1247 results about "Point location" patented technology

Certain examples described herein relate to a mapping system. An example mapping system has a differentiable mapping engine to receive image data comprising a sequence of images captured using one or more camera devices of an object as it navigates an environment and a neural network scene representation comprising a neural network architecture trained to map input coordinate tensors indicating at least a point location in three-dimensional space to scene feature tensors having a dimensionality greater than the input tensors. The neural network scene representation is communicatively coupled to the differentiable mapping engine and the differentiable mapping engine is configured to use the neural network scene representation as a mapping of the environment during operation of the differentiable mapping engine.

The invention discloses a robot inspection method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: collecting environment information of a target inspection space through information collection equipment, and constructing an environment map of the target inspection space through the environment information; determining the position information of the target inspection object and the position information of the obstacle in an environment map; determining an optimal shooting point location related to the target inspection object according to the position information of the target inspection object and the position information of the obstacle, and generating an inspection path related to the target inspection object; moving to the optimal shooting point position according to the inspection path to shoot the target inspection object to obtain an inspection image about the target inspection object; and sending the inspection image to an upper system for inspection analysis to obtain a robot inspection result. By applying the technical scheme provided by the invention, more efficient and intelligent article inspection and management are realized, the article safety can be effectively guaranteed, and the labor cost is reduced.

The invention provides a deep and large foundation pit monitoring method and system, a program product and a storage medium, and relates to the technical field of foundation pit detection. The GPS positioning device collects foundation pit slope crest displacement data, the monitoring sensor network collects deep horizontal displacement data, the two data types complement each other, comprehensive information of surface and internal deformation of the foundation pit is provided, and the generated deep horizontal displacement curve chart visually displays the displacement conditions of the supporting structure at all depths. A potential weak area can be identified, a foundation pit excavation response data acquisition device is arranged at the maximum stress part, response data of a key position is obtained, and the quantitative index of overall deformation is provided by measuring and monitoring the position change of a central point of a sensor network and calculating the deformation amount of the foundation pit. According to the multi-dimensional and multi-type data acquisition and integration method, the description comprehensiveness of the foundation pit environment is improved, potential risks can be judged more accurately, and limitation caused by a single data type is overcome.

The invention provides a BIM (Building Information Modeling) plane cutting analysis method based on Cesium, which comprises the following steps of: obtaining geometrical characteristics, topological relations and semantic attributes of components in a BIM, and calculating importance weights of the components according to a preset evaluation standard; obtaining the viewpoint position and the sight line direction of a user, calculating the visibility of the sub-region, and generating a clipping priority queue by combining the importance weight of the sub-region; dynamically scheduling a cutting task according to the cutting priority queue, and re-evaluating the cutting priority if the user viewpoint distance or the shielding relationship is remarkably changed; obtaining a component list of a to-be-cut sub-region, and calculating an influence weight of a component on a cutting effect according to the distance between the component and the viewpoint and the shielding relationship; and for the simplified and cut component, a progressive grid simplification algorithm is adopted, and the grid resolution is adjusted according to the geometric complexity and cutting precision requirements of the component.

The invention relates to the technical field of computer vision and industrial automation, in particular to a shoe sole three-dimensional gluing contour extraction system and method based on vision single-line laser, and aims to solve the problems that virtual calibration target spots cannot be accurately generated based on shoe sole geometry, the positions and sizes of the target spots are difficult to determine by combining curvature extreme values and principal component analysis in the prior art, and the production cost is low. The problem that a double-branch deep learning model cannot be adopted to fuse feature prediction transformation, and the re-projection error is increased is solved; a virtual calibration target spot is automatically generated based on sole geometry through a feature fusion calibration module, a grid is generated through point cloud processing and Poisson reconstruction, the position and size of the target spot are determined by combining a curvature extreme value and principal component analysis, a corresponding relation is established by utilizing two-dimensional and three-dimensional feature matching, initial alignment is realized through ICP and re-projection error optimization, and the target spot position and size are determined. A double-branch deep learning model is adopted to be fused with feature prediction transformation, iterative optimization is carried out through space consistency errors, and re-projection errors are reduced.

Examples described herein provide automatic location of access points by a computing device. Examples may include receiving, by the computing device from each AP in a subset of a plurality of APs, a Global Navigation Satellite System (GNSS) signal measurement, and based on each received GNSS signal measurement, constraining, by the computing device, the map of relative AP locations by at least one translational degree of freedom or one rotational degree of freedom. Examples may include resolving, by the computing device, locations of the plurality of APs in the map of relative AP locations.

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

The invention relates to the technical field of quality management, and discloses an engineering project quality automatic evaluation system based on AIGC and BIM point cloud algorithms. Comprising the steps of preprocessing point cloud data and image data, identifying potential quality defects through a neural network identification model, constructing a BIM model and a point cloud model of a building structure, performing difference comparison on the point cloud model and the BIM model, evaluating the quality grade of the building structure, and generating a quality evaluation report of an engineering project. Compared with the prior art, the method can ensure that the point cloud model and the BIM model can accurately and consistently represent all the point locations of the building structure, and can accurately and visually evaluate and analyze the actual quality information of the building structure by performing difference comparison on the point cloud model and the BIM model. Therefore, the quality problem of the building structure in the real three-dimensional scene can be accurately evaluated, and the method has high timeliness and accuracy.

The invention discloses an end-cloud collaborative deployment method and device for a multi-modal large model, a medium and a product, and relates to the field of multi-modal large model deployment, and the method comprises the steps: a cloud side computer obtains a to-be-deployed multi-modal large model, determines an optimized coding model and a corresponding segmentation candidate point according to the to-be-deployed multi-modal large model, and transmits the optimized coding model and the corresponding segmentation candidate point to an end side computer; the end side computer obtains multi-modal data, the optimized coding model is used for coding the multi-modal data, and intermediate data and segmentation point position information are obtained; and the end-side computer compresses and packs the intermediate data and the segmentation point position information, and sends the compressed and packed intermediate data and the segmentation point position information to the cloud-side computer through a transmission path, so that the intermediate data is calculated and processed by using a processing model to obtain a calculation result, and the calculation result is sent to the end-side computer, decoded by using a decoding model and converted into an output format. And obtaining the processed multi-modal data. The method can dynamically adjust the distribution of the calculation tasks, and improves the reasoning efficiency.

The invention provides a three-dimensional space track identification method based on multi-view three-dimensional reconstruction, which comprises the following steps of: shooting the same scene from different angles by using a plurality of cameras, and determining a picture intersection part of the plurality of cameras as a research scene; frame extraction and pedestrian detection are carried out on the video of each camera, pedestrians and position information of the pedestrians in each frame of video are identified, a unified identity number is distributed to the same pedestrian, registration is carried out on video frame images of different cameras, the pixel position of each pedestrian in each camera is obtained, and a multi-view-angle triangulation method is adopted, and internal and external parameters of the cameras are combined, so that the pedestrian positioning accuracy is improved. Solving a three-dimensional point coordinate of each pedestrian in a camera coordinate system; the three-dimensional point coordinates are converted into coordinates in a world coordinate system, and world coordinate system coordinates of pedestrian point locations of all frames and all numbers are obtained; and according to the time information, connecting the pedestrian point locations of each frame and each number to form a track, and completing reconstruction of the pedestrian three-dimensional space track in the world coordinate system. According to the invention, manpower and calculation cost can be reduced.

The invention relates to a multi-person motion capture real-time digital human driving method based on a skeleton-muscle interaction model, and belongs to the field of artificial intelligence and ubiquitous universe. According to the method, the key points of the human body are detected through YOLO-Pose, the positions of the shielding points are obtained through a double-stage dynamic shielding point compensation method, and the accuracy of the key points is guaranteed. And outputting the three-dimensional coordinates of the key points of the human body by adopting a three-dimensional key point depth extractor based on multi-dimensional exclusive-shared feature reasoning in combination with an MT ransPose model. A real person-skeleton model-digital person real-time mapping method is provided, three-dimensional key points are mapped to a standard human body skeleton model, and a digital person role and the skeleton model are bound through a weight mapping algorithm. Based on a skeleton-muscle interaction model, the digital person accurately reflects a real motion posture by introducing the mechanical action of a skeleton rigid body dynamic model and a muscle force line model, and the process of driving the digital person in real time through multi-person motion capture is achieved.

The present disclosure provides an artificial intelligence-based method for enhancing mine safety by identifying and predicting locations of water inrush points in a mine, including the following steps: S1: constructing a numerical model to determine priori information of parameters to be recognized based on observation data, including coordinates of locations of water inrush points; S2: generating a training sample dataset and a test sample dataset of an alternative model based on the numerical model and the priori information of the parameters; S3: constructing and training a neural network of the alternative model; S4: testing an accuracy of the alternative model; and S5: performing a simulated annealing algorithm to identify the locations of water inrush points and simulation model parameters.

The invention provides a reinforcing steel bar binding point detection method based on improved YOLOv8, and the method comprises the following steps: obtaining a construction site image, carrying out the preprocessing, inputting a to-be-detected image into an improved target detection model, sequentially carrying out the local space attention calculation, depth separable convolution and channel attention weighting operation, and carrying out the detection of the to-be-detected image. Detail feature expression of reinforcing steel bar intersection points is enhanced; performing dynamic weight fusion on the top-down first feature path and the bottom-up second feature path, and embedding a lightweight residual unit in a fusion node; a classification task and a regression task are respectively processed by adopting a classification branch and a regression branch, the classification branch outputs category confidence, and the regression branch outputs coordinate positioning through depth separable convolution and a probability distribution prediction layer; and decoding output data of the target detection model, and generating a detection result containing the position and the category of the binding point. According to the method, the requirement of real-time detection can be met while the accuracy of steel bar binding point detection is improved.

The invention discloses a thunder early warning model optimization method based on a deep learning algorithm. The thunder early warning model optimization method comprises the steps that five risk early warning levels are divided according to the distance between a thunder occurrence position and a target point, namely six corresponding early warning levels are obtained; selecting lightning positioning, radar extrapolation results and atmospheric electric field instrument data around the target point from the sample data set; and risk grading preprocessing is carried out on the thunder early warning probabilities and critical thresholds of the three early warning factors. The method has the following advantages that the precision is high, analysis and modeling are carried out through comprehensive multi-source data, information related to thunder and lightning occurrence and development can be captured more comprehensively, and therefore the precision of early warning is improved. Data can be obtained and processed in real time, early warning information can be updated in time, and the requirement of a user for the real-time performance is met. Personalized customization: personalized risk grading and early warning setting can be carried out according to characteristics of different target point locations and user requirements.

The invention discloses a slope deformation prediction method and a multi-source heterogeneous data acquisition system. The slope deformation prediction method comprises the following steps: acquiring multi-source heterogeneous data of each point location of a slope, wherein the multi-source heterogeneous data comprises Beidou GNSS data, MEMS data and InSAR data; preprocessing the multi-source heterogeneous data; carrying out clustering processing on the preprocessed multi-source heterogeneous data; and predicting the deformation of each point position of the slope to complete slope deformation prediction. According to the slope deformation prediction method, comprehensive prediction of slope deformation is achieved through multiple monitoring means and multiple prediction models, and the prediction result is high in accuracy, scientific and reliable.

The invention discloses an AR glasses control system and method, and belongs to the field of artificial intelligence and AR glasses application, and the method comprises the steps: capturing a user face image, combining a voice assistant, collecting user voiceprint data, and carrying out the matching through a recurrent neural network; constructing a three-dimensional environment model, positioning and marking obstacles and interactive objects, and identifying objects in the environment; the method comprises the following steps of: tracking a gesture, acquiring a key point coordinate by adopting an OpenPose algorithm, identifying a predefined gesture by utilizing a support vector machine, and mapping the gesture into a specific operation instruction; analyzing the user intention based on natural language processing, and calling a corresponding function module according to an analysis result; acquiring a gazing direction of a user by using an eye movement tracking sensor, calculating a screen focus position through an algorithm, and dynamically highlighting options or displaying information according to a gazing point position; through multi-modal data fusion, gestures, voices and fixation point input are analyzed in a unified manner. By accurately understanding the intention of the user, the interaction precision of the AR glasses is improved, and the user experience is optimized.

The invention discloses an abnormal detection method and system for a splayed anti-loosening line of a rail train, and relates to the technical field of rail traffic detection, and the method comprises the steps: image collection and processing: shooting an image of a fixed point region containing the anti-loosening line, and preprocessing the image to obtain a target image; line integrity judgment: extracting a bolt, a bolt marking line and an anti-loosening line through an analysis model to form a target area graph, and judging whether the anti-loosening line is complete or not according to line segment continuity and form; performing line deformation depth analysis, and synchronously obtaining the position of an anti-loosening line intersection point and deformation data through the model in combination with the loosening angle of the bolt marking line when the line deformation is complete; performing line deformation safety judgment, setting a safety threshold group based on historical data, and comparing deformation data to output a signal; after line deformation abnormity judgment and comparison signal receiving, actual data are compared with theoretical data output by the mechanical model, and abnormal or normal signals are output. The anti-loose line full-dimension high-precision detection is achieved, automation and real-time performance are improved, and train operation safety is guaranteed.

The invention discloses a cultural relic digital fingerprint identification method based on geometric texture and spectral information, and the method comprises the steps: obtaining multi-source data, carrying out the texture mapping, and forming a three-dimensional grid model with texture data; curvature values and topological features of all vertexes of the three-dimensional grid model are calculated, feature fusion is carried out on the basis of local saliency adjustment feature weights, and fingerprint point location screening is completed; based on the screened fingerprint point locations, a significant plane is constructed through a bit plane, and texture features of the cultural relics are extracted based on an improved histogram and a gray-level co-occurrence matrix; extracting characteristic wave bands of the cultural relics based on the screened fingerprint point locations in combination with the hyperspectral data; and multi-modal fusion identification is carried out based on features of geometry, texture and spectral information. According to the method, the geometric structure, the texture features and the hyperspectral information are fused, multi-mode collaborative cultural relic identity high-precision identification is achieved, and the method has the advantages of being high in accuracy, high in robustness, good in interpretability and capable of adapting to complex environments.

The invention provides a robot positioning prediction method and device, a medium and equipment, and relates to the field of industrial robot motion control. According to the method, on one hand, a physical-data double-branch collaborative network is designed, meanwhile, a DH parameter method is used for predicting and obtaining a theoretical position coordinate of a target robot, an improved Transform network is used for predicting and obtaining a coordinate compensation value of the target robot, so that a theoretical value and the compensation value are decoupled, and the limitation of single model establishment is avoided; and then, the two are fused, and high-precision end point location prediction for the target robot is realized by combining priori knowledge of robot kinematics and nonlinear expression ability of deep learning. And on the other hand, a spatial physical information mixed loss function is provided, and a spatial topological structure output by the DH model is used to guide the distribution of predicted values of the whole position prediction model, so that the geometric rationality of target robot end point location prediction is improved.

The invention discloses a plane and spherical surface joint constraint spectral confocal probe space pose position calibration method, which belongs to the technical field of measurement, and comprises the following steps: converting point location information under a contact probe coordinate system into coordinate information under a base coordinate system; the point location information under the spectral confocal probe coordinate system is converted into coordinate information under a base coordinate system; acquiring information of at least two point locations in a plurality of planes by adopting a spectrum confocal probe, establishing a linear equation set, and solving to obtain a spatial pose of the spectrum confocal probe; and measuring coordinate information of at least three point positions on the spherical surface by adopting the spectrum confocal probe, establishing an overdetermined equation set, and solving the zero point position of the spectrum confocal probe. According to the spatial pose position calibration method, coupling between the light beam direction and the zero point position is relieved by introducing a space equation elimination mode, respective calculation of the spatial pose and the zero point position of the spectrum confocal probe is achieved, and the precision and efficiency of spectral confocal probe pose position calibration can be greatly improved.

The invention relates to a self-adaptive step length RRT (Rapidly-expansion Random Tree, RRT) path planning method based on collision detection, and belongs to the field of robot path planning. The method comprises the following steps: firstly, initializing a system, namely initializing map information, determining a robot obstacle space and a non-obstacle space, determining position coordinates of a path initial point and a target point, a cardinal number of each connection step length, a distance target point threshold value and the like; then, path planning is carried out by using an improved RRT algorithm, including sampling in a non-obstacle space, optimization of random sampling points in combination with a target bias strategy and an APF algorithm, and expansion in a random tree expansion direction according to a dynamic step length strategy; then, judging whether the new node is smaller than a threshold value, if so, finding a path, otherwise, jumping to the sampling step; and finally, after the path is found, the path length is shortened by using a path pruning strategy, the final path is smoothed, and the path is output. Compared with a traditional RRT algorithm, the improved RRT algorithm has the advantages that the path searching speed is increased, redundant nodes are eliminated, and the path quality is improved.

The invention discloses a path planning method and device, automatic driving equipment and a readable storage medium. The path planning method comprises the steps that path information and the starting point position of automatic driving equipment are acquired, the path information comprises a path reference line, and a to-be-optimized path point is determined based on the path reference line; determining a Frenet coordinate system based on the starting point position and the path reference line; based on the to-be-optimized path point, a cost function is established under a Frenet coordinate system, the cost function is a quadratic function, a first penalty term is introduced into the cost function, and the first penalty term is used for representing the deviation between the to-be-optimized path point and the transversely-biased path reference line; and solving the cost function based on a preset constraint condition, and obtaining a target path corresponding to the minimum cost function. According to the invention, when the distance between the travelable boundary and the path reference line is small due to the fact that the obstacle partially occupies the travelable area, the planned target path can be far away from the obstacle, so that the driving experience is improved.

The invention relates to an inspection robot global path planning method based on a three-dimensional grid map. The method comprises the following steps: constructing a three-dimensional point cloud map; constructing a three-dimensional occupation grid map; calculating the height difference between each grid and an adjacent grid of the three-dimensional occupied grid map, and marking the current grid and continuous grids in the vertical direction, namely the z direction, as grids with the same attribute; giving current position and target point position information of the robot, and converting the information into serial numbers to which grids belong; and traversing surrounding grids from a starting point by using an A * algorithm until a target point position is searched, and forming a global path from the starting point to the target point. According to the invention, a traditional two-dimensional grid map is replaced by a three-dimensional grid map, so that the obstacle avoidance capability of the ground mobile robot in a complex terrain is remarkably improved, the flexibility of the robot is improved, the safety of the robot in an actual application scene is enhanced, and especially in a dynamically changing environment, the obstacle avoidance capability of the robot is greatly improved. The path can be adjusted in time to avoid collision.

The present application provides a measurement method and system, a device, and a storage medium. The method is implemented by using the measurement system. The measurement system comprises a laser ranging module, an image measurement module, and a navigation module. The method comprises the following steps: acquiring measurement data of a point to be measured, the measurement data comprising first measurement data acquired from the laser ranging module, second measurement data acquired from the image measurement module, and third measurement data acquired from the navigation module; on the basis of the measurement data of said point, calculating at least one position measurement result of said point; and on the basis of the at least one position measurement result, calculating a final position measurement result of said point by using a preset measurement fusion algorithm. In the present application, the measurement data of the laser ranging module, the image measurement module, and the navigation module are effectively fused, so that the accuracy of measurement of the spatial position of a point to be measured is improved.

The invention provides an SLAM positioning method and system based on multi-sensor fusion, and the method comprises the steps: firstly receiving data of a plurality of sensors, and obtaining initial data including visual feature data, I MU pre-integration data and UWB distance data through anomaly detection and processing; initializing by using visual feature data and I MU pre-integration data, estimating an initial pose, and predicting a UWB anchor point position as a fixed constraint in combination with UWB distance data; inputting the initial data into a sliding window, outputting drifting-free odometer data, judging the motion state of the SLAM system according to the data, and if the motion state is static, executing zero-speed updating to obtain target pose data; and finally, determining a new key frame based on the parallax or visual feature data quantity of the current frame and the previous key frame. According to the invention, the problems of relatively weak robustness and relatively low positioning accuracy of the system facing extreme scenes are solved; by means of motion state judgment and zero-speed updating, the robustness of the system in long-term operation is improved.

The invention discloses a map information updating method and system, and relates to the technical field of map updating, and the method comprises the steps: according to each road sign object, using an editing distance and a cosine similarity to measure a first similarity between symbol elements in an element set of the road sign object and symbol elements in a map element set; measuring a second similarity between the color elements in the element set of the road sign object and the color elements in the map element set by using the Euclidean distance; measuring a third similarity between a central point position element in the element set of the road sign object and a central point position element in a map element set by using a Hausdorff distance; taking the intersection-union ratio of the contour frame elements in the element set of the road sign object and the contour frame elements in the map element set as fourth similarity; determining whether the four similarities are matched or not through the four similarities; if yes, updating the map. The map can be updated in time, and the real-time performance is good.

The invention provides a video monitoring point location layout method, system and device and a medium, and belongs to the technical field of video monitoring layout, and the method comprises the following steps: collecting geographic position information in a video monitoring target area and point location information of a built video probe, and processing the geographic position information and the point location information; calculating the coverage area of each video probe and the actual coverage area of all the video probes, and constructing a fitness evaluation function; based on point location information of a built video probe, a genetic algorithm is used, a population of monitoring configuration layout is initialized, fitness calculation is carried out, population selection and optimization of crossover and variation are carried out with the maximum fitness as the target, and the optimal monitoring configuration layout is obtained and applied to an actual monitoring system when no monitoring blind area exists. The video monitoring layout is optimized through the genetic algorithm, efficient configuration and blind area complementation are achieved, the monitoring coverage rate is increased, repeated investment is reduced, and the method flexibly adapts to monitoring demand changes.

The invention discloses a multi-protocol hardware communication adaptation module based on an energy storage system and a configuration method, the module comprises a protocol template library, an intelligent mapping engine and a model generator, and the method comprises the following steps: storing structured protocol point location information through the protocol template library; an intelligent mapping engine is used for shielding the isomerism of Modbus, MQTT, IEC104 and other underlying protocols, and a unified data interface is provided; intelligent matching among different protocol terms is realized by adopting a natural language processing technology through a semantic level mapping unit; and finally, generating a target data model by a model generator through one key. The method effectively solves the problems that when the energy storage system integrates multi-manufacturer equipment, protocol configuration depends on manpower, efficiency is low, errors are prone to occurring, and expansibility is poor.

The invention discloses a path planning method and system for corner optimization. The method and system are used for cutter radius compensation and machining instruction smooth transition of a three-dimensional path of a numerical control machine tool. The method comprises the steps that a machining instruction is pre-read, and a path point location is analyzed; calculating a path direction vector and a normal vector, and determining a path plane equation; calculating a plane included angle and an intersection direction vector through a normal vector; generating a compensation curve by translating along the perpendicular direction of the intersecting line by taking the radius of the cutter as a constraint; and generating a smooth control instruction through curve subdivision and inserting the instruction into an instruction queue. The problem that three-dimensional path corner transition is not smooth is solved, the machining precision and efficiency are remarkably improved, and the method is suitable for a numerical control machine tool for complex curved surface machining.

The invention relates to the technical field of building engineering, and provides a building construction progress digital monitoring method and system. The method comprises the following steps: collecting three-dimensional point location data of a construction area, and comparing the three-dimensional point location data with a BIM model to extract a progress deviation; associating the construction log to a BIM component node and setting a first evaluation index; associating the material records to BIM component nodes, and setting second evaluation indexes; drawing up a resource map of construction process logic in combination with the deviation and the two indexes; and based on the atlas, according to a weight attenuation coefficient, multi-scale assessment of cumulative influence is carried out by taking efficiency as a feature, and resource compensation reminding is carried out in combination with dynamic threshold adjustment. The technical problem that progress deviation is difficult to accurately recognize due to data updating lagging and construction process logic disjunction in traditional building construction progress monitoring is solved, real-time accurate analysis of the construction progress is achieved through intelligent comparison of multi-source data fusion and the BIM model, resource allocation is dynamically optimized in combination with multi-dimensional evaluation indexes, and the construction progress monitoring efficiency is improved. And the construction management efficiency is improved.