266 results about "Line model" patented technology

The invention discloses an intelligent scheduling method for a comprehensive virtual power plant, and relates to the technical field of power plant intelligent scheduling, and the method comprises the steps: carrying out the redundancy elimination filtering and unified time sequence calibration of multi-node sensing data of a cold and hot energy station, energy storage equipment and the like through the deployment of an edge side high-concurrency collection module, generating a high-quality time sequence fusion sequence, and screening schedulable resources; on-line model correction is realized through an error dynamic evaluation mechanism, a multi-stage scheduling strategy is generated by adopting an improved particle swarm algorithm aiming at the targets of economy, reliability and environmental protection, and an optimal instruction is output in combination with an equipment constraint condition. Through priority control and real-time monitoring index evaluation deviation, a rolling correction and standby strategy is triggered, equipment-level fault risk quantification and self-healing control are realized, abnormity is rapidly isolated, and redundant resources are started. Through data-prediction-scheduling-execution closed-loop cooperation, the resource utilization rate, the scheduling precision and the system fault-tolerant capability are significantly improved, and the scheduling capability of source heterogeneous resources is enhanced.

The invention belongs to the technical field of BIM (Building Information Modeling), and particularly relates to a curved surface composite truss design and construction integrated method and device based on the BIM technology. The method comprises the following steps: considering four structural schemes for a building facade, generating a corresponding parameterized structural topology and a three-dimensional structural line model, adding a load, calculating, analyzing and optimizing a cross section, and generating four structural calculation models and building scheme models; the building space effects and the structure force transmission efficiency of the four structure schemes are compared, and the optimal one is selected as an implementation scheme; and forming a full-professional BIM model according to the selected implementation scheme. Building datamation is achieved through the BIM technology, digital delivery of design results is achieved, the link of two-dimensional plotting is reduced, steel structure deepening is further conducted through the BIM model in the construction stage, visual deduction is conducted on key procedures such as steel structure machining, assembling and hoisting, and digital construction is achieved.

An offline pre-training and online fine-tuning apparatus based on reinforcement learning includes a data management unit collecting and processing data for offline reinforcement learning in advance; an offline model training unit training an offline policy network and an offline state-action value function network using a previously collected dataset that includes a state, an action, a next state, a reward, and an accumulated reward collected by the data management unit; and an online model training unit performing fine-tuning to update parameters of the offline policy network using an online dataset that includes action information determined based on state information acquired through interaction with the offline policy network and an environment, state information at a next time point according to the action information, and the reward, and the previously collected dataset.

A machine learning (ML) system and methods are provided that are configured to provide a performance evaluation of an online ML model using an evaluation framework with an offline ML model. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform model comparison operations which include accessing, for a performance evaluation of a first ML model, a second ML model using the evaluation framework, determining a batch size for the performance evaluation, calculating first model scores for an analytical metric during an online run using the batch size, calculating decayed weights applied to the first model scores, comparing the first model scores with second model scores for the second ML model, and outputting the performance evaluation based on the comparing.

Disclosed in the present application are a method and apparatus for calculating carbon emissions from equipment, and a computer device and a storage medium. The method comprises: providing a utility and auxiliary facility model, an equipment model and a connection line model; by means of the connection line model, connecting the utility and auxiliary facility model and the equipment model; adding operating parameters to the models, and associating the models with a carbon emission database; and on the basis of the carbon emission database, calculating carbon emissions from equipment. In this way, a connection line model and an equipment model can be flexibly established, the connection line model is decoupled from the equipment model, and carbon emissions from an equipment group are rapidly calculated; moreover, the method enables adaptive adjustments for different scenarios, and has a wide range of application.

The invention relates to an intelligent tree barrier detection method and system based on multi-modal perception, and the method employs an unmanned plane to carry a sensor to collect data, including RGB images, LIDAR point clouds, multispectral data and the like. Firstly, data is preprocessed to form standardized input. Hough transformation is carried out on the LIDAR point cloud, the position of the power line is extracted, and a three-dimensional line model is generated. And then positioning a tree bounding box from the RGB image by using a deep learning algorithm, and screening a high-risk point cloud region in combination with a line model. And constructing a three-dimensional tree model, calculating the minimum safety clearance distance between the power line and the tree in real time through a collision detection algorithm, obtaining the three-dimensional tree model with a safety distance mark, and finally carrying out space risk assessment based on the model. According to the invention, the reliability and efficiency of power line inspection can be significantly improved.

