2023 results about "Simulation based" patented technology

The invention discloses an infrastructure risk operation optimization management system based on AI driving, and belongs to the field of computer data processing and commercial management, and the system comprises a multi-modal causal twinning construction module which integrates on-site multi-modal data streams to construct a dynamic space-time causal map; the risk evolution deduction module is used for performing anti-fact simulation based on a causal atlas to construct a prospective risk model; the collaborative configuration optimization module is used for solving an optimal collaborative defense strategy according to the risk model; the instruction analysis and digital prescription generation module is used for analyzing the defense strategy into a job digital prescription for a specific risk scene; and the intervention efficiency attribution and evolution correction module performs attribution analysis according to the execution effect of the digital prescription and adaptively updates the causal atlas. According to the method, a comprehensive method of constructing a dynamic causal map for risk deduction, coupling resource constraints for collaborative optimization and performing closed-loop feedback on a correction model is adopted, and active prediction, accurate intervention and continuous learning optimization of capital construction risks can be realized.

The invention provides a construction method of a double-path collaborative decision network for multi-agent collaborative path optimization, and the method comprises the steps: obtaining multi-source heterogeneous real-time business data and historical business data, and constructing a standardized data set; converting the standardized data set into a dynamic environment input sequence; at least two domain agents perform full-process business logical reasoning based on the dynamic environment input sequence to generate a decision chain structure covering the full life cycle of the business; implementing full-flow path planning simulation based on the decision chain structure, and generating an initial path scheme including multi-resource collaborative scheduling; the environment perception intelligent agent puts the initial distribution path scheme into an actual logistics distribution scene for execution, and loads real-time environment change information to determine a logistics decision chain weight; and the logistics strategy agent optimizes the initial distribution path scheme based on the logistics decision chain weight to obtain a logistics business knowledge graph so as to construct a logistics double-path collaborative decision network.

The invention relates to the technical field of engineering management and control, in particular to an electric power engineering construction management and control method and system based on digital twinning. The method comprises the following steps: acquiring heterogeneous sensing data of a construction area, and carrying out dynamic modeling on a structural member to obtain a member-level digital twin grid; performing environmental factor-material performance response coupling based on the component-level digital twin grid to obtain a material environmental response distribution diagram; component environment sensitivity dynamic injection is carried out according to the component-level digital twin grids, and a construction process dynamic map is obtained; performing multi-mode construction state field deduction based on the construction process dynamic map to obtain a construction state evolution distribution field; and performing reversible process scheduling optimization and simulation according to the construction state evolution distribution map to obtain a reversible scheduling optimization map. The efficiency of electric power engineering construction and the resource utilization rate can be improved.

The invention relates to a campus Internet of Things terminal scheduling method based on digital twinning. The method comprises the following steps: defining a general twinning state representation structure; introducing a state mapping relationship, and establishing a one-to-many dynamic mapping relationship between a physical state and a virtual twinning state by using a finite state machine; when the state of the physical terminal changes, triggering multi-level response mapping of the twin in the virtual space; semantic fusion is carried out on twinborn real-time data, a scheduling sensitivity index is introduced, disturbance simulation is carried out based on past scheduling behaviors, and the potential influence of a certain scheduling behavior on the whole system is analyzed; carrying out local division on the situation map; using Bayesian estimation and Monte Carlo combination to sample and evaluate the feasibility and income of each solution set in a period of time in the future; simulating the expected influence of each scheduling strategy through a twin body, and selecting a scheduling path; unified modeling and semantic fusion of multiple types of terminals are realized, the intelligent level and predictive ability of scheduling decision are improved, the collaborative scheduling ability of the terminals is enhanced, and coupling unit level regulation is realized.

