1685 results about "Direction information" patented technology

The invention discloses an intelligent control method and system for AR equipment, and the method specifically comprises the steps: determining a pupil center position coordinate and a cornea reflection point position coordinate of a user according to an eye image of the user, and inputting the pupil center position coordinate and the cornea reflection point position coordinate into a geometric mapping model for matching, obtaining user sight direction information; acquiring image brightness value distribution of an area near a user fixation point, determining a plurality of candidate saliency areas according to the image brightness value distribution, and comparing the candidate saliency areas with a preset saliency area template to determine a user attention focus; and acquiring head posture information of the user in real time by adopting a head posture tracking algorithm, and dynamically controlling the shooting direction of a camera of the AR equipment according to the head posture information, the sight direction information of the user and the attention focus of the user. According to the method and the device, accurate alignment of the AR content and the visual focus of the user is realized, and the immersion and interaction experience of the AR application are greatly improved.

The invention relates to the technical field of aviation communication, and discloses a true north calibration system and method for an aviation airborne laser communication equipment alignment system, and the method comprises the steps: synchronously collecting flight data through an IMU sensor and a double-antenna GPS at a preset sampling frequency, and outputting the real-time attitude angle information of an aircraft through an IMU; filtering and de-noising the synchronously acquired IMU and double-antenna GPS data, and carrying out noise suppression and deviation compensation on accelerometer and gyroscope data output by the IMU by adopting a Kalman filtering algorithm; based on the preprocessed sensor data, calculating an initial course angle by measuring relative position change and phase difference between antennas by using a double-antenna GPS (Global Positioning System); the calibration controller transmits the calculated true north direction information to an alignment system of the laser communication equipment in real time through a communication interface; and the attitude change and external environment disturbance of the aircraft are continuously monitored in the whole flight process. The method has the advantage that the alignment performance and the communication stability of the laser communication equipment are improved.

The invention provides a smart glasses AI voice interaction method, which comprises the following steps: simultaneously acquiring a user gesture image and a voice signal, acquiring a user historical interaction record, extracting key point coordinates and pointing direction information of the gesture image, acquiring hand distance information, generating a gesture motion path, simultaneously extracting frequency spectrum information and an intonation peak value of the voice signal, and generating a gesture motion path; forming a voice beat sequence; extracting a spatial semantic mode of the gesture and voice fusion data, and recognizing a core object of a user pointing instruction according to pointing coordinates of a gesture motion path and an intonation peak value of a voice beat sequence; after the core object pointing to the instruction is recognized, the user intention is determined in combination with the spatial semantic mode and the historical interaction record of the user; and the complete intention analysis result is output to the intelligent glasses display module to execute corresponding operation, and is fed back to the acquisition module to adjust the next capture parameters including the acquisition frequency and the recognition sensitivity, so that the response speed and the accuracy are improved.

The invention relates to the technical field of quality detection, in particular to a product quality control method and system based on machine vision, and the method comprises the following steps: obtaining product surface image data, calculating the gray gradient value of each pixel, extracting the gray gradient change rate, recording the gradient amplitude and direction information, and generating product surface gradient data. According to the method, through pixel-level gray scale gradient calculation, the product local feature expression ability is improved, multi-scale gradient change trend analysis is combined, the accurate recognition ability of a product defect area is improved, through texture direction angle calculation and vector field construction, the direction change anomaly detection reliability is enhanced, and the direction change anomaly detection accuracy is improved based on the combination of a direction deviation accumulated value and an abrupt change threshold value. Effective identification of a structure sudden change area is ensured, adjustment is carried out for curvature continuity abnormal points, defect boundary fitting precision is optimized, defect area internal gradient distribution and boundary feature comparative analysis are carried out, accurate classification of defect types is realized, and stability and adaptability of automatic product quality detection are ensured.

The invention discloses a foam concrete image processing method based on sub-pixel edge reconstruction, and particularly relates to the field of foam concrete image processing.The method comprises the steps that an image of foam concrete is obtained, and a boundary response feature set containing edge direction information, edge gradient change information and local intensity comparison information is extracted; performing edge structure classification on the image region based on boundary response feature set combined threshold judgment; and aiming at the image region which is judged to be the local direction convergence mutation region, identifying a cross mutation point set by fitting a direction tensor convergence trend, and executing directionally-guided corrosion and expansion image processing. By constructing a structure judgment variable based on image boundary response characteristics and guiding a corrosion expansion path and a vein backtracking path to execute sub-pixel-level aperture boundary separation, image structure processing and contour reconstruction of a porous adhesion area are realized.