The invention provides a gait monitoring system based on multi-modal sensing fusion, which comprises the following steps of: establishing an initial base line to form a personalized gait base line model; baseline updating is adopted, and after a system obtains new gait data, a weighted moving average algorithm is adopted for self-learning updating, so that the historical data weight is enhanced, and the baseline stability is improved. The technical problems of insufficient individual difference adaptation, lack of dynamic threshold adjustment, poor real-time performance and the like in the prior art are solved.

A straight line fitting method for image detection in a complex interference environment comprises the following steps: firstly, carrying out interference point deletion on a feature point set of an environment image, then carrying out multiple times of random sampling, forming a total point set by using associated points through distance operation, obtaining a plurality of straight line models through initial fitting, and carrying out linear fitting on the linear models; and then calculating an interior point proportion in the total point set according to the linear model, finally selecting the total point set with the maximum number of interior points, and continuously reducing a residual threshold in multiple iterative calculations to improve an interior point proportion threshold by dynamically adjusting a feedback mechanism of the residual threshold when the interior points are calculated. In the initial stage of iteration, more points can enter the model to ensure the diversity and robustness of the model, and the threshold value is gradually tightened along with the convergence of the model, so that the interior points conforming to the model are screened out more accurately. And finally, performing secondary sampling on the inner points and performing straight line fitting on an inner point set subjected to secondary sampling again to eliminate potential error inner points and further improve the fitting precision.

The invention relates to the technical field of civil engineering digitization, and discloses a steel structure physical digital twin model construction method, device and equipment and a medium, the method comprises the following steps: carrying out multi-source point cloud data acquisition on a steel structure, and generating a steel structure complete point cloud model based on the multi-source point cloud data; extracting a complete geometric line model and a section size of the steel structure from the complete point cloud model of the steel structure, and obtaining an actually measured material constitutive of the steel structure through a material property test; establishing a large-span space steel structure finite element model by adopting finite element analysis software based on the complete geometric line model, the section size and the actually measured material constitutive structure of the steel structure; and correcting the finite element model based on the residual error between the measured data of the steel structure and the simulated data of the steel structure output by the finite element model, and taking the corresponding finite element model when the residual error is less than a preset threshold value as the physical digital twinborn model of the large-span space steel structure. According to the method, millimeter-level geometric reduction, real material parameter association, lightweight efficient calculation and dynamic data correction are realized.

The present invention relates to an intelligent tree barrier detection method and system based on multimodal perception. The method uses sensors carried by drones to collect data, including RGB images, LIDAR point clouds, multispectral data, etc. First, the data is preprocessed to form a standardized input. Then, a Hough transform is performed on the LIDAR point cloud to extract the position of the power line and generate a three-dimensional line model. Then, a deep learning algorithm is used to locate the tree bounding box from the RGB image, and the high-risk point cloud area is screened in combination with the line model. Then, a three-dimensional tree model is constructed, and the minimum safe clearance distance between the power line and the tree is calculated in real time through a collision detection algorithm to obtain a three-dimensional tree model with a safety distance annotation. Finally, a spatial risk assessment is performed based on this model. The present invention can significantly improve the reliability and efficiency of power line inspections.

The application discloses a curved surface structure zero state form finding method, comprising the following steps: (1) a model is established based on a design configuration, and deformation data is obtained by simulating the whole construction process; (2) a zero state configuration is deduced reversely: key node deformation values are extracted, arches are started, a simulation is re-performed after a linear model is fitted and adjusted, and iteration correction is performed until deformation error meets the requirement, and a zero state installation configuration is obtained; and (3) construction data is generated by extracting component line shapes and node coordinates. The application innovatively adopts an integral curvature adjustment strategy to replace a traditional node coordinate local adjustment mode, and through reverse deduction of 'construction simulation-control point arching-curved surface fitting-grid mapping', the problem of curved surface discontinuity caused by the traditional reverse superposition method is solved, construction precision and efficiency are significantly improved, and the application is suitable for intelligent construction of complex curved surface space grid structures.

