11050 results about "Cloud server" patented technology

The invention provides a shield tunneling machine fault detection method and system based on edge computing, and relates to the technical field of industrial Internet, and the method comprises the steps: deploying edge computing nodes on a shield tunneling machine, and carrying out the time-space alignment and feature level fusion of data collected by different sensors, and obtaining unified feature data; inputting the fused feature data into an online learning model pre-deployed in an edge computing node to obtain a fault detection result; the edge computing node uploads the fault detection result and feature data related to the fault to the cloud server, generates a fault analysis report and feeds back the fault analysis report to the edge node and the user terminal; and the edge node periodically pulls the updated online learning model and knowledge base from the cloud to realize cloud-edge collaborative model updating and knowledge evolution.

The invention provides an intelligent traffic data processing method and system based on edge computing, and the method comprises the steps: collecting multi-source traffic data through an edge computing node, carrying out the preprocessing of the data, and obtaining the preprocessing data; and performing space-time alignment and data association processing on the preprocessed data to generate an associated data set, and completing feature extraction and preliminary analysis at an edge computing node to obtain a local analysis result. The local analysis result is transmitted to the cloud server, deep data mining and mode recognition are carried out, and a global analysis result is generated. And based on the global analysis result, generating a local traffic control strategy at the edge computing node, and generating a global traffic optimization scheme at the cloud server. The local traffic control strategy is issued to traffic facilities for execution, and the global optimization scheme is distributed to each edge computing node to realize cooperative control. The advantages of edge computing and cloud processing are combined, and the real-time performance of traffic data processing and the overall optimization capability of an intelligent traffic system are effectively improved.

The present invention relates to a digital twin processing method and system, and a cloud platform. The method comprises: acquiring production system elements, carrying out abstraction definition and parameterization description on the production system elements by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, so as to construct a digital twin ontology model; analyzing and reconstructing model data to obtain a mapping model of which object variables can be directly accessed and operated by a collective motion control method, so that the model is visualized at the cloud; and using an external data source to drive parameter update and operation matching of the model by means of a motion control method, so as to complete cooperative deployment and synchronous evolution of an actual physical device and the model in the production process on a cloud server. According to the present invention, a model is constructed by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, the model is mapped to achieve motion visualization, model parameter update and operation matching on the cloud are achieved, and then cooperative deployment and synchronous evolution of a physical device and the model are completed.

The invention relates to the technical field of charging pile collaborative management, in particular to a new energy automobile charging pile intelligent operation and maintenance management system, which comprises a data acquisition and transmission unit, an edge computing unit and a cloud server unit, and is characterized in that the data acquisition and transmission unit collects multi-aspect information of a charging pile and transmits the multi-aspect information to the edge computing unit; the edge computing unit preprocesses the data, starts an emergency protection mechanism when the power grid fluctuates, and uploads the data and decision results, the cloud server unit constructs a cloud computing platform, a data storage service stores the data, a data analysis service excavates a charging rule, and an algorithm model service deploys an intelligent adaptive power distribution algorithm. The system comprises a dynamic priority determination module, a real-time power adjustment module and a cross-station cooperation power scheduling module which are respectively used for determining the vehicle priority, adjusting the power according to the power grid load and guiding the vehicle to perform cross-station charging, thereby solving the problems of unreasonable power distribution and insufficient cross-station cooperation in the operation and maintenance management of the charging pile, and improving the use efficiency of the charging pile.

The invention provides an electric vehicle energy storage battery fault diagnosis and predictive maintenance control method and system, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting the real-time working condition data and environment parameters of an electric vehicle energy storage battery, and carrying out the data preprocessing through a cloud server; carrying out life prediction by utilizing a multi-task deep learning framework, and generating a comprehensive state evaluation report; constructing a multi-target maintenance decision optimization model based on the evaluation report, and predicting an optimal maintenance time window; historical maintenance data and fault diagnosis experience are fused, and a maintenance operation list is generated; and performing resource scheduling optimization through a mixed integer programming algorithm to form an optimal battery maintenance scheme.

