequence by using a CTC-based sequence prediction model, and outputting a discrete sign language word sequence with a timestamp; and finally, capturing a sign language grammar structure of the sequence through a graph structure enhanced Transform encoder, inputting the sign language grammar structure into a Transform decoder integrating grammar consistency loss, and generating a target text conforming to target natural language grammar and semantic rules. According to the method, the problems of continuous sign language action adhesion and grammar structure difference are effectively solved, and the sign language translation accuracy and the natural language generation fluency are greatly improved.

The invention discloses a pulmonary nodule boundary detection method based on semantic segmentation. The method comprises the following steps: generating an edge prior image set; obtaining a multi-scale boundary alignment feature map set and a deformation offset parameter set; a post-gating deformation offset parameter set is obtained, the multi-scale boundary alignment feature image set is updated, cross-scale feature fusion is executed, and a fusion feature image set is generated; establishing a node set and an edge set by utilizing node features in the fusion feature graph set and a spatial adjacency relationship, and initializing a node feature vector set; forming a fusion feature map set enhanced by multi-hop reasoning; and outputting a pulmonary nodule region mask prediction image set and a pulmonary nodule boundary probability prediction image set. According to the method, fracture between pseudo boundaries and slices is effectively inhibited, so that a three-dimensional structure prediction result is more coherent, and high-readability and high-consistency boundary segmentation output is provided for a clinical CAD (Computer Aided Design) system.

The invention relates to the technical field of voice signal processing, and discloses a voice segmentation intelligent editing system based on deep learning. The system comprises a voice feature extraction module, a segmentation boundary detection module, a semantic content analysis module, an editing strategy generation module and a real-time quality evaluation module. The voice feature extraction module collects multi-dimensional voice features and timestamp information, and verifies feature integrity and timeliness; the segmentation boundary detection module identifies voice pause intervals and semantic turning nodes and divides segmentation units and boundary types; a semantic content analysis module extracts text content and emotion features of each segment, and analyzes semantic topic relevance and information density; an editing strategy generation module formulates a segmentation retention rule and a sequence adjustment scheme, and matches user preferences and scene demands; the real-time quality evaluation module monitors voice fluency and information integrity in the editing process and analyzes splicing errors and user feedback. According to the system, intelligent processing of the whole voice editing process is realized.

The invention discloses a remote sensing image semantic segmentation method and equipment based on an enhanced MambaVision architecture. The method comprises the following steps: performing initial feature extraction on a to-be-segmented high-resolution remote sensing image through a patch embedding module; the initial features are input into a MambaVision backbone network to extract multi-scale features, the backbone network comprises four layering stages, the stage 1 and the stage 2 adopt adaptive expansion convolution blocks to balance high and low frequency features, and the stage 3 and the stage 4 adopt two-dimensional selective scanning and a multi-head attention mechanism to capture global dependence; performing fusion and up-sampling on the multi-scale features through an MFMS decoder, and optimizing texture and boundary detection; the decoder comprises an MFMS attention module and a channel mixer; and performing multi-scale convolution fusion on the output of the MFMS attention module based on the channel mixer to generate a segmentation result. According to the method, through two-dimensional selective scanning, adaptive expansion convolution and a multi-frequency multi-scale MFMS decoder, the segmentation precision and efficiency are improved, and the method is suitable for high-resolution and large-size remote sensing images.

The invention belongs to the technical field of three-dimensional point cloud processing, and particularly discloses an automatic tooth segmentation method and system for oral cavity scanning point clouds, and the method comprises the following steps: collecting and preprocessing dentition three-dimensional point clouds, extracting point cloud features containing coordinates and normal directions, and constructing a neighborhood structure; setting a first-stage multi-branch network, and performing tooth and gingiva coarse segmentation and instance initial clustering on the point cloud; setting a second-stage semantic refinement network, and carrying out local cutting on the original point cloud; setting a boundary detection and boundary offset network to enhance a tooth contact area, and refining a boundary instance by clustering to obtain a boundary enhanced instance tag; and performing label fusion, and propagating the fused label to the original high-resolution point cloud through a nearest neighbor algorithm to obtain a final semantic label and a final instance label. By adopting the technical scheme, high-precision instance-level segmentation of the complete dentition point cloud is realized through semantic segmentation, boundary detection and clustering cooperative work based on a biased field.

