3279results about "Configuration CAD" patented technology

The invention provides an equipment fault early warning method based on a neural network, a medium and equipment, and the method comprises the steps: collecting equipment state information at regular time, recognizing abnormal equipment, obtaining operation parameters and structure parameters of the abnormal equipment and a layout schematic diagram of a system where the abnormal equipment is located, constructing a dynamic coupling topology link, and extracting associated parameters; verifying and generating a first error weight in combination with a physical kinematics equation, generating a second error weight by matching historical fault data, and comprehensively generating fault state information; a detection mode is switched based on risk level evaluation, a system dynamic coupling topology link, associated parameters, abnormal equipment information and fault state information are input into a neural network model, fault link information is output, and finally first early warning information and second early warning information are generated according to a dynamic detection mode to realize two-stage early warning. According to the method, the fault conduction relation between equipment is modeled through the graph neural network, and fault path prediction and systematic risk early warning are realized.

A BIM-based prefabricated building design system and a simulated assembly method. The system comprises: a data collection module, a BIM modeling module, a drawing generation module, a component processing module, an assembly module, and a verification module; the data collection module is used for collecting data information of a prefabricated building; the BIM modeling module constructs a BIM model of the prefabricated building according to the data information; the drawing generation module is used for outputting a building component drawing according to the BIM model; the component machining module is used for performing machining and manufacturing according to the building component drawing; the assembly module is used for performing assembly path planning for a manufactured building component, and performing assembly on the basis of a planned path; and the inspection module is used for performing inspection on an assembled building. The present invention implements integration of prefabricated building design and implements prefabricated building installation intelligence, allowing assembly to be accurately completed.

The invention provides a frozen soil roadbed thaw collapse prediction method based on multi-source data and deep learning driving, and relates to the field of cold region engineering. The method comprises the steps that mathematical description is conducted on the thermal-mechanical coupling process of the frozen soil roadbed, and a frozen soil roadbed thermal-mechanical coupling physical model is obtained; the method comprises the following steps: acquiring frozen soil roadbed field monitoring data, remote sensing data, indoor test data and engineering environment data, and performing cleaning and normalization processing on multi-source data to obtain a multi-source data set for constructing and training a physical information neural network model; constructing a physical information neural network based on the frozen soil roadbed physical model and the multi-source data set; constructing a physical residual error of the physical information neural network model through a differential equation, and constructing a loss function based on a residual error item; and training the physical information neural network model to obtain a deep learning model for predicting thaw collapse deformation of the frozen soil roadbed. According to the method, the accuracy and reliability of thaw collapse deformation prediction of the frozen soil roadbed can be improved.

A digital documentation system for preparation of engineering documents utilizing one or more artificial intelligence (AI) algorithms is provided. The system includes a user interface for selecting and populating templates with data, and one or more AI algorithms for creating and recommending templates, and preparing documents based on the recommended templates. The system uses natural language processing and semantic analysis algorithms to understand the content of the templates, documents, and associated engineering data, and to generate and recommend relevant templates to the user based on user prompts. The system also uses machine learning and predictive modeling and decision-tree algorithms to assist with the preparation of documents, by generating suggestions for data fields and values based on the user's previous inputs and the overall context of the document and available engineering data, including model data and metadata from digital models accessed in a zero-trust framework.

The invention provides a wind measurement laser radar optical automatic calibration method and system based on reinforcement learning, and relates to the technical field of calibration, and the method comprises the steps: obtaining the initial state information and environment parameters of a wind measurement laser radar optical system, constructing a three-dimensional simulation model of the wind measurement laser radar optical system, performing a multi-field coupling simulation test on the three-dimensional simulation model to obtain wind measurement data and wind measurement error data under the current environmental condition, and constructing a mapping model of wind measurement errors and environmental parameters and wind measurement laser radar optical system state parameters; and inputting the wind measurement error data and the mapping model into a preset reinforcement learning model to obtain an optimal calibration parameter set in a current state, generating a calibration instruction of the wind measurement laser radar optical system according to the optimal calibration parameter set, and performing aberration correction by combining online wavefront detection and adaptive optical compensation to obtain a calibration result of the wind measurement laser radar optical system. And automatic calibration of the wind measurement laser radar optical system is completed.