The invention relates to a health monitoring and fault prediction technology of intelligent equipment, in particular to an equipment fault prediction method based on health degree analysis of the intelligent equipment. According to the method, multiple sensors are embedded in the intelligent device, data of the operation state of the device are continuously collected, and primary processing and abnormal value elimination are carried out through an edge computing node. The processed data is transmitted to a cloud server, and machine learning models such as a deep neural network, a Bayesian classifier and a support vector machine are utilized to analyze the equipment health degree trend, and a potential fault mode is identified. And intelligently generating a fault prediction report according to an analysis result, and notifying an operation and maintenance team to take measures through an operation and maintenance management system. In addition, the system further comprises a historical fault database, a scoring system, an online review group and other functional modules, the accuracy and reliability of prediction are ensured, and the equipment maintenance efficiency and safety are improved.

The invention provides a data transmission method and transmission device applied to a vehicle-road cloud collaborative road network system, and the method comprises the steps: collecting vehicle operation state data through a vehicle-mounted terminal, collecting real-time road network topology data through road side equipment, and transmitting the data to a cloud server for data preprocessing; performing feature extraction on the preprocessed data based on a preset time-space correlation model, generating dynamic road network feature data, calling a cooperative transmission model to perform transmission path optimization on the dynamic road network feature data, generating a path optimization parameter set, and performing dynamic adjustment on a target transmission path according to the path optimization parameter set. Generating a self-adaptive transmission path adaptive to the current road network state; and distributing the target vehicle data packet to the roadside node and the cloud node through the self-adaptive transmission path, and carrying out retransmission scheduling on the data packet which is not successfully transmitted based on a feedback confirmation mechanism. According to the invention, the communication efficiency and the system robustness in a vehicle-road cloud collaborative scene can be improved.

The present invention discloses a blockchain-based Industrial Internet of Things (IIoT) data management system and method, where the system includes a physical layer, an edge layer, a network interface layer and a cloud layer; the physical layer includes a plurality of different IIoT device monitoring systems; the edge layer is configured to build an IIoT blockchain network; the cloud layer includes cloud servers which are configured to be deployed in a real blockchain network environment to improve performance and effectiveness of the blockchain network. The present invention can effectively solve the problem of parameter optimization lag in industrial production applications.

The invention relates to the technical field of geometric quantity precision measurement based on satellite positioning, in particular to a pumped storage power station dam safety monitoring method based on Beidou positioning, and the method comprises the steps: arranging a Beidou monitoring station and base station array in a dam body and a slope region, synchronously receiving multi-band satellite signals, and carrying out the edge calculation processing; and the effective frequency band data are screened and fused by using a signal-to-noise ratio threshold to generate preprocessed observation data, and the preprocessed observation data are uploaded to a cloud server in real time through a 4G network and a Beidou short message dual-channel transmission link. A cloud server constructs a local enhanced network, ionosphere and troposphere errors are corrected by adopting a multi-base-station joint error modeling method, a closed-loop mechanism dynamically optimizes monitoring point distribution density, signal processing parameters and atmospheric refraction compensation coefficients, and data integrity is guaranteed in combination with a dual-channel redundancy check and interpolation completion technology. According to the invention, full-domain millimeter-level deformation dynamic monitoring of the dam under the complex terrain is realized, and the space-time reference uniformity, the data real-time performance and the structure safety evaluation precision are improved.

The invention discloses a digital human real-time interaction system and method. The interaction system comprises terminal equipment and a cloud server. The terminal device receives voice input by a user and transmits the voice to the cloud server. The cloud server comprises a video acquisition and processing module, an intelligent question and answer and voice generation module, an audio processing module and an image rendering module. A video capture and processing module captures original video data indicative of an object of a digital figure and generates successive image frames. The intelligent question and answer and voice generation module converts the voice into a text; performing semantic analysis on the text, understanding the intention of the user, and generating a corresponding reply text; the reply text is also converted into a reply voice. The audio processing module processes the reply voice and the continuous image frames to generate first video data. And the image rendering module fuses the first video data with the virtual background, and performs real-time processing on the fused video to generate a response video. And the terminal equipment outputs the response video.

The invention belongs to the field of intelligent edge systems and privacy enhancement computing, and particularly relates to a privacy protection-oriented robot large model cloud edge end collaborative reasoning and federated learning system, which comprises a cloud server layer used for deploying a large-scale pre-training model and executing complex reasoning and global federated learning coordination; the edge calculation layer is used for deploying an intermediate layer model and executing local data aggregation, privacy protection processing and intermediate feature calculation; the terminal equipment layer is used for deploying a lightweight model and executing data acquisition, primary processing and lightweight reasoning; the federated learning framework is used for optimizing the model; the privacy protection module is used for integrating data localization, differential privacy, homomorphic encryption, secure multi-party computing and a block chain verification mechanism; the adaptive allocation module is used for dynamically adjusting computing resources. According to the method, the problems of privacy leakage risk, computing resource limitation, network delay, insufficient data isolation and the like of the traditional AI service in a robot scene are solved, and efficient privacy protection and data security isolation are realized.