The invention discloses an ultrasonic image denoising method based on variational mode decomposition and local space sparse fusion, which comprises the following steps of: firstly, adaptively optimizing key parameters of variational mode decomposition by using a grey wolf optimization algorithm to realize stable and efficient decomposition of an ultrasonic image; then, classifying the modal components according to the structural features of the modal components, and implementing differentiated denoising strategies for different types of modals to separate noise and reserve useful information; after modal reconstruction, a sparse expression method based on local space information is further introduced, according to the method, accurate boundary detection is carried out through gradient vector flow and gray scale proportion analysis, self-adaptive partitioning is carried out on an image according to boundary information, and finally sparse reconstruction is carried out through double dictionaries trained for different areas. According to the method, speckle noise in the ultrasonic image can be effectively suppressed, and meanwhile, the capability of keeping the edge and detail information of a tissue structure is remarkably improved, so that the ultrasonic image with higher quality is obtained.

The invention discloses a safety fence protection method and device based on the Internet of Things technology, and belongs to the technical field of safety alarm and Internet of Things application, and the method comprises the following steps: respectively collecting the distance, heat source distribution and movement track information of a target protection area through an ultrasonic sensor, an infrared sensor and a millimeter wave radar; generating a virtual fence model with an initial alarm boundary by adopting a confidence weighted fusion algorithm based on environmental condition dynamic adjustment; environmental change parameters such as obstacle distribution and meteorological conditions are monitored in real time, when the change exceeds a preset threshold value, a dynamic boundary adjustment strategy is triggered, and the fence alarm boundary shape is optimized through a boundary node position redistribution mechanism; a distributed computing framework is adopted to decompose boundary detection tasks, and the tasks are dynamically allocated according to the real-time resource utilization rate of edge computing nodes. According to the invention, the problems of insufficient real-time performance, limited dynamic adjustment capability and low system integration degree in a complex environment in the prior art can be effectively solved.

The invention discloses a hybrid PDF (Portable Document Format) document analysis and knowledge fragment construction method and device, and belongs to the technical field of digital document intelligent processing. The method comprises the following steps: firstly, carrying out layout element identification and semantic classification on a PDF page through a deep learning model, and calculating the confidence of the PDF page; when the confidence coefficient is lower than or equal to a threshold value, a correction module based on vertical and horizontal projection analysis is automatically switched to carry out boundary detection and result error correction; the fused layout result is converted into structured JSON data, and the structured JSON data is further mapped into a Markdown format with a reserved title level; and finally, knowledge fragment segmentation is carried out based on a Markdown hierarchical relationship, so that each fragment carries a complete title context path. According to the method, the problems of semantic deficiency, insufficient stability and context splitting in the prior art are effectively solved, and the robustness and accuracy of complex format PDF and low-quality scanning copy analysis and the interpretability of downstream knowledge retrieval are remarkably improved.

The invention discloses an RGB-D salient target detection method based on decoupling contrast learning, and designs a saliency detection framework integrating expression enhancement, modal collaborative perception and structural discrimination learning by combining a structural heterogeneity problem in multi-modal modeling and utilizing the frequency domain structural advantage of a deep mode and the long-distance modeling capability of Transform. By introducing wavelet convolution and Transform joint modeling, a cross-modal interaction parallel fusion mechanism and a pixel-level structure perception contrast learning strategy, high-precision, multi-scale and boundary clear detection of a salient target area in a complex scene is realized. The method can effectively solve the problems of large information difference between modes of the RGB and the depth map, difficulty in structure alignment, fuzzy boundary prediction, weak feature expression ability and the like, significantly improves semantic consistency and structural integrity of the salient region, and has good cross-modal generalization ability and robustness.