The invention relates to a steel structure component virtual pre-assembly method based on three-dimensional laser scanning. Designing a self-adaptive hierarchical scanning strategy, and dynamically adjusting parameters of scanning equipment to obtain data of each region of the component; abnormal points are detected and eliminated by fusing scanning data of different levels; datum points are extracted, coordinate calibration is carried out, data of all levels are spliced, and a full-component three-dimensional point cloud model is formed; classifying the point clouds by using a multi-dimensional data analysis algorithm; establishing a closed-loop data verification system; designing a multi-feature point matching algorithm; performing iterative docking and position calibration through an algorithm; quantitative analysis is carried out on spatial deviation between the components in the assembling process; after the deviation is detected, a fine adjustment scheme is generated, and the position of the component is adjusted; a virtual pre-assembly path is generated according to the shapes, the connection modes and the installation sequence of the components, and the assembly sequence and the layout of spatial positions are controlled; and the assembly state of each component is monitored in real time, and the path is optimized and adjusted through a feedback mechanism.

The invention relates to the technical field of building design, and discloses a BIM (Building Information Modeling) creation method based on a parameterization technology, which comprises the following steps: obtaining building project parameters, setting parameterization rules, generating a parameterization model framework, optimizing multi-level parameters, generating an intelligent BIM, verifying and simulating a model and automatically generating a construction scheme. According to the BIM creation method based on the parameterization technology, through collection of multi-dimensional parameter information, intelligent rule optimization, a multi-target optimization algorithm, comprehensive simulation verification and automatic construction scheme generation, high customization, real-time environment adaptability, intelligence and efficient cooperation of building design are achieved. According to the method, the design efficiency and quality are improved, and the optimal performance of the building in the aspects of structure, material, energy efficiency, construction and the like is ensured. Through the application of the block chain technology, the system further guarantees the security and transparency of the data, and guarantees the traceability and consistency of the design process.

The invention belongs to the field of digital delivery, and particularly relates to a digital delivery topology mapping method and system for multi-source real-time data fusion, and the method comprises the steps: obtaining factory building distribution, equipment distribution and operation control logic and preset function block operation logic, and constructing a hierarchical clustering function mapping space through combining an association analysis and clustering algorithm; in response to a target function demand, obtaining a layered response mapping path in combination with a deep search algorithm; layered synchronous response and distributed node anomaly monitoring are realized based on the path, the three-dimensional simulation model and the display system equipment performance and the network state. Tracing abnormities based on a monitoring result in combination with a hidden Markov algorithm and a forward reasoning model, performing iterative verification after conflict resolution until the function is free of abnormities, and updating a mapping space; and adjusting the demand repeating steps to obtain a complete and updated mapping space, and realizing accurate function and picture collaboration under multi-source data fusion.

The invention discloses an inspection well overflow point forecasting and early warning method and system based on digital twinborn. The system comprises a city sensing module, a digital twinborn body module, an inspection well forecasting module and a flood risk early warning module. The method comprises the following steps: firstly, perfecting a city knowledge graph to construct a city perception module; constructing a digital twin city; constructing an inspection well forecasting module; and constructing a flood risk early warning module. Based on a digital twin platform, through an Internet of Things network and a data flow processing framework, an urban hydrological hydrodynamic model is coupled, parameters of the coupled model are dynamically calibrated, and an inspection well is finely simulated; the method comprehensively displays the multi-temporal-spatial-scale urban development state flood inundation condition, tracks and reflects the actual operation state of an urban drainage system, dynamically evaluates the risk level and carries out real-time early warning, improves the urban emergency management response efficiency, and provides support for smooth development of urban flood control and drainage work.

The invention relates to the technical field of bridge digital twinborn platform construction, and particularly discloses a Unity 3D bridge digital twinborn platform construction method integrated with a Midaas-Civil calculation function, and the method carries out the real-time monitoring and evaluation of a bridge through a digital twinborn technology, thereby improving the structural safety and maintenance efficiency of the bridge. The method comprises the following steps: acquiring stress distribution data of a bridge in real time through bridge monitoring equipment, and analyzing a stress concentration area in a digital twin platform; a crack propagation rate and a stress gradient change value are extracted, a comprehensive feature vector is constructed, the current state of the bridge is evaluated by using a machine learning model, and a high-risk area is identified according to an evaluation result; the maintenance strategy of the bridge is dynamically optimized by predicting the future crack expansion trend and stress distribution change of the low-risk area; and on the basis of a risk assessment result, a resource optimization allocation model is constructed, optimal allocation and dynamic adjustment of bridge maintenance resources are realized, and accurate implementation of a maintenance strategy is ensured.

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

A prefabricated building design and construction integrated collaborative method based on component parameter library, which belongs to a field of prefabricated building design and construction technology, comprising: constructing a BIM parameter system for a prefabricated building and its prefabricated components, and establishing a component parameter library of the prefabricated components of the prefabricated building based on the BIM parameter system. By forming the component parameter library through the BIM parameter system of the prefabricated building and its prefabricated components, architects are provided with accurate information that meets the subsequent construction requirements and the construction requirements of various disciplines in schematic design and detailed design, and guided to apply BIM and select appropriate prefabricated components for design, so as to increase depth of first design, and improve accuracy for design optimization. Therefore, prefabricated building design and construction integrated collaboration is promoted.