A stability assessment method of roadway surrounding rock includes: obtaining actual stratum rock parameters; establishing a two-dimensional geological model through numerical simulation based on a drill core columnar diagram and the actual stratum rock parameters; changing influencing factors, and recording amounts and acceleration values of deformation of roadway sidewalls, and whether failure occurs to obtain dynamic response characteristics of roadway surrounding rock, combining the changed influencing factors and the dynamic response characteristics as labels to obtain a dataset, and obtaining multiple datasets including the dataset; dividing the multiple datasets into a training set and a validation set, inputting the training set into a PSO-BP neural network and a GA-SVM deep learning model to obtain a preliminary stability assessment model, and adjusting and validating the preliminary stability assessment model by the validation set to obtain an optimized stability assessment model; and using the optimized stability assessment model to assess roadway stability.

The invention relates to the technical field of intelligent laser processing path planning, and discloses a laser flight processing path planning method and device for a complex curved surface, and the method comprises the steps: obtaining the geometric continuity features of the curved surface, carrying out the region division and boundary feature extraction, obtaining the geometric features of the boundary points of each region, and obtaining the geometric features of the boundary points of each region; performing track generation based on boundary geometric features to obtain an initial processing track path, then performing heat accumulation analysis and track screening, identifying a track section needing to be adjusted, executing multi-target path scheduling optimization, and obtaining adjusted track execution feature data; processing sequence optimization is carried out based on the feature data, and an optimized processing sequence is obtained; performing thermal input analysis and parameter correction according to the processing sequence to obtain an updated processing parameter set; and finally, performing global coordination based on a simulated annealing algorithm, generating a final processing track set, completing track optimization and instruction set generation, and outputting a complete processing scheme. According to the method, the problem of insufficient track fitting degree can be solved.

The invention relates to the technical field of engineering construction management and control, in particular to an engineering construction management and control method and system based on digital twinning. The method comprises the following steps: acquiring real-time construction engineering data and regional environment data, identifying a real-time structured construction state and generating a three-dimensional simulation model, then acquiring a target construction engineering plan and performing process node positioning to calculate a real-time engineering progress deviation, performing compensation simulation based on the deviation, and determining an engineering construction development trend. And then environment constraint mapping and resource conflict analysis are performed to generate corresponding data, and an optimized construction path conforming to environment constraint and resource conditions is screened out through construction optimization path mining of the simulation model, so that the construction efficiency and the management level are improved. According to the invention, the competitiveness and sustainable development capability of the building industry are enhanced, and an efficient and transparent engineering management system is finally formed.

The invention discloses an intelligent dynamic tracking and early warning method and system for carbon emission, and relates to the technical field of carbon emission management.The method comprises the steps that a digital twinborn model of a target area is constructed, grid division is conducted based on monitoring precision, and a first layer of fixed monitoring points are arranged; planning a mobile monitoring path in combination with a carbon emission attention fitting result, and configuring a second layer of mobile monitoring points; collecting data through two layers of monitoring points, and establishing a carbon emission data network; performing airflow simulation based on a digital twinborn model, performing traceability inversion in combination with a data network, and positioning an emission source; and finally, equipment-level positioning and carbon emission tracking early warning are realized through equipment matching analysis. The technical problems that in the prior art, the carbon emission monitoring coverage range is limited, the traceability is insufficient, and accurate positioning and real-time early warning of the carbon emission source are difficult to achieve are solved, and the technical effects of high-precision traceability positioning and real-time traceability early warning of the carbon emission source are achieved.

The invention relates to the technical field of distributed simulation based on containerized deployment and elastic expansion and contraction, and discloses a distributed simulation method and system based on containerized deployment and elastic expansion and contraction. According to the distributed simulation method and system based on containerized deployment and elastic expansion, real-time resource monitoring and historical load trend data of a simulation task are collected, and unified load evaluation is carried out in combination with a simulation calculation complexity parameter and an I / O density parameter; the refined modeling and resource demand pre-judgment of the simulation task are realized, and the accuracy of task scheduling and allocation is improved; modularized deployment of simulation tasks is achieved through subtask segmentation based on minimum executable units and a standard containerization packaging mechanism in cooperation with a container arrangement platform, and then concurrent execution and elastic scheduling in a multi-node environment are supported.