The invention provides a casting joint line polishing method based on vision, and belongs to the crossing field of mechanical manufacturing automation and intelligent manufacturing, and the casting joint line polishing method comprises the following steps: obtaining template surface point cloud of a standard casting, carrying out reverse modeling on the template surface point cloud to obtain first template point cloud, and obtaining first measurement point cloud of a casting to be polished; performing rough matching and fine matching on the first template point cloud in sequence, and converting the original joint line model into a new joint line model; points, closest to track points of the template track, in the new joint line model are obtained, for each closest point, the point, having the largest vertical distance from the plane where the new joint line model is located, in the neighborhood of the closest point is obtained, and all the points having the largest distances form a path planning point cloud; and traversing all planning points in the path planning point cloud according to the machining direction, selecting a machining mode according to the machining allowance corresponding to each planning point, and generating a final machining track. The grinding precision, the grinding quality and the grinding efficiency of the joint line can be improved, and the grinding device is also applicable to complex curved surfaces.

The invention relates to the technical field of railway line planning, in particular to an improved SAC railway line planning method based on course learning, and the method comprises the steps: modeling a three-dimensional line selection environment; constructing an improved SAC decision model, and designing a 100-dimensional state space including topographic features, forbidden zone positions and unfavorable geological risks; constructing a reward function fusing engineering cost, terrain fitness and invalid action penalty; curriculum learning staged training is carried out; and generating and optimizing a line scheme. According to the improved SAC railway line planning method based on course learning, a continuous action space decision framework based on the SAC algorithm can effectively solve the high-dimensional continuous action optimization problem of railway line selection. By designing a multi-dimensional fusion state space, a continuous action space based on a proportionality coefficient and a reward function based on target guidance, the adaptability of the intelligent line selection model to topographic features is remarkably improved.

The invention relates to the technical field of image recognition, in particular to a building plane element recognition model construction method based on a convolutional neural network, and the method comprises the following steps: obtaining an input APN sample; elements in the APN sample are labeled, and a labeled file is generated; converting the annotation file into a format required by YOLO, and normalizing the annotation file; performing data enhancement on the APN sample, expanding the training data volume, and outputting to obtain an element detection result and a feature vector; installing dependency and carrying out data configuration; setting command line model parameters and customizing training scripts; and carrying out model training and model reasoning. According to the method, the plane layout is abstracted based on the machine learning method, and the vector data type has high flexibility and deformation capability, so that the vector output which keeps the original plane image form unchanged can be converted into various objects according to the purpose of a user. And classifying and identifying the plane elements by using the GNN.

The invention belongs to the technical field of energy internet, and particularly relates to a regional energy internet toughness online evaluation method based on model-data hybrid driving. According to the method, firstly, a comprehensive energy network physical model is constructed, a toughness evaluation index system is established, the physical model covers energy nodes and line models, and energy activities are simulated through the law of conservation of energy and the like; and then carrying out real-time monitoring and risk early warning, collecting data by using an intelligent sensor, and predicting the risk in combination with machine learning. Then, based on physical model output and real-time data, a deep learning algorithm is adopted to evaluate network toughness, and a CNN-RNN model, adversarial training and Bayesian reasoning are fused to determine a weight; and finally, according to an evaluation result, applying a hybrid optimization algorithm and a reinforcement learning optimization strategy, and considering market factors to adjust energy operation. The advantages of the model and the data are integrated, the toughness evaluation accuracy, the risk early warning timeliness and the strategy optimization effectiveness are effectively improved, and the method is of great significance to energy internet development.

The invention discloses a line gradient design method with optimal energy saving as a target, and relates to the technical field of rail transit. According to the method, firstly, a train operation acceleration curve model, a corresponding traction force model and a braking force model are constructed, corresponding speed value, traction force and braking force data can be searched based on positions, secondly, on the premise that a speed curve is unique, the optimal line gradient design is optimized by taking the minimum traction energy consumption of an objective function as a target through a genetic algorithm, and the optimal line gradient design is obtained. And a certain reference can be provided for rail transit line design.