The invention discloses a Transform large model training method based on cloud edge collaboration. The method comprises the following steps: S1, constructing a data set; s2, preprocessing the data set; s3, segmenting the preprocessed data set by using a data parallelism strategy, and performing distributed training by combining data parallelism and model parallelism technologies; s4, monitoring computing resources and network conditions of the edge equipment and the cloud server in real time, dynamically adjusting a data distribution strategy, and optimizing computing load distribution; s5, a multi-agent dominant actor-commentator algorithm is combined with a multi-arm tiger machine strategy, and the layered structure of the Transform model is adjusted in real time; s6, performing partial model reasoning tasks on the edge equipment in combination with the edge reasoning capability; s7, protecting data privacy by adopting a federal learning strategy and utilizing a differential privacy technology; and S8, performing hardware acceleration by utilizing heterogeneous computing resources. According to the method, a cloud edge cooperative computing and intelligent scheduling method is adopted, and large model training and reasoning with efficient privacy protection are realized.

A video streaming system including a video cache system having a cache database and a video storage device, and including a video server configured to communicate with a gateway system configured to store multiple video streams, communicate with a cloud backup system configured to store video in a file system, and communicate with a cloud server to receive a stream request, the stream request indicating requested video that comprises multiple video segments. The video server is further configured to receive the stream request and determine a playlist that indicates where on the video cache system, the cloud backup system, and the gateway system the video segments that are needed to fulfill the stream request are stored, and to communicate needed video segments to the video cache system in preparation for providing streaming video to fulfill the stream request.

The invention relates to the technical field of electromagnetic pulses, and discloses an electromagnetic pulse data transmission method. The method comprises the following steps: collecting natural electromagnetic pulse signal data of a detection target area through a six-dimensional complementary electrode array structure, and executing Kalman filtering peak extraction and characteristic frequency locking to obtain frequency-optimized signal data; executing first-order and second-order gradient difference matrix calculation and space vector synthesis to obtain signal source position coordinate data and signal intensity data; performing spectral domain reconstruction and multilayer dynamic threshold analysis to obtain a spatial clustering abnormal data packet; and selecting an optimal transmission channel to transmit the spatial clustering abnormal data packet to a cloud server. According to the invention, the influence of an external interference source is effectively eliminated, accurate extraction and characteristic frequency locking of effective frequency band signals are realized, power frequency and harmonic interference thereof are effectively suppressed, the signal-to-noise ratio of the effective frequency band of the signals is improved, and the reliability and timeliness of data transmission in a severe field environment are ensured.

The invention provides an unmanned aerial vehicle identification early warning method and system. The method comprises the following steps: acquiring RGB image data, thermal radiation data and spectral data of an unmanned aerial vehicle no-fly zone through a visible light camera, an infrared thermal imager and a multispectral imager; performing transmission preprocessing on the RGB image data, the thermal radiation data and the spectral data, and adaptively adjusting multi-level fusion of a fusion weight based on real-time environmental parameters to generate target fusion data; based on a deep learning model and a tracking prediction algorithm, performing unmanned aerial vehicle identification early warning on the target fusion data, and generating early warning data; and transmitting the target fusion data and the corresponding abnormal event log to a cloud server, and updating the deep learning model by adopting the target fusion data and the abnormal event log. Through cooperative work of a multi-mode sensor, visible light, infrared, multispectral and other wave bands are covered, all-weather and full-scene unmanned aerial vehicle detection is achieved, the fusion weight is adjusted in real time based on real-time environment parameters, and the accuracy of the recognition result under the complex air situation is ensured.

The invention relates to the technical field of Internet of Things communication, and discloses an Internet of Things data information transmission method, a switch and a transmission system. The method comprises the following steps: acquiring multi-source data streams of Internet of Things terminal equipment, dynamically fragmenting according to types to generate fragmented data packets, matching a target transmission protocol for the fragmented data packets, generating a dynamic routing path in combination with a priority mark and the like, and transmitting the dynamic routing path. The system also has the functions of link quality evaluation and route adjustment, redundant coding packet loss recovery, transmission delay and integrity monitoring processing, encryption transmission based on security level, resource optimization, transmission strategy adjustment and the like. The switch is integrated with a data fragmentation module, a protocol matching module and the like. The transmission system comprises a terminal device cluster, an edge node network, a cloud server and a protocol conversion gateway. The data transmission efficiency, reliability and safety are improved, resource utilization is optimized, and the data transmission problem of the Internet of Things is effectively solved.