The invention discloses a UE4-based digital twin model construction system and method, and relates to the technical field of computer vision, and the system comprises a geometric modeling unit which generates three-dimensional model data based on geometric modeling data of a target physical entity, and achieves dynamic LOD rendering level switching through virtualization processing; the real-time data interaction unit is used for analyzing the real-time sensor data flow and generating equipment state data in combination with a transmission operation and maintenance instruction; the simulation engine unit is used for executing rigid body motion and collision simulation based on the equipment state data and outputting a collision event sequence and state transition data by utilizing an event trigger; the machine learning unit is used for training an LSTM (Long Short Term Memory) model based on the collision event sequence and the state transition data, and optimizing state covariance parameters through adaptive Kalman filtering; and the visual interaction unit is used for realizing data synchronization of the command large screen and the mobile terminal through multi-screen collaboration. According to the method, the digital model capable of accurately reflecting physical entity behaviors and characteristics is constructed.

The invention relates to the technical field of mechanical engineering, in particular to a three-dimensional simulation design method and system based on an elevator structure. The method comprises the following steps that an elevator structure drawing is obtained; a three-dimensional elevator assembly is constructed according to the elevator structure drawing; lift car static load simulation is conducted according to the three-dimensional elevator assembly, and lift car static load data are obtained; detecting the structural strength of the lift car based on the lift car static load data; evaluating welding defects based on the structural strength of the lift car, and generating welding defect data; identifying a weld weak area according to the welding defect data; determining the abrasion loss of the car structure based on the weld weak area; according to the lift car structure abrasion loss, the lift car bottom plate fracture risk is predicted; carrying out welding optimization design based on the fracture risk of the bottom plate of the lift car to obtain welding optimization design data; and traction and traction simulation is conducted according to the three-dimensional elevator assembly, and traction and traction data are obtained. Based on the mechanical engineering technology, the safety and stability of the elevator structure are improved, and the design precision and efficiency are remarkably improved.

The invention discloses a power equipment defect detection method based on image recognition, and belongs to the technical field of power equipment defect detection, and the method comprises the steps: carrying out the defect simulation based on physical mechanism driving according to an equipment three-dimensional model and physical field simulation parameters, and obtaining a defect simulation data set; according to the defect simulation data set and the real inspection data, training a cross-modal deep learning network based on physical law constraint to obtain a defect identification model; performing time-space diagram neural network modeling according to the historical time sequence inspection data and the defect identification model to obtain a state evolution model; and inputting inspection data acquired in real time into the equipment health state evolution model, and performing online reasoning to obtain a defect detection result. The problems that an existing electrical equipment defect detection method excessively depends on scarce real defect samples, the generalization ability for complex working conditions is weak, and the defect evolution trend prediction ability is lacked are solved.

The invention relates to a ship automatic berthing safety control system, and belongs to the technical field of port ship operation safety control. The system comprises four core modules which are sequentially connected to form closed-loop control: a situation synchronous sensing module generates total-factor dynamic attitude information through space-time synchronization and data fusion of a ship-shore inertial unit; the digital twin deduction module performs real-time simulation based on a ship six-degree-of-freedom motion model, and predicts a ship motion track and berth acting force in a prospective manner; the safety decision and early warning module compares the prediction track with the safe berthing corridor through a track-berthing force coupling algorithm, and outputs a cooperative control strategy; and the multi-terminal cooperative execution module converts the strategy into an instruction and synchronously distributes the instruction to a tug, a navigation bridge and a wharf system. Automatic closed-loop control of the whole process from sensing, prediction, decision making to execution is achieved, and the safety, accuracy and operation efficiency of ship berthing are effectively improved.