An online soft measurement method for dioxin emission concentration in MSWI process includes: performing principal component analysis on process data according to a historical process data set of the MSWI process to obtain a drift index control limit; constructing an offline model based on FTBL, and inputting the process data and historical DXN true value data of the MSWI into an off-line model; performing principal component analysis according to online data, judging whether the online data is drift data according to the drift index control limit; if the typical sample pool is the drift data, constructing an online model based on FTBL, and inputting the process data and the drift data of the typical sample pool and output data of the incremental layer of the offline model into the online model; and determining a DXN emission concentration predicted value according to an offline calculation result and an online calculation result.

The invention relates to a power distribution network grounding fault line selection method based on an image level, and the method comprises the following steps: obtaining historical waveform data of each line through power grid data, building a power distribution network fault feature system, and carrying out the training to obtain a grounding fault line selection model; acquiring real-time waveform data, uploading the real-time waveform data to the grounding fault line selection model, predicting a line state, and stopping power supply and positioning a line fault position when a line fault occurs; fault waveform data during line fault classification are analyzed by using a deep learning method, and the fault type of the line is obtained; and the line fault position and the fault type are sent to a power grid system, and the power grid system makes a fault decision for the line. The fault state of each line in the power distribution network system can be monitored in real time through the grounding fault line selection model, then the fault type of the line is obtained by using the deep learning method and the random forest classifier, and finally the purpose of making a fault decision on the line rapidly, fully automatically and accurately is achieved.

The invention discloses a business database natural language question and answer processing method fused with a large model technology. The method comprises the following steps: S1, receiving an emergency query request input by a user in a natural language form; and S2, carrying out analysis and intention recognition on the emergency query request based on a large language model, and generating a group of candidate query intentions arranged in a descending order according to matching degree scores and corresponding key entity information. According to the method, a core link can be optimized by introducing a large language model technology: in an intention recognition stage, intention recognition accuracy is improved through a formula of fusing semantic similarity and entity type matching degree; a priority-based asynchronous execution strategy is adopted to improve the response efficiency; the response reliability is guaranteed through real-time and conflict verification; meanwhile, the system performance is continuously improved in combination with an offline model optimization link, the defects of an existing processing method are finally overcome, and an accurate, efficient and reliable solution is provided for business database natural language question answering in an enterprise emergency scene.

The present invention discloses a parameterized three-dimensional modeling method of a spatial M-shaped node reinforcement based on a 3Dexperience platform, which belongs to the field of BIM modeling of building structures, including: creating a parameterized three-dimensional model template of arc beams and cylinders in the M-shaped node area; establishing a node skeleton line model according to the starting point coordinates of the cylinder and the arc beam; calling the template to generate a lod200 three-dimensional model of the M-shaped node; upgrading the lod200 model and adjusting the cylinder and arc beam reinforcement and end connection structural parameters to obtain a lod300 three-dimensional model of the M-shaped node; creating a longitudinal reinforcement positioning plate for the M-shaped node cylinder and arc beam; using MBD technology to perform three-dimensional annotation on the lod300 model and generate a reinforcement material table. The present invention improves the speed and accuracy of modeling reinforcement of a spatial M-shaped node; the reinforcement positioning plate can avoid rework and delay caused by the position deviation of the reinforcement, and the combined use of the model and three-dimensional annotation facilitates the understanding of complex node design and improves the efficiency of on-site construction.

The invention discloses an electric power inspection method and system based on big data and a random tree algorithm, and relates to the field of electric power system management and maintenance. Comprising the following steps: collecting key information, historical information and personnel basic data information of power equipment in real time, and processing the obtained key information; based on the processed key data, a plurality of factors are comprehensively considered, and an inspection line model is constructed; solving the inspection line model by using a fast search random tree algorithm; and outputting the optimized power inspection path, and carrying out data storage. According to the method, the information subsets of the electric power equipment and the inspection maintenance personnel object provided by the big data platform are introduced, multiple feasible paths subjected to coarse screening are further screened, the optimal electric power inspection path is generated through iteration for a certain number of times, the line scheme is output and stored, and intelligent optimization of the electric power inspection path is achieved.