The invention discloses an electric ship remote intelligent control method and system based on cloud-side cooperation, and relates to the technical field of cloud-side cooperation computing, and the method comprises the steps: carrying out the feature extraction of images around a ship and time-space aligned multi-modal data, obtaining visual features, motion features and environment features, carrying out the feature fusion, and generating an environment situation vector; inputting the environment situation vector into a channel boundary detection model, analyzing the emergency degree of the current situation, generating a collision avoidance instruction, and generating a ship control decision through a three-level collaborative decision; combining a ship control decision with ship control characteristics, simulating a traffic development situation in a navigation area, comparing with historical ship navigation conflict data, and identifying potential conflict data; through collaborative fusion of the ship-end decision-making unit, the shore-end decision-making unit and the cloud-end decision-making unit, an optimal control strategy is generated in combination with respective advantages, intelligent decision-making of global optimization in a complex environment is realized, and adaptability and safety of ship control are enhanced.

The invention discloses a driving environment sensing method, device and equipment for an underground roadway and a medium. The method comprises the following steps: acquiring environmental point cloud data in the driving process of a vehicle in an underground roadway; based on the environment point cloud data, multi-dimensional environment perception detection is carried out on the underground roadway to obtain environment perception detection data, and the multi-dimensional environment perception detection comprises at least one of ground slope detection, obstacle detection, roadway boundary detection and roadway intersection detection; correspondingly, the environment perception detection data comprises at least one of ground slope data, obstacle information, roadway boundary information and roadway intersection information. Through the technical scheme of the invention, the problem that the underground view of the underground vehicle is limited in illumination restriction and narrow roads is solved, the effect of multi-dimensional environment perception is realized, the occurrence of safety accidents such as collision and scratch of the underground mine car is effectively avoided, and the safety of underground personnel and property of a coal mine is further ensured.

The invention relates to the technical field of trajectory control, in particular to a multi-modal motion trajectory real-time tracking method and system for machine control, which performs weight redistribution through channel noise variance and amplitude difference, performs weighted stacking and continuous updating in combination with Kalman filtering, reduces state estimation variance and channel bias transfer, and improves the tracking accuracy of a multi-modal motion trajectory. Time consistency and amplitude consistency of fusion track state data are improved, an overshoot peak value is inhibited, a deviation attenuation process is shortened, abnormal samples in a deviation sequence are rejected through an isolated forest, interference of outliers on parameter updating is reduced, and speed difference and acceleration difference are compared for a correction control sequence, so that the accuracy of correction control is improved. The boundary detection rate and the determinacy of the stage number are improved, the energy change rate is screened through the speed difference and the curvature value of the adjacent stages, the transfer relation is determined, the time sequence coherence and the geometric continuity of stage switching are kept, and the time sequence stability and the stage connection coherence of the control instruction are kept in the multi-degree-of-freedom space.

Embodiments of the present disclosure provide a cleaning method, a window wiping robot, and a storage medium. According to the embodiments of the present disclosure, when a window wiping task needs to be performed, the window wiping robot firstly spins to detect a boundary of a glass window to be cleaned. If the boundary of the glass window to be cleaned is detected during spinning, the window wiping robot performs the window wiping task directly along the boundary. The cleaning mode may save the boundary detection time and help to improve the cleaning efficiency when starting to perform the window wiping task without detecting the boundary up, down, left, and right.

The invention relates to the technical field of optical fiber communication device detection, and provides an optical fiber connector end face 3D shape detection method and system, and the method comprises the steps: obtaining a multi-step phase shift interference image sequence of an optical fiber connector end face; performing interface segmentation on the multi-step phase shift interference image sequence by adopting a fiber core boundary detection network based on radial gradient perception to obtain interface position data; based on the interface position data and the multi-step phase shift interference image sequence, performing phase unpacking by adopting a dual-coordinate system fusion phase unpacking algorithm to obtain initial phase data; performing global reconstruction on the initial phase data by adopting a regional confidence coefficient weighted reconstruction algorithm to obtain global phase field data; performing spherical parameter fitting on the global phase field data by adopting an IEC (International Electrotechnical Commission) standard constraint hierarchical particle swarm optimization algorithm to obtain spherical geometric parameters; and generating a 3D shape detection result of the end face of the optical fiber connector according to the spherical geometric parameters. According to the invention, the precision and reliability of 3D shape detection of the end face of the optical fiber connector are improved.