The invention relates to the technical field of smart communities, and discloses a smart community target identification tracking method based on big data, which comprises the following steps: constructing an omnibearing monitoring network in a community, collecting real-time video and sensor data in the community, obtaining feature data of a target through an edge computing technology, and carrying out target identification tracking on the feature data; transmitting the target feature data to a cloud server through a network, identifying and classifying the target feature data, judging the type and identity of the target, analyzing the current behavior of the target according to a preset target historical behavior pattern library, and determining whether the behavior is abnormal or not; and if the behavior of the target is not matched with the normal mode in the historical behavior mode library, judging that the behavior is an abnormal behavior, and triggering an early warning mechanism. According to the invention, intelligent identification, dynamic tracking and timely early warning of the abnormal behavior of the smart community are realized, and the intelligent level and response efficiency of community safety management are improved.

The invention relates to the field of virtual reality positioning, and discloses a VR large-space positioning interaction system based on multi-modal perception, and the system comprises the steps: deploying a multi-modal sensor to obtain sensing data, carrying out the visual feature extraction and preprocessing, building a sparse point cloud map in a matching manner, carrying out the scale calibration, and constructing an environment model; pre-judging a UWB signal path based on an environment model, performing error optimization compensation on an NLOS state, and performing observation updating and fusion through degradation detection to obtain a predicted state change; constructing an interactive perception network, and tracking the hands and the whole body; tactile feedback is realized by using a layered tactile system, and a tactile effect is generated by using vibration frequency mapping; the transmission efficiency is improved by using a beam forming technology, an edge cloud server cluster renders a virtual scene, and the scene is pre-rendered in advance to offset network and rendering delay; an online calibration mechanism is designed, and system errors are corrected through visual loopback detection, UWB beacon dynamic correction and IMU drift compensation.

The invention discloses an evaluation method based on water ecological environment monitoring, and relates to the technical field of water ecological environment evaluation. Comprising the steps of data collection: obtaining a river basic information data set, a monitoring equipment information data set and a historical monitoring information data set, and building a cloud server to store the river basic information data set, the monitoring equipment information data set and the historical monitoring information data set. According to the method, each region is accurately monitored through region division of river landforms and a targeted monitoring method, the timeliness of monitoring data is ensured, the water quality condition can be evaluated in real time and the water quality of the corresponding region under unique weather can be evaluated by acquiring a region monitoring data set and a weather information data set in real time to cooperate with an evaluation model, and the water quality evaluation accuracy is improved. In addition, weather changes can be monitored and early warned, timely support is provided for dealing with emergencies, and by summarizing the method, related departments can formulate more reasonable treatment schemes, the water quality management efficiency and effect are improved, and the water quality problem is found and solved in time.

A system for breakage early warning of high-voltage transmission lines based on acoustic prints, including a data acquisition unit, an edge processing unit, a cloud server, a warning execution unit, and a communication unit. The data acquisition unit is configured to collect environmental information of a high-voltage transmission tower in real time through a multimodal sensing device. The edge processing unit includes an adaptive filtering module configured to eliminate wind noise interference in the environmental information and compensate for frequency response deviation. The cloud server can generate a warning instruction based on the environmental information. The warning execution unit can perform warning and alarming actions based on the warning instruction. The communication unit is configured to enable data and information transmission among the units. A method implemented by such system is also provided.

The invention discloses an AI server intelligent data analysis method based on edge-cloud collaboration, and relates to the technical field of edge-cloud computing collaboration, edge equipment is deployed at an edge end, original data of each task is collected in real time, the data is preprocessed, task difficulty features are extracted, and a task difficulty feature sequence is formed; and based on the task difficulty feature sequence and a pre-trained AI data analysis model, the task difficulty of the collected task data is evaluated in combination with the current resource condition, and the feasibility of task execution at the edge end is judged. According to the method, equipment is deployed at the edge end and data is preprocessed, so that part of computing tasks are effectively unloaded to the edge end, the computing burden of a cloud AI server is remarkably reduced, the edge end can independently complete simple tasks, complex tasks are uploaded to the cloud according to needs, excessive centralized use of cloud resources is avoided, and the computing efficiency is improved