The invention relates to the technical field of hot continuous rolling material performance prediction, and discloses an efficient hot continuous rolling material performance prediction method and system, and the method comprises the steps: achieving the multi-source data cleaning through multi-dimensional outlier detection and cross-rack space-time interpolation, building a material component graph model, capturing the interaction of elements, and predicting the performance of a hot continuous rolling material. And fusing physical characteristics and gene coding characteristics by adopting a space-time attention mechanism. Cross-production-line model parameters are integrated based on a federated learning framework, model robustness is optimized in combination with physical conservation constraint and adversarial training, and finally, optimal process parameters are inverted through a sequential quadratic programming algorithm and PLC closed-loop control is realized. By adopting a multi-dimensional outlier detection algorithm and a cross-rack space-time interpolation technology, the problems of poor synchronism and abnormal missing detection of a traditional data cleaning method in a hot continuous rolling multi-source heterogeneous data scene are solved, and data quality enhancement in a high-noise environment is realized by joint distribution statistics and physical law constraints.

The invention belongs to the technical field of ship engineering CAD (computer-aided design) three-dimensional modeling, and particularly relates to a ship molded line parameterization modeling method, a storage medium and a ship molded line parameterization modeling system.The ship molded line parameterization modeling method comprises the steps that point models in three-dimensional modeling are classified and differentially expressed; classifying the line model of the hull molded line, and controlling the geometrical shape of the line model through parameters; based on classification of the point model and the line model, a relevance control mechanism is established, and a three-dimensional line frame is generated through interpolation, fairing and synchronous optimization of the molded line. Based on point and line categories and different parameter relevance, in the overall design stage, molded line three-dimensional modeling is carried out whether the molded line is designed preliminarily or under the condition of existing molded values, and the three-dimensional line frame is generated. The method has the advantages that the molded lines can be quickly created and optimized, the molded lines related to the points can be synchronously modified by modifying and fairing the points or the lines, and preconditions are provided for follow-up construction of the fairing hull molded surface by the molded lines.

The invention relates to the technical field of bridge construction, in particular to a method for creating a bridge component based on Revit and Dynamo. The method comprises the following steps: S1, dividing bridge components into a point-based model and a line-based model, and respectively setting two automatic modeling programs; s2, establishing a parameterized family based on a point model, and loading the parameterized family into Revit; s3, reading the parameter data in the excel table; s4, using data to complete model size modification and automatic accurate positioning; s5, establishing a parameterized family based on a line model, and loading the parameterized family into Revit; s6, determining starting and ending point positions of the linear component; s7, determining a vertical offset distance; and S8, generating a model instance, and automatically modifying instance size parameters in batches. An excel table is read through Dynamo, parameters are automatically given to instance parameters in the model, data are used for driving parameterized model families, the model is automatically generated, and the model precision and the construction efficiency are remarkably improved. The method is mainly applied to the aspect of automatically creating the fabricated bridge member.

The invention discloses a cross-language migration-based low-resource programming language corpus enhancement method, which comprises the following steps of: receiving a source language code, and driving a large language model to generate an initial target language code through a two-way retrieval mechanism fusing example guidance and knowledge constraint; secondly, constructing an automatic iterative repair closed loop by utilizing the feedback of a compiler, and carrying out self-correction on codes which fail to be compiled; thirdly, high-quality codes are screened out through automatic quality gating and fed back to a corpus and a knowledge base, and self-enhancement circulation of data and knowledge is formed; finally, the method further comprises an offline model evolution step based on grammar and semantic alignment driven by a compiler, a big language model is trained by collecting preference data generated by an online process, and the ability of the big language model to understand a target language is improved fundamentally. According to the method, the core problems of low quality, poor efficiency and lack of self-evolution ability of a low-resource programming language in code migration are solved.