The invention discloses a vehicle-road cloud cooperative traffic abnormity early warning method, which comprises the following steps that: a road side unit receives abnormal event data reported by a vehicle side, checks the abnormal event data in combination with road side data, and uploads the abnormal event data to a cloud side; the cloud receives the data reported by the road side unit, performs fusion in combination with the cloud data, and evaluates the risk level after the occurrence of the abnormal event: constructs a space-time diagram neural network, and calculates the vulnerability index of the road network based on the traffic road network diagram structure; constructing a Bayesian network, updating probability distribution of the network by using detected data corresponding to the abnormal event as an observation value, and predicting a conduction path of the abnormal event; carrying out preliminary risk grade assessment according to the vulnerability index and the predicted conduction path; and performing traffic intervention simulation based on the abnormal event, calculating an intervention index improvement rate according to a simulation result, and correcting a risk level. According to the method, deep fusion is carried out on the vehicle and road cloud data, the traffic abnormal event identification capability is improved, and the dynamic risk level evaluation of the whole road network is realized.

The invention relates to the technical field of pipeline safety detection, in particular to a pipeline hydrogen-doped leakage real-time monitoring and early warning method and system based on Fluent simulation, and the method comprises the steps: obtaining leakage diffusion data of gas in a pipeline detected by multiple sensors, and carrying out the preprocessing of the data; extracting data features: carrying out conjoint analysis on a time-frequency domain to obtain a time-frequency matrix; by calculating mutual information evaluation correlation of different sensor data, feature statistical correlation is realized, and features with non-significant spatial correlation are removed; the pressure data, the flow data and the concentration data are subjected to sub-channel filtering and fusion processing based on federated Kalman filtering (FKF); establishing a pipeline model based on Fluent, and simulating a pipeline hydrogen-doped leakage process by taking the extracted features as input conditions; a risk model is constructed based on real-time sensor data, simulation prediction data and environmental parameters, and a hierarchical early warning mechanism is established. Through cooperative monitoring of multiple sensors, leakage characteristic data can be accurately captured, and safety and reliability of the system are ensured.

The invention relates to the technical field of wind energy resource evaluation, in particular to a method and a system for calculating a hydrodynamics wind field numerical value based on high-precision landform, and the method comprises the steps: generating a three-dimensional unstructured grid for CFD wind field simulation based on high-precision landform data; analyzing an atmospheric boundary layer structure, and establishing a turbulence calculation model; according to the turbulence calculation model, the influence of atmospheric stability on the mountain flowing wind acceleration factor is researched, and a mountain flowing model is developed; performing CFD wind field simulation according to the three-dimensional unstructured grid and the mountain flow model, and correcting parameters of the mountain flow model; a refined grid is made through terrain elevation data; and according to the refined grid, driving the corrected mountain flow model to carry out numerical simulation, and carrying out wind power plant wind resource evaluation. Through the method, the complex characteristics of the wind field can be reflected more comprehensively, the precision and the calculation efficiency of wind resource evaluation are remarkably improved, and the efficiency and the accuracy of numerical simulation of the complex terrain wind field are improved.

The invention discloses a chalcogenide phase change storage material potential model construction method, a material characteristic calculation method and electronic equipment, and belongs to the field of semiconductor materials. On the basis of a potential model trained by a first training set collected through molecular dynamics simulation based on a first principle calculation method, a current potential model is fully utilized, molecular dynamics simulation is carried out based on the current potential model, training samples are further collected, representative training samples are screened out, and a second training set is formed; according to the method, the training efficiency is high, the generalization ability is high, and a large amount of training data can be obtained with low cost. Meanwhile, verification of physical properties is introduced into convergence conditions of model training, so that the physical consistency and interpretability of the potential model are improved; on the basis, the training set which is good in diversity and sufficient in data volume can be obtained with low cost, and meanwhile the accuracy of the potential model in an actual application scene is further improved.