A computer-implemented method for designing a 3D modeled object representing a product to be manufactured, the designing method including, by a computer system, displaying simultaneously a 3D shape representation of the 3D modeled object, and a 2D block representation of the 3D modeled object. The method also comprises by a user interacting graphically with the 2D block representation, performing a selection of one or more block nodes among the at least one block node, upon user-action, adding to the 2D block representation a block node representing a respective operator of the subset, and automatically determining a respective arc between the output connector of each selected block node and a respective input connector of the added block node. The disclosure improves ergonomics.

The invention provides a prefabricated pipe pile construction total factor intelligent management method and system based on the digital twinborn technology, and relates to the technical field of construction management, a three-dimensional model is established through the BIM technology, pipe pile attributes are bound, construction data are collected in real time through the Internet of Things, and a dynamic digital twinborn body is formed. Through integration of an API interface and a project management platform, an AI algorithm is used for analyzing data, resource allocation and construction plans are dynamically adjusted, instructions are optimized and pushed to the site, construction efficiency and installation precision are improved, and the problems that traditional management is low in efficiency and large in error are solved.

The invention discloses a chip layout method and system based on reinforcement learning and back-end optimization. The method comprises the following steps: constructing a layout optimization model; generating a corresponding position mask, a line mask and a view mask; fusing the position mask and the line mask to generate a local feature, and fusing the line mask and the view mask to generate a global feature; performing feature fusion on the local features and the global features to generate comprehensive features; according to the embedded vectors of the comprehensive features and the global features, generating the probability of various layout optimization actions which should be adopted, and calculating the excitation corresponding to the state after each round of action is updated and the value of the excitation; performing parameter updating according to newly stored experience; training the layout optimization model through the original data set; and eliminating the layout overlapping part to obtain a final layout map. According to the invention, a network can capture a complex mode in chip layout in a deeper level, the layout efficiency of the chip is improved, a more effective layout scheme can be explored, and the exploration efficiency is improved.

The invention discloses a general lens intelligent core adjustment method and system based on a lens library and optical simulation, and belongs to the field of optical lens core adjustment. Adding different simulation lenses into a simulation light path system, taking a clear extended image as input in each view field, calculating each view field point spread function matrix of the simulation lenses under different misalignment amounts, and taking the misalignment amounts as labels; obtaining an illuminance distribution matrix and a distortion distribution matrix corresponding to each misalignment amount at the same time; a series of ideal degraded images under different misalignment amounts are calculated, and simulation degraded images are obtained through optimization of the illumination distribution matrix and the distortion distribution matrix; the simulation degraded image and the misalignment label are used for training a universal lens intelligent core adjustment model, and intelligent core adjustment of lenses of the same optical structure can be achieved after fine adjustment. According to the method, aberration characteristic priori of a large number of simulation lenses is utilized to pre-train the core adjustment model, generalization of the model to different lenses is improved, and the purpose of performing quick core adjustment on any lens by only using one model is achieved.

The invention discloses a method for generating a fitting catenary through point cloud data. The method is used for designing or maintaining a target electric power facility. The method comprises the following steps: acquiring point cloud data of a target electric power facility through sensing equipment; preprocessing the collected data, including denoising, segmentation and coordinate transformation, so as to extract key point data related to the catenary; based on the preprocessed key point data, establishing a catenary mathematical model containing gravity, tension and catenary length parameters; solving the parameters of the mathematical model by using an optimization algorithm, and minimizing the deviation between the model and the key point data; and finally, according to a fitting calculation result, generating a geometrical shape and parameterized description of the catenary. According to the method, point cloud data processing and catenary modeling technologies are combined, the catenary characteristics in the electric power facility are efficiently and accurately fitted, and the method is suitable for engineering requirements in catenary form analysis and electric power facility design and maintenance.

The embodiment of the invention provides a wind and light energy prediction method and device and electronic equipment, and relates to the field of wind and light energy prediction. The wind and light energy prediction method comprises the following steps: acquiring multi-source observation data; performing time and spatial resolution resampling on the obtained multi-source observation data; a wind field solving module and a radiation transmission module are integrated in the numerical weather forecasting mode; a turbulence closed equation set is embedded in the wind field solving module; enabling an aerosol correction factor in the radiation transfer module; in combination with observation data in a historical period, parameter tuning is carried out on the formed coupling system; different surface thermal field disturbance schemes are set in the target area, and multiple groups of wind field and solar radiation field forecasts are generated; according to the method, the problems of insufficient coupling of the physical process and the chemical process of the atmospheric boundary layer, lack of dynamic feedback of meteorological and pollutant disturbance, insufficient high-temporal-spatial-resolution prediction and the like in the prior art are effectively solved.

A computerized method for generating a 3D Computer-Aided Design (CAD) is provided. The method comprising: receiving an input comprising requirements for a new design; processing the input to generate a CAD design using a trained AI model, wherein the CAD design is an assembly of at least two separate components; and outputting the CAD design.

The invention provides a park energy monitoring method and system based on the Internet of Things, and relates to the technical field of energy monitoring of the Internet of Things technology, and the system comprises a path planning module, a VR / AR interface module, a visual image module, an emotion calculation module, and a block chain module. The monitoring method comprises the steps of energy distribution path planning, VR / AR interaction monitoring, visual image auxiliary analysis, emotion calculation optimization distribution, block chain guarantee data security and the like, the problems of distribution, monitoring, analysis, distribution, data security and the like in park energy management are effectively solved, and the park energy management intelligent level and the energy utilization efficiency are improved.

The invention belongs to the field of material performance testing of mechanical structure strength, and particularly relates to an ultrasonic fatigue experiment monitoring method based on digital twinning. According to the method, on the basis of an ultrasonic fatigue testing machine platform, an ultrasonic fatigue digital twin model of a tested object is established by collecting frequency, temperature, load and other test data, and real-time online monitoring of ultrasonic fatigue and life prediction are carried out in combination with a fatigue life prediction model. Compared with a traditional test method, the method has the advantages that ultrasonic detection data and a digital twinborn model technology are combined, the fatigue life of the material is predicted and evaluated, the test efficiency is improved, the long-life test cost is reduced, and the accuracy and reliability of test data are improved.

The invention discloses a foundation pit high-performance mobilization strength design method and system. The method comprises the steps that S1, wall deformation is decomposed into deformation mechanism superposition of different shapes; s2, calculating soil body strains corresponding to different shape deformation mechanisms; s3, superposing the soil body strain corresponding to different shape deformation mechanisms, and determining the total shear strain of the soil body; s4, calculating soil mass mobilization shear stress according to an assumed soil pressure mobilization mechanism; s5, calculating horizontal soil pressure according to the soil mass mobilization shear stress; and S6, numerical integration is conducted on the horizontal soil pressure twice, bending moment caused by the supporting load is added, and the structural bending moment applied to the wall is obtained. The technical problem that in the prior art, errors are caused to foundation pit deformation design due to the fact that soil mobilization strength is ignored is solved, and the technical effects that the soil mobilization strength is considered, and wall deformation and stress can be rapidly calculated only through a simple constitutive model are achieved.

Systems and methods for generating three-dimensional (3D) models based on images are disclosed herein. An example computing system can generate, using a trained machine learning model and based on at least one image, a vectorized object corresponding with the object and a parametric list that can include a sequence of predefined operations and associated parameters to construct a corresponding computer-aided design (CAD) object. The parametric list can be employed, and is modifiable, in a CAD software or CAD workflow to construct a 3D model of the CAD object.

The invention discloses a meta-model-based technical index construction and management method and system. The method comprises the steps of defining index metadata according to standard specifications, project experience and historical data; according to product design requirements, constructing a structured index template library; quoting an index template library, defining indexes and association relationships between the indexes and a demand model, a SysML model, a geometric model and the like, and forming an index library; calling an index suite, and applying the index set to form a plurality of sets of index parameter solutions; calculating and analyzing the index parameters of different solutions so as to realize preliminary balancing of the solutions; according to the changed index parameters, the associated index parameters are identified, and change prompting is carried out; performing tracking, detection and early warning on the index parameters through calculation and analysis of the index parameters; setting an index verification task according to the product research and development process, and managing the execution condition and result of the verification task. According to the technical scheme provided by the invention, full-life-cycle management of the product indexes can be realized.

The present application relates to the technical field of chip verification, and relates to a software and hardware collaborative simulation verification platform for accelerating verification and positioning of a smart network interface card chip. The platform comprises: a host side QEMU virtual machine, which simulates a hardware device, sends an IO request, responds to the IO request, and calls a device driving program to implement driving execution of a host side hardware device; a host side thread adapter, which converts a multi-thread processing program of the host side QEMU virtual machine into a single-thread processing program; an UVM verification platform, which performs simulation reproduction of FPGA prototype verification of the smart network interface card chip to generate a simulation waveform and a simulation log; a network side data stream adapter, which performs data type conversion on an Ethernet message; and a network side data packet transceiver, which performs message legitimacy check on the Ethernet message returned by the network side data stream adapter. The platform of the present application can improve the problem positioning speed and the chip verification efficiency, shorten the overall chip development period, and improve the chip quality.

The invention provides an interactive BIM modeling method and system based on a large model. The method comprises the steps that a target tool set description document corresponding to a target project class is determined; receiving a task instruction input by a user, and performing task splitting by the project class large model according to a task intention of a currently executed task to obtain at least one subtask; the calling project class large model determines a tool calling instruction according to the task intention of the currently executed subtask and the target tool set description document; the tool calling instruction is sent to a tool calling module, and the tool calling module carries out compliance verification on the tool calling instruction; the tool calling instruction is processed, an execution instruction of the BIM application is generated, an execution result is received and returned to the project class large model, and the project class large model presents the execution result to a user. According to the method disclosed by the invention, the BIM modeling task is completed through simple natural language input, the operation complexity can be reduced, and a designer is helped to complete the modeling task more efficiently.

The invention discloses a rapid construction method for a standard knowledge graph of railway bridge design. The method comprises the steps that S1, data are collected and preprocessed; s2, a BERT pre-training model and a Bi-LSTM model are adopted to convert a text sequence in the processed data into a labeled tag sequence {y1, y2... yn}; s3, identifying and labeling entities, attributes and relationships thereof by using a conditional random field (CRF) and a graph neural network (GNN) technology, forming nodes and edges of the knowledge graph, and obtaining a complete knowledge graph (KG); and S4, adjusting the complete knowledge graph KG structure in real time by using a dynamic topological optimization algorithm. According to the method, the standard data is rapidly integrated and optimally designed, data redundancy is reduced, and newly added and changed data are effectively processed, so that the construction speed and the maintenance efficiency of the knowledge graph are improved.

Digital model platform that enables the streamlined creation and management of digital twins and physical twins by leveraging external feedback and artificial intelligence (AI) is disclosed. Methods and systems for a model collaboration platform that facilitate the integration of external feedback, whether from physical, virtual, or human sources, into the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Updated results are output to those external sources, including physical twins, other digital twins or models and simulations, and / or human users. External feedback includes feedback data from physical prototypes and their environment, virtual prototypes, simulations, and subject-matter experts. Embodiments are directed to integrating external feedback into the assembly and system-level assessment of digital twins, physical twins, digital threads, and the iterative design of constituting digital engineering models, including constructing, maintaining, and improving digital engineering models for the design, validation, verification, certification, operations, and maintenance of complex systems.

The invention discloses a prefabricated building component quality monitoring method, and relates to the technical field of buildings, and the method comprises the steps: interactively obtaining target building design data; obtaining a target building model; calling networking data according to the target building position to obtain multiple pieces of load environment information; obtaining a plurality of load condition models; m building areas identified by adopting the stage assembly task sequence are obtained; m component mass constraint libraries are obtained; and dynamically calling the M component quality constraint libraries according to the stage assembly task sequence to carry out quality monitoring in the production process of the assembly type building component. The technical problems that in existing building component quality monitoring, real-time monitoring is difficult to conduct at all assembly nodes, the quality problem is difficult to find in time, environment changes cannot be dynamically adapted, and then the real-time performance and accuracy of the result of building component quality monitoring are insufficient, and the environment adaptability is poor are solved. The technical effect of improving the accuracy, timeliness and construction flexibility of building component quality monitoring is achieved.

The invention discloses a lightning arrester temperature compensation method based on a DSA-CTCM network. The lightning arrester temperature compensation method comprises the following steps: acquiring historical environment data of a lightning arrester; performing data preprocessing on the historical environment data of the lightning arrester; the method comprises the following steps: constructing and training to obtain a temperature compensation model through historical environment data after data preprocessing based on a dual-stage self-attention and MambaNet time sequence convolutional network; inputting real-time environment data of the lightning arrester into the temperature compensation model to obtain predicted temperature data, and calculating a temperature deviation value between the real-time temperature data and the predicted temperature data; a dynamic feedback mechanism is established, a compensation coefficient of the temperature compensation model is dynamically generated according to the temperature deviation value, the compensation coefficient is fed back to the temperature compensation model so as to adjust parameters of the temperature compensation model, and performance evaluation is conducted on the temperature compensation model; according to the lightning arrester temperature compensation method based on the construction of the DSA-CTCM network, environmental data of different time steps can be accurately identified and weighted, and the accuracy of temperature compensation is improved.