The present invention relates to the technical field of buildings, and in particular, to a visual inspection and measurement method for a reinforcing mesh, comprising the steps of: using an RGBD camera to perform data acquisition on a building site; performing Mask R-CNN instance segmentation on RGB, preliminarily identifying a workpiece, and segmenting a 2D workpiece from the background; converting the obtained 2D workpiece into a 3D point cloud on the basis of an intrinsic mapping relationship of the RGBD camera; and for on-site acquired data, obtaining a segmentation result of rebar details by means of FPFH feature matching with an offline model; and on the basis of an identified rebar point cloud, performing cylinder fitting to obtain the number of rebars within the field of view. According to the present invention, preliminary identification of rebars is performed on a 2D site image by means of Mask R-CNN instance segmentation, and then rebar 3D geometry is precisely identified by using FPFH feature consistency.

The invention discloses an intersecting line trajectory planning method with simple demonstration, which relates to the technical field of welding, and comprises the following steps: step 1, modeling an intersecting line: simplifying an intersecting line model based on a workpiece coordinate system, identifying radiuses of a main pipe and a branch pipe through demonstration points, meanwhile, the position and posture of the original point of the workpiece coordinate system in the world coordinate system are determined; step 2, demonstration of an intersecting line and generation of a track path: setting four modeling points on a main pipe; the teaching mode is very simple, a large number of point positions are not needed, the intersecting line track can be demonstrated through four points, the whole positioning process is simplified, operation is easy, time and labor are saved, the phenomenon that the teaching time is too long is avoided, production efficiency is improved, and production cost is reduced; the track mode generated by the algorithm is simple and reliable, frequent adjustment and optimization are avoided, it is ensured that the generated track is the intersecting line of the standard brick, the accuracy of the welding path is ensured, and the welding quality is improved.

The invention discloses a berthing state estimation method based on shipborne laser radar target detection, and the method comprises the steps: constructing a ship three-dimensional target detection network model through an improved PointPill coding network, and introducing an average pool layer and an attention pooling layer into the coding network to carry out the fusion of the global perception information and the local perception information of a target, and performing model training according to the constructed model loss function, detecting and confirming whether an anchored ship exists in a preset berth through an optimal detection network model, and respectively acquiring whether the anchored ship exists or does not exist in the preset berth by introducing an RANSAC plane fitting algorithm. A berthing speed used for ship berthing state estimation is obtained by combining the required berthing front edge plane or the outer contour surface of the target ship, namely a berthing berthing boundary surface and the position of the shipborne laser radar; the defect that the berthing state estimation accuracy is poor due to the fact that a berthing line model obtained through a traditional method based on linear fitting serves as the berthing state estimation reference is solved.

The invention relates to the technical field of intelligent auxiliary driving, and provides a 3D lane line generation method and device and a storage medium. After a lane line model is obtained, the abscissa of the vertex coordinate on the lane line model is modified according to a curvilinear function x = f (z), so that the lane line track generates transverse deformation; moreover, according to a curvilinear function y = f (z), longitudinal coordinates of vertex coordinates at the far end of the curved section track on the lane line model are modified, new vertex position coordinates are constructed, the curved section track is made to generate longitudinal deformation to form a YZ plane, and then target vertex coordinates are output to generate a 3D lane line, so that breakpoints at the far end when the lane line is bent are eliminated, the anti-aliasing effect is improved, and the accuracy of the lane line is improved. The phenomenon of breakage or flicker during lane line rendering is effectively avoided, the visual effect is improved, and the driving safety is improved.

The invention discloses a time-varying unit line method for river channel flat beach flow simulation. Comprising the following steps: S1, on the basis of measured data of daily flow and sand content in different hydrological years of a river channel calculation section, determining annual effective sand transport flow and sand content; s2, determining the flat beach flow based on the actual measurement water level-flow relation data of the cross section in the flood season of the riverway; s3, constructing an instantaneous unit line equation for river channel calculation section flat beach flow simulation; s4, constructing a time period unit line equation for river channel calculation section flat beach flow simulation; s5, constructing a time-varying unit line model for calculating the flat beach flow of the cross section of the riverway; and S6, model parameter time-varying analysis and simulation application. According to the method, the influence of water and sediment conditions in different time periods on the flat beach flow can be decomposed and reflected, the time-varying rule of model parameters under the water and sediment conditions in different time periods is considered, the calculation method is relatively simple, and a more scientific and reasonable method can be provided for simulation of the change process of the river channel flat beach flow.