A harvesting machine is configured to gather harvested material into the harvesting machine during a harvesting operation. A conveyance subsystem configured to convey the harvested material from the harvesting machine to a receiving vehicle during the harvesting operation. An image capture system generates an image of the receiving vehicle and an image processor uses a machine learning system to recognize a boundary of the receiving vehicle. A control system determines a position of the receiving vehicle boundary relative to the harvesting machine, and generates a control signal based on the identified receiving vehicle boundary.

The invention relates to a multi-cavity injection mold insert defect identification method, which comprises the following steps: acquiring a high-resolution image through an industrial camera, and carrying out graying, de-noising, histogram equalization and other preprocessing to improve the image quality. Then, an edge thermodynamic diagram is generated in combination with Canny edge detection and a deep learning model, a multi-channel segmentation network is guided to extract multi-scale features and dynamically enhance edge response, and continuity and accuracy of defect boundary detection are improved by means of an adaptive feature fusion module. Furthermore, the defect area is repaired through dynamic threshold segmentation and morphological operation, and finally visual labeling and automatic output of the high-precision defect area are achieved. According to the method, parameters can be adaptively optimized to deal with image blurring and other situations, a perfect model parameter adaptation mechanism is provided, and the defect recognition robustness and engineering applicability under complex working conditions are remarkably improved.

The invention discloses a bill identifier identification method, which comprises the following steps: continuously acquiring a plurality of exposure images of a target bill at different exposure times through acquisition equipment, and fusing the plurality of exposure images according to the pixel brightness of a plurality of image pixels in the exposure images to obtain a target exposure image corresponding to the target bill; performing image enhancement on the target exposure image to obtain a target enhanced image, determining target boundary information of the region of interest according to the target enhanced image and a boundary detection model, and determining a target mask region corresponding to the region of interest according to the target boundary information; the method comprises the steps of obtaining a gradient magnitude of each pixel in a target mask region, dividing the target mask region into a plurality of pixel regions, determining a dynamic threshold value of each pixel region according to the gradient magnitudes of the plurality of pixels in each pixel region, and generating a target image according to the plurality of dynamic threshold values, the bill identification of the target bill is determined based on the target image through the pre-trained image identification model, and the bill identification precision can be improved.

The invention discloses a calculation method for determining a visible time period between a satellite and a ground station through TLE data, and relates to the technical field of spaceflight measurement and control and satellite communication scheduling. Through a dynamic sampling mechanism of cluster perception, the time resolution is automatically improved in a dense transit period, the sampling density is reduced in a non-cluster period, the redundancy calculation is remarkably reduced, and the calculation efficiency is improved. The calculation power peak congestion is avoided; a boundary detection algorithm based on elevation angle change rate symbol switching can capture a rapid jump point of a satellite elevation angle, the error merging phenomenon of short interval windows is eliminated, and the precision of an output timestamp reaches a sub-second level; the confidence identification dynamically reflects TLE data timeliness and cluster complexity, a user can intuitively evaluate the window time credibility, and the communication interruption risk caused by orbit prediction errors is reduced.

The invention discloses an anomaly detection method for a digital video monitoring system, which relates to the related field of video data processing technology, and comprises the following steps: setting a sampling period and a sampling interval, and randomly intercepting a certain number of video clips from video data acquired by the digital video monitoring system in real time; the collected video clips are preprocessed, the video clips are segmented through shot boundary detection, key features are extracted from the video clips, and the noise influence is reduced; inputting the preprocessed video clips into a pre-trained video anomaly analysis model, performing anomaly analysis on the video clips in combination with text information generated by a text encoder, and determining video clips with abnormal events; and the target detection model is applied to detect abnormal video clips, abnormal events are classified and positioned, and an abnormal detection result is displayed on an application interface of the digital video monitoring system. The problems of low video anomaly detection efficiency and incomplete video feature information are solved, and the detection precision and the intelligent level are improved.

A lane boundary detection method based on learnable line anchors and KAN (Kolmogorov-Arnold Network) network detection head is presented. The method uses a backbone image feature extraction model to perform multi-stage feature extraction on an input lane boundary image, focusing on learning and adjusting line anchors, performs line anchor pooling on the feature map output from the backbone image feature extraction model using predefined line anchors, uses a KAN network-based detection head to generate initial lane boundary prediction results, adjusts the line anchor positions for line anchor pooling in the next stage of feature map, and combines the pooling results from the previous stage into global information, and after repeating this process multiple times, performs line anchor pooling on the final stage of feature map using the adjusted line anchors.

The invention relates to a method and system for identifying a projectable area of a projector. The method comprises the steps of establishing a coordinate system taking the center of a projector lens as a base point based on boundary contour parameters of the projector, generating corresponding contour line data by utilizing the boundary contour parameters, and projecting contour lines on a projection surface to identify a movable axis range and / or a variable focus range of the projector lens. The system comprises a projection optical module, a boundary detection unit, a contour generation unit, and an independent contour projection unit and a control unit which are arranged on a shell. According to the scheme, the contour line data is generated based on the boundary contour parameters and is projected on the projection surface, so that a user can intuitively perceive the adjustable limit range, blind adjustment is avoided, the risks of image distortion, dark corners and content missing are reduced, the adjustment efficiency and accuracy are improved, practicability and flexibility are both achieved, and the user experience is improved. The usability of the projector and the user interaction experience can be effectively improved, and the defect of lack of visual boundary prompt in the prior art is overcome.

The invention provides an unmanned aerial vehicle autonomous exploration method based on boundary detection and probability sampling fusion, and the method comprises the steps: firstly generating a high-quality viewpoint through a probability sampling model, and providing precise data support for path planning in an exploration stage; secondly, efficient collaboration of global planning and local planning is achieved by means of a layered planning architecture and a topological path search technology; and finally, through a re-planning mechanism, the robustness and task continuity of the unmanned aerial vehicle in the exploration process in a complex unknown environment are ensured. Aiming at the technical defects that the existing autonomous exploration method is easy to fall into local optimum, low in calculation efficiency, insufficient in track smoothness and the like, the invention provides a solution suitable for an unknown environment, and a space exploration task can be efficiently and quickly completed.

A binocular visual field (VF) testing system and a method employing spherical (non-Euclidean) geometric modeling and eye-tracking technology to generate cyclopean visual field maps are disclosed. The system projects dichoptic stimuli to each eye independently at controlled fixation distances and dynamically records vergence responses using eye tracking. A non-Euclidean spatial encoding model based on logarithmic spirals and Jacobian analysis is applied to the resulting binocular data, enabling three-dimensional topographic field mapping. This approach extends traditional monocular field testing by accounting for retinal curvature, binocular integration, and fixation depth effects, resulting in volumetric visual field models more representative of real-world spatial perception. The system includes hardware (BINOSCOPE) and software modules that implement real-time depth encoding, topological mapping, and boundary detection of peripheral visual fields.

The invention discloses a deep hidden rock mass boundary detection method, system and device, a medium and a product, and relates to the field of rock mass boundary detection, and the method comprises the steps: obtaining multi-source data; the multi-source data comprises a surface wave profile, an electrical method profile, a seismic profile, a gravity plane, a magnetic method plane, geological drilling and physical property data; carrying out data processing and inversion on the surface wave profile and the electrical method profile, and determining an inversion profile; carrying out data processing and technical attack on the seismic section, and determining a geological interpretation section by combining physical property data; carrying out data processing on a gravity plane and a magnetic method plane, and determining 2.5 D same-section modeling by combining a geological interpretation section, geological drilling and physical property data; constructing a three-dimensional geological model according to the inversion profile, the geological interpretation profile, 2.5 D same-profile modeling, geological drilling and physical property data; and detecting the deep hidden rock mass boundary according to the three-dimensional geologic model. According to the method, the contact part of the rock mass and the surrounding rock and the deep hidden rock mass boundary can be accurately depicted.