The invention relates to the technical field of financial knowledge maps, in particular to a large language model-based data structure adaptive visualization method, which comprises the following steps of: acquiring internal quantitative data and external unstructured text data of an investment portfolio; processing the quantized data and the text data by using a large language model, constructing a dynamic financial knowledge graph, and updating a risk conduction weight between entities in the dynamic knowledge graph in real time by using a causal event extracted from the text data; receiving a risk impact hypothesis input by a user, and performing prospective simulation based on the dynamic financial knowledge graph and the risk impact hypothesis to generate a plurality of synthetic risk data paths; generating a visual result based on the plurality of synthetic risk data paths; and in response to the selection of the user on the specific simulation result on the visual result, executing anti-fact analysis, and generating an attribution report containing the contribution degree, so that the interactive risk simulation and automatic attribution analysis of the visual result are realized.

The invention relates to the technical field of virtual reality, in particular to a centimeter-level VR real-scene real-time synchronous rendering method for multi-source data fusion and physical field coupling. Comprising the following steps: eliminating cloud layer interference and expanding Kalman filtering to improve positioning precision through multi-source acquisition of satellite remote sensing data, meteorological data and an equipment sensor; dynamically adjusting process noise by using Kalman filtering, and complementing delay data; constructing a ray tracing and U-Net noise reduction model, carrying out hierarchical loading of textures in combination with dynamic texture management, modeling dynamic illumination and wind field simulation based on a solar declination angle, and realizing scene rendering; a virtual ground is aligned through WebXR, gesture recognition and a force feedback mechanism are integrated, and fingertip curvature and movement speed features are extracted in combination with a Leap Motion sensor. According to the invention, through multi-source data fusion, efficient rendering and accurate interactive design, the authenticity and real-time synchronization capability of the VR scene are significantly improved.

The invention discloses a mine rescue training and danger dynamic simulation method based on virtual reality, and the method comprises the following steps: S1, modeling a rescue process into a directed graph structure, and outputting a task state diagram bound with a virtual reality scene; s2, collecting an operation behavior, a task state, an environmental parameter and a physiological response, and constructing a training state vector; s3, inputting the state vector into the deep Q network to calculate a jump node evaluation value, and outputting a jump node; s4, modeling the control rule as a rule node, constructing a directed acyclic graph, binding a weight, and outputting a rule graph; s5, performing self-evolution on the rule atlas according to training feedback, and outputting an evolution structure; s6, reasoning in the evolution rule map, fusing the rule recommendation node and the jump node, and outputting a final task node; and S7, executing jump control according to the final node, loading a virtual scene, and pushing environment parameters, task contents and risk information. According to the invention, intelligent path decision and training process adaptive optimization of the mine rescue task are realized.

The invention discloses a near-slope foundation bearing capacity estimation method and system considering slope body stability, and the method comprises the steps: collecting geological data and environmental data of a target region, constructing a target region simulation model, and carrying out the simulation obtaining of the slope body parameter changes of the target region under different environments and load effects; determining a potential sliding surface based on the simulated slope parameter change, a preset sliding surface determination mode and a preset slope stability coefficient calculation method, and calculating a slope stability coefficient of the target area; determining a geological risk level of the target area, and when the geological risk level is greater than a preset risk level, selectively collecting a slope surface image of the target area, and calculating a soil parameter adjustment value; and correcting the foundation bearing capacity formula by using the slope stability coefficient or the slope stability coefficient recalculated according to the soil parameter adjustment value, and outputting an estimated value of the foundation bearing capacity. According to the method, the slope stability can be considered under the complex terrain condition, and the bearing capacity estimated value of the near-slope foundation can be obtained more accurately.