The invention relates to the technical field of image data processing, in particular to a wood composite board furniture surface defect identification method and system. And wood board images are collected from the surface of the wood composite board furniture. Adaptive grid division is carried out on the wood board image according to different scales, texture direction information and high and low frequency texture components are extracted, and a texture feature map is obtained; and extracting structure edge information of the wood board image to obtain a structure information graph. Matching the texture feature map with the structure information map, and calculating local mutual information of the texture feature map and the structure information map in each region; the method comprises the following steps: acquiring resolution images of a wood board image under different scales, and calculating a mutual information matrix of the resolution images; distributing an abnormal score for each matrix element based on the mutual information matrix; and dividing grids according to the abnormal score, calculating a correlation value of adjacent grids, and obtaining a defect area based on the correlation value. And carrying out defect classification on the defect area. According to the invention, interference of natural textures can be reduced, and defect identification of small defects and tiny changes can be improved.

The invention provides a thunder-vision fusion device and a thunder-vision fusion method for automatic capture of a rotor unmanned aerial vehicle, and belongs to the field of unmanned aerial vehicle high-altitude control. The invention aims to solve the problems that the distance is generally short and a high-speed target unmanned aerial vehicle cannot be captured in the existing unmanned aerial vehicle control mode. The general direction information of the target unmanned aerial vehicle is obtained through the radio frequency detection sensor, the carrier crossing machine is guided to approach the target, when the distance is within 50 meters, the wide-angle camera sensor and the detection radar start to work, and the target and the position information thereof are obtained through the detection radar; according to an image obtained by the wide-angle camera sensor, the edge computing terminal identifies a real unmanned aerial vehicle target and eliminates interference of insects, birds and the like, the two-dimensional holder is matched to control the unmanned aerial vehicle catching net launching device to launch a catching net in the target direction after coordinate transformation, the purpose of unmanned aerial vehicle control in a sensitive area is finally achieved, and the method has a very ideal application prospect.

The invention discloses a full-state wind field simulation system and method for wind resistance detection of an unmanned aerial vehicle. The system comprises a system base, a distribution box, a system control cabinet, a modular wind wall, a protective net and a wind speed and direction sensor. The wind field simulation system is of a cuboid structure, independent fan arrays are embedded in modular wind walls, the three modular wind walls independently generate three single-direction wind fields forming 90-degree included angles, and wind fields of any size and any direction can be generated through space wind field vector superposition. The distribution box provides power support for the ventilation wall to work, and the system control cabinet can independently control the fan arrays. The protective net is fixed on the system base bracket, so that the unmanned aerial vehicle is prevented from causing system damage in the testing process; the wind speed and direction sensor senses wind speed and direction information of a wind field in a test area and quantitatively represents the simulated wind field. According to the invention, a wind field with any size and direction can be generated for an unmanned aerial vehicle wind resistance test, and necessary test platform support is provided for unmanned aerial vehicle wind resistance detection.

The invention relates to the technical field of remote sensing monitoring equipment, and discloses tropical agricultural ecology remote sensing monitoring equipment which comprises an unmanned aerial vehicle platform, a remote sensing unmanned aerial vehicle is arranged on the unmanned aerial vehicle platform, and a monitoring sensor assembly is loaded on the remote sensing unmanned aerial vehicle. The monitoring sensor assembly comprises a high-resolution camera device, a hyperspectral sensor, a thermal infrared sensor and a laser radar, a data receiving module, a data processing module and a display module are arranged in the unmanned aerial vehicle platform, and the monitoring sensor assembly is mainly used for obtaining soil images and data information. The data processing module can judge soil ecological information according to images and data, and the monitoring sensor assembly and the data processing module are matched to obtain the erosion degree and fertility condition of soil in the planting area, the moisture content, the soil type, the moisture evaporation condition and the slope and direction information of the soil. The growth state of plants in a planting area and the ecological environment of soil are monitored, and producers are better assisted in formulating targeted agricultural management measures.

The invention provides an intelligent early warning method and system for phosphorus tailing improved soil heavy metal migration, relates to the technical field of computers, and accurately predicts the main migration direction of heavy metal pollutants by combining factors such as terrain gradient, hydrological flow direction and soil structure heterogeneity. The method comprises the following steps: acquiring soil information of a to-be-detected area and soil potential information of a detection point in the to-be-detected area; determining heavy metal concentration information of the detection point according to the soil potential information; the heavy metal concentration information is used for reflecting heavy metal concentration distribution conditions corresponding to different soil depths of the detection points; determining migration direction information of the heavy metal at the detection point; the migration direction information is used for reflecting the corresponding heavy metal migration direction distribution condition of the heavy metal of the detection point in the three-dimensional space; and predicting a pollution diffusion area based on the migration direction information, the organic decomposition rate and the underground water level fluctuation information, and performing visual display.

The invention belongs to the technical field of plastic part quality detection, and discloses a plastic part gold stamping mark accurate positioning method and system based on visual identification, and the method comprises the steps: collecting environment illumination parameters, equipment operation parameters and image data, and processing the image data to obtain regional image data; performing edge detection and fracture repair operation on the regional image data to obtain identification edge feature data; calculating to obtain a position deviation quantized value according to the identification edge feature data; based on the position deviation quantized value, in combination with preset inclination direction information, adjusting an image visual angle and correcting an angle deviation to obtain corrected image data; executing edge path tracking on the corrected image data to obtain path complete data; obtaining an identifier positioning result based on the path complete data in combination with the environment illumination parameters and the equipment operation parameters; and carrying out stability evaluation on the identification positioning result, and obtaining a final system identification stability result in combination with preset detection fluctuation characteristics.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of intelligent auxiliary navigation, financial science and technology, medical health and the like, and discloses a navigation instruction generation and path updating method, device, equipment and medium. And fusing the obstacle category information and the environment depth information, generating spatial position information and direction information of the obstacle, generating a navigation instruction containing scene description information, obstacle orientation and distance description information and action suggestion information based on the spatial position information, the direction information and the environment image information, and updating a navigation path. According to the method, the obstacle category information and the environment depth information are fused, the obstacle recognition result with the semantic level is generated in combination with the environment image information, the navigation instruction containing the orientation and the distance is output, and the accuracy of obstacle perception and the practicability of navigation guidance can be improved.

The invention discloses a cosmic ray muon scattering imaging method, system and device, and relates to the field of ray imaging, and the method comprises the steps: obtaining the direction information of muons in a to-be-imaged region; calculating the most probable three-dimensional scattering track of the muons in the to-be-imaged region based on Bayesian inference and a particle multiple coulomb scattering distribution function according to the direction information of the muons in the to-be-imaged region; and reconstructing a scattering density image in the to-be-imaged region according to the direction information of the muon in the to-be-imaged region and the most probable three-dimensional scattering track. By calculating the most probable three-dimensional scattering track, the scattering points can be effectively reconstructed, the utilization efficiency of muon events is improved, the scattering points are prevented from being separated from the to-be-imaged area, the most probable three-dimensional scattering track better fits the real scattering track, and then the quality of the scattering density image is improved.

The invention discloses a traveling wave fault positioning method and system for a full parallel AT power supply traction network, and belongs to the field of railway traction power supply system relay protection. The method specifically comprises the following steps: after the full parallel AT power supply traction network has a fault, firstly, analyzing the characteristics of an AT station fault initial current traveling wave, and determining a fault line by using the amplitude of the AT station fault initial current traveling wave; then, the polarity of the fault initial current traveling wave and the fault initial voltage traveling wave of the AT station fault line is used for judging the fault direction; and finally, calculating the fault distance by selecting a positive direction or negative direction distance measurement formula according to the moment when the fault initial voltage traveling wave arrives at the outlet of the traction substation, the AT station and the subarea station and the fault direction information. According to the method, only fault initial traveling wave information is utilized, wave head identification is not needed, wave velocity parameters are not needed, and simulation results show that the positioning method not only can provide fault distance, but also can provide fault line information, is convenient for maintainers to quickly find fault points, and has extremely high robustness and accuracy.

A display method for a skill indicator includes: in response to a trigger operation on a target skill, determining a release position and an effect range of the target skill, the release position being one scene unit in a virtual scene, the release position being located in the effect range, and the effect range including a plurality of scene units; based on relative positions of a plurality of specified scene units in the effect range, determining direction information of a skill indicator of the target skill; and based on an indicator parameter that is preset for the target skill, and using the release position as a reference, generating and displaying the skill indicator in the direction indicated by the direction information.

The invention relates to the technical field of image analysis, in particular to a motorcycle paint surface bubble sagging defect real-time detection system based on image analysis. The method comprises the following steps: firstly, acquiring a plurality of images of a to-be-detected paint surface under different illumination conditions by an image acquisition and processing unit, and preprocessing the images to obtain a preprocessed multi-illumination image; then, a three-dimensional information analysis unit analyzes and calculates a surface normal vector of each pixel point of the paint surface according to brightness information of the same pixel point in the preprocessed multi-illumination image, and generates a surface normal graph for representing three-dimensional direction information of the paint surface; then, a candidate area positioning unit analyzes a gradient field of the surface normal diagram and positions bubble and sagging defect candidate areas respectively; and finally, the feature extraction and classification unit extracts fusion features of the defect candidate region, and outputs a detection result obtained by recognition of the classifier through an output unit. According to the invention, the accuracy of paint bubble sagging defect detection can be improved.

The invention provides a ship traffic flow prediction method and device, electronic equipment and a storage medium, and belongs to the field of marine traffic management. The method comprises the following steps: acquiring ship trajectory data of a to-be-detected port, and performing bidirectional trajectory prediction restoration on the ship trajectory data to obtain restored trajectory data; performing direction identification trajectory clustering on the repaired trajectory data to obtain trajectory clustering data, and performing density adaptive parameter node clustering on the trajectory clustering data to obtain node clustering data; and performing spatial-temporal feature extraction and prediction output on the node clustering data to obtain a ship traffic flow prediction result. According to the method, the integrity of ship trajectory data is ensured through bidirectional trajectory prediction and restoration, trajectory direction information is mined while clustering is performed through direction recognition trajectory clustering, and node comprehensive information is mined while clustering is performed through density adaptive parameter node clustering; dynamic transformation information and a deep relationship can be reserved, data can be effectively integrated, and the accuracy of ship traffic flow prediction is improved.

The invention provides a single-microphone multi-array pickup method and system based on sound attenuation simulation, and the method comprises the steps: obtaining an original audio signal recorded by a single microphone and sound source direction information, and carrying out the sound attenuation simulation based on the sound source direction information and a preset virtual microphone array orientation parameter, calculating the included angle between the sound source direction and each virtual microphone; generating an audio intensity attenuation coefficient of each virtual microphone according to the included angle; and acting the audio intensity attenuation coefficient on the original audio signal to generate multi-channel audio data simulating the multi-microphone array. By adopting the method, the response difference of different microphone positions to the sound source can be simulated, the pickup result of the multi-microphone array is obtained, and high hardware cost and complex installation flow caused by actual deployment of a complex microphone array are avoided; and low-cost and high-diversity training data sources are provided for sound source localization, noise suppression and other models based on deep learning.

The invention provides an arbitrary point turn-back method under a train control system route architecture based on communication, which comprises the following steps of: when an arbitrary point turn-back command for a train is received and the train is determined to be in a stable stop state, generating a reverse route containing turnout opening information. And according to the reverse direction head code, generating a starting end signal machine list of which the route needs to be manually solved and sending the starting end signal machine list to an automatic train protection system. And an unlocking request command of the first signal machine in the starting end signal machine list is sent to the area controller, and the access in the starting end signal machine list is unlocked through interactive verification with a train automatic protection system. After it is confirmed that the route corresponding to the first signal machine is successfully unlocked, a turn-back instruction is sent to the automatic train protection system, and meanwhile the automatic train protection system is controlled to execute end changing operation. After the end changing operation is completed, new movement authorization is established to control the train to run, route unlocking in the original direction and passenger prompting are triggered when preset conditions are met, meanwhile, a turn-back mark of any point is updated and displayed on a human-computer interface, and turn-back of any point is achieved.

The invention relates to a staged-based adaptive multi-target tracking method, and provides a staged association thought and an adaptive weight strategy, when a to-be-tracked target in a video sequence has complex conditions such as long-time shielding, deformation and illumination change, target trajectories are classified according to the target shielding degree, and the target tracking accuracy is improved. A reasonable track detection frame association sequence is set in combination with the actual situation, and meanwhile, different association strategies are designed for each association, so that the tracking accuracy is further improved; a confidence coefficient difference value and speed direction information are used as a weak clue to assist a strong clue in matching between a target track and a detection frame, so that the correlation success rate is further improved; and the appearance feature updating module designs different appearance feature updating strategies for the targets with different shielding degrees so as to improve the discrimination of the appearance features. The tracking method provided by the invention can still keep track continuity and tracking stability in a complex scene.

The invention discloses a structural terrain semantic segmentation image optimization method based on gradient data. The method comprises the following steps: converting depth data (Depth) into gradient data with image pixel gray value change based on a Sobel operator, normalizing the gradient data, and displaying the normalized gradient data into a normal map by using red, green and blue (RGB) data; the normal chartlet and red green blue (RGB) data are fused according to a pixel point matrix Max method, recoding of an input image is achieved, and normal direction information with color representation and image information fused with a basic RGB image are output; and optimizing the edge of the semantic segmentation result of the MobileNetV3 lightweight network based on the Canny edge detection data and the gradient data of the normal map. Compared with the prior art, the method optimizes the edge of the semantic image, and gives consideration to both speed and precision. The semantic segmentation edge optimization method provided by the invention is simple and efficient, and is suitable for semantic segmentation of most structural terrains.

The invention discloses an infrared small sample target detection method based on domain self-adaption, and solves the problems that an infrared image generally lacks rich visual details and semantic information, so that effective features are more difficult to extract in a deep learning model, and an existing cross-domain algorithm is lower in accuracy in infrared target detection. And realizing style migration from the visible light image to the infrared image through a cross-domain visual optimization model. A YOLOv8 target detection network is improved, a DCNv4C2f module is used for replacing a specific C2f layer in a backbone network, and deformable convolution is introduced, so that the features of objects with complex shapes and appearances can be captured more effectively. A new multi-pooling mixed attention mechanism is designed and introduced into a target detection network, cross-channel information can be effectively captured, direction information and position information can also be obtained, the detection capability of the multi-pooling mixed attention mechanism on a fuzzy target and a dense target in an infrared image is improved, and the problem of missing detection is effectively avoided.

The invention relates to the technical field of traffic control, in particular to a smart city traffic guardrail anti-collision prompting system, which comprises a vehicle sensing module, which is used for detecting passing vehicles, acquiring the speed, position and direction information of the vehicles, transmitting the information to a data center in real time and storing data based on sensors mounted on two sides of a road, recording as a data set, and generating vehicle dynamic data. According to the invention, through comprehensive collection and monitoring of the vehicle driving data, accurate evaluation of vehicle behaviors is realized. The speed, the position and the direction of the vehicle are collected and analyzed in real time, and data support is provided for judging the relative position and the movement trend between the vehicle and the guardrail. And whether the vehicle may deviate from the normal driving route is effectively predicted by comparing the real-time driving route with the preset route. Through continuous tracking and immediate feedback of vehicle behaviors, the sensitivity and accuracy of the early warning system when potential collision risks are judged are remarkably improved.

The invention discloses an agent action prediction method based on multi-scale spatial perception, which comprises the steps of map spatial structure modeling, historical trajectory feature extraction, feature fusion and multi-modal action prediction. According to the map space structure, map feature extraction is conducted on two-dimensional vector map data in an application scene map through a multi-scale map convolutional neural network, and high-dimensional map feature information is obtained; the historical trajectory feature extraction adopts a convolutional neural network and a feature pyramid network to extract high-dimensional trajectory data features of all agents; according to feature fusion, correlation modeling fusion is carried out on high-dimensional map feature information and high-dimensional trajectory data feature information through a self-attention mechanism, and agent trajectory fusion features with direction information are obtained; according to the multi-modal trajectory prediction, intelligent agent trajectory fusion features are used for prediction regression and confidence scoring, multi-modal complete trajectory coordinates of action prediction and corresponding confidence scores are output, and a reasonable auxiliary decision is provided for intelligent agent actions.

The present application relates to the field of federated learning and privacy protection, and particularly relates to a direction-aware local differential privacy federated learning method. The present application effectively solves the problem of serious decline in model performance caused by the destruction of gradient direction information by traditional differential privacy methods by adopting a direction-aware noise selection strategy based on an exponential mechanism locally on the client side. Specifically, the client first performs L2 norm clipping on the gradient to control the sensitivity, then generates K candidate noise vectors from a Gaussian distribution, evaluates the direction consistency of each candidate noise and the true gradient through cosine similarity, and selects the noise closest to the gradient direction in a probabilistic manner using the exponential mechanism, and finally adds the selected noise to the gradient to complete the perturbation. The method provides a strict epsilon-local differential privacy mathematical proof for the entire noise selection process, reduces the calculation time by about 80% compared to existing direction-aware methods, and achieves a triple balance of privacy protection, model utility and computational efficiency.

The invention discloses a medical image weak supervision variation segmentation method based on single-point labeling, and relates to the technical field of tumor segmentation in medical images. The method comprises the following steps: firstly, randomly taking a point in a target tumor area, and constructing a new anisotropic Riemannian metric according to texture direction information of a whole CT image, local direction information of gray scale at a boundary and heterogeneity of the gray scale; secondly, calculating two distances from all points in the whole CT image to the points taken in the tumor area: geodesic distance and Euclidean distance based on new Riemannian metric; then, the two kinds of point-by-point distance information are embedded into a Chan-Vese model; and finally, proving the existence and uniqueness of a model solution, and solving the model solution by adopting a Douglas-Rachford algorithm to obtain a final tumor segmentation result. According to the method, a more accurate, more effective and more robust segmentation result can be obtained, the method can be used for tumor detection, positioning and segmentation, the workload of manual labeling can be greatly reduced, and the method has high clinical application value.

The invention relates to the technical field of mark point identification and positioning, in particular to a polygon mark point rapid identification and positioning method and system and a storage medium. Then, the gradient direction information and the coordinate information of the reserved strong edge are used for rapidly estimating the rotation angle and the center of the polygon mark point to be detected, the method is small in calculation amount and easy to operate, and rough pose information of the polygon mark point to be detected can be rapidly obtained; the estimated angle is further corrected in a small range in a rotary projection mode, the deviation between an estimated value and a real value is reduced, and a more accurate initial value is provided for subsequent registration; finally, the corrected coarse positioning result is used as an initial value for registration, the convergence speed can be increased, the number of iterations can be reduced, meanwhile, the probability that iteration falls into a local extreme value can be reduced, the operation speed is increased, and the accuracy rate is increased.

The invention provides an intention recognition method based on unmanned aerial vehicle cluster three-dimensional trajectory analysis, and relates to the field of group behavior recognition of unmanned aerial vehicle clusters. Through preprocessing the three-dimensional trajectory, the consistency of the trajectory in time and space dimensions is ensured, the stability and quality of data are improved, the subsequent intention recognition process is more accurate and reliable, through the improved HDBSCAN algorithm, the direction information of the trajectory is effectively recognized, and the clustering structure is dynamically adjusted under different density levels, so that the accuracy of intention recognition is improved. And the intention identification of the unmanned aerial vehicle in different group scales and complex task scenes is more accurate. In combination with a deep learning network, the method can better capture the time dependency relationship of a three-dimensional track, improves the analysis capability of a complex behavior mode in an intention recognition process, improves the accuracy and reliability of a recognition result, and improves the recognition efficiency in complex scenes such as multi-unmanned aerial vehicle cooperative combat. More effective identification of the unmanned aerial vehicle cluster intention is realized, and the intention identification performance is greatly improved.

The invention discloses a two-dimensional construction drawing exporting method and system based on a three-dimensional BIM model, and the method comprises the steps: analyzing the geometric information and attribute information of a component from obtained three-dimensional BIM model data, and the geometric information comprises pipeline geometric information, equipment geometric information and structure geometric information; on the basis of the analyzed geometric information, automatically identifying a labeling object; carrying out labeling processing on the identified labeling object, calculating an optimal angle and a character avoidance position of a labeling lead in a labeling process, and generating optimized labeling information; on the basis of the analyzed attribute information, automatic layer mapping is conducted on the component, and standardized layer information is generated; on the basis of the analyzed pipeline geometric information, performing axonometric projection transformation on topological relation data obtained through pipeline topological relation analysis, and generating axonometric map information including a pipeline space connection relation and flow direction information; and integrating the labeling information, the layer information and the axonometric drawing information, and outputting a standardized two-dimensional construction drawing.

The invention discloses a high-precision walking positioning system and method for a secondary lining trolley, and the system comprises a walking mechanism, a steering mechanism, an air switch group, a walking drive controller, a steering drive controller, a laser distance measurement sensor, a data processor, and an informatization platform. The steering mechanism adjusts the advancing direction of the secondary lining trolley, the laser distance measuring sensor measures the distance between the secondary lining trolley and a tunnel primary supporting face, and the data processor is connected with the walking driving controller, the steering driving controller and the laser distance measuring sensor and controls the secondary lining trolley to walk based on preset position data. And the informatization platform is used for storing tunnel mileage and construction design process data and is in communication connection with the data processor. Accurate positioning of the secondary lining trolley is achieved in an automatic and intelligent mode, and the positioning accuracy and construction efficiency of the secondary lining trolley are improved.