The invention relates to the technical field of intelligent control, in particular to a dynamic shelf level intelligent scheduling system. The system comprises a goods shelf distribution module, a mechanical element friction optimization module, a goods gravity center shift evaluation module and a goods handling simulation module. Obtaining storage structure data, and carrying out dynamic shelf storage space detection to obtain dynamic shelf storage space data; obtaining cargo information; performing cargo shelf distribution simulation according to the cargo information and the dynamic shelf storage space data to obtain cargo shelf distribution data; performing electric push rod stroke anomaly detection according to the goods shelf distribution data to obtain electric push rod stroke anomaly data; based on the abnormal stroke data of the electric push rod, driving motor locked-rotor detection is carried out to obtain driving motor locked-rotor data; and performing mechanical element friction optimization according to the locked-rotor data of the driving motor to obtain element friction optimization data. The warehousing operation efficiency and the system operation stability are improved based on the intelligent control technology.

The invention discloses a wind wave-seabed-pile foundation-photovoltaic array multi-physics field coupling calculation system for an ocean photovoltaic power station. The system comprises a wave model construction module, a photovoltaic array wind load numerical simulation module and a multi-physics field coupling numerical model integration module. The wave model construction module is used for extracting wave elements and constructing a wave model; the photovoltaic array wind load numerical simulation module is used for performing photovoltaic array wind load numerical simulation based on the extracted wave elements and the constructed wave model; and the multi-physics field coupling numerical model integration module is used for carrying out multi-physics field coupling numerical model integration, solving and verification based on a numerical simulation structure. According to the method, the wind wave load, the seabed seepage field, the pile foundation stress field and the photovoltaic array wind field are cooperatively calculated by building the multi-field dynamic coupling model, and therefore the accuracy and reliability of offshore photovoltaic power station pile foundation bearing performance evaluation are improved.

System and methods are described for developing a delivery plan for a pulsed field ablation device for treating an arrhythmia. A method runs a lesion development simulation based on cardiac characteristics and a treatment plan that specifies a delivery plan and a target location to generate a simulated lesion having lesion characteristics. The method initializes a three-dimensional (3D) mesh representing a heart based on cardiac characteristics. The vertices of the 3D mesh are associated with cardiac tissue characteristics with some of the associated with cardiac tissue characteristics representing an arrhythmia source. The method adjusts the cardiac tissue characteristics of vertices of the 3D mesh to reflect the effect of an ablation resulting in formation of the simulated lesion. The method runs a lesion evaluation simulation based on the 3D mesh with the adjusted cardiac tissue characteristics to determine whether the simulated lesion would be effective at treating the arrhythmia.

The invention relates to the technical field of Internet of Things perception, in particular to an intelligent storage digital visual management system and method based on the Internet of Things. The method comprises the following steps: acquiring intelligent storage structure data; constructing a storage three-dimensional model according to the intelligent storage structure data; carrying out material carrying simulation based on the storage three-dimensional model to obtain material carrying data; identifying a high-frequency congestion area based on the material handling data; carrying out cargo placement analysis according to the high-frequency congestion area to obtain cargo placement data; identifying a cargo spatial form based on the cargo placement data to obtain cargo spatial form data; carrying out cargo gravity center shift detection according to the cargo spatial form data to obtain cargo gravity center shift data; and according to the cargo gravity center shift data, acquiring a cargo unique label to obtain cargo label data. The goods stacking safety rate and the goods shelf structure operation stability rate in intelligent storage are improved based on the Internet of Things sensing technology.

The invention discloses a motion intervention multi-dimensional decision support system and method for CIPN remission, and relates to the technical field of decision support, and the system comprises an intervention effect prediction unit which is used for collecting the multi-dimensional data of a patient, constructing a patient simulation model, and carrying out the simulation of different motion intervention schemes through the Markov decision process theory, establishing an intervention effect prediction model based on the simulation result; an exercise intervention recommendation unit; an encouraging and exciting unit; the optimal motion recommendation unit is used for updating a patient simulation model according to the constructed encouraging and incentive mechanism, analyzing a causal relationship between a motion effect and neural restoration through a hypergraph gradient causal discovery algorithm, and screening out an optimal motion recommendation scheme; and a visualization unit. According to the invention, a personalized exercise intervention scheme can be customized according to the individual characteristics and physiological status of the patient, and the effects of different intervention strategies are predicted, so that the pertinence and effectiveness of exercise treatment are improved.

The invention relates to the technical field of dynamic regulation, in particular to a CFD (computational fluid dynamics) simulation-based flue gas flow field dynamic regulation system, which comprises a multi-dimensional data collaborative acquisition module, an anti-ash deposition sensor array is deployed at a key position of a flue, and a time-space fusion input data set is generated; the dual-mechanism CFD dynamic coupling module operates the main CFD prediction model and the simplified fluid network model in parallel, compares output differences through an evidence theory fusion algorithm, corrects boundary conditions and reconstructs network parameter characteristics when the confidence coefficient is lower than a threshold value, and updates a dynamic parameter knowledge base; the multi-target self-adaptive regulation and control module is used for solving a Pareto optimal strategy meeting the safety constraint by taking the baffle opening degree and the pump frequency as variables based on the updating model, driving an actuator and feeding back flow field response data; and the system forms a closed-loop adaptive mechanism, so that the CFD model continuously tracks the degradation state of the equipment, and the effectiveness of the regulation and control strategy is maintained.

The invention discloses a digital twin model driven photovoltaic field construction transportation system and method, and belongs to the technical field of photovoltaic field construction. The system comprises a digital twin module, a construction planning and design module, a monitoring and management module and an interaction module. The digital twinning module constructs a three-dimensional digital twinning model; the construction planning and design module generates a candidate construction material transportation path scheme and a candidate construction equipment scheduling scheme based on a photovoltaic field construction transportation objective function; driving the digital twin model to execute a simulation task, and adjusting the candidate construction material transportation path scheme and the candidate construction equipment scheduling scheme based on a simulation result; the monitoring and management module generates a monitoring result based on the real-time monitoring data of the construction site of the photovoltaic field area; and the interaction module provides information of a photovoltaic field construction site for a user. The construction efficiency is improved, the construction cost is reduced, quality control is achieved, and construction safety and flexibility are guaranteed.

The invention relates to the technical field of lamp model integration, in particular to an integrated design method and system for a three-dimensional simulation model of lamp equipment. The method comprises the following steps: acquiring lamp equipment basic data, classifying the lamp equipment basic data according to components, performing three-dimensional reconstruction of independent components, forming three-dimensional lamp basic composition units, acquiring a physical connection relationship, determining functional positioning of each unit, and segmenting the lamp equipment basic data into three-dimensional decoration units and three-dimensional light source units; determining a physical support structure according to the three-dimensional decoration unit; simulating and optimizing a load bearing design through weight aggravation; generating and optimizing the three-dimensional decoration unit; performing light scattering simulation based on the three-dimensional light source unit, tracking a light scattering track of the light source, optimizing the three-dimensional decoration unit according to the light scattering track, and integrating the three-dimensional light source unit and the final decoration unit. And forming a lamp equipment design integration model. According to the invention, the design efficiency of the lamp equipment is improved, and the digitization and intellectualization process in the field of lamp design is promoted.

The invention relates to the technical field of green intelligent digital construction, and discloses a green building digital collaborative design and optimization method based on BIM and cloud computing, comprising the following steps: S1, project initialization and data collection, S2, preliminary design and simulation based on BIM, S3, sensor equipment layout and optimization, S4, fault detection and early warning processing, and S5, cloud computing. According to the method, a green intelligent digital construction key process technology is adopted, energy conservation and emission reduction benefits and environment quality improvement benefits generated in the whole life period of a green building technology can be improved, and digital intelligent technologies such as BIM, 5G, IoT, BMS and EMS are adopted, so that the construction period is shortened, and the construction efficiency is improved. Visualization and perceptibility of energy conservation and emission reduction benefits and environment quality improvement benefits generated in the whole life period of the green building technology can be achieved, and economic benefits are generated from the energy conservation and emission reduction benefits generated by the green building technology in a carbon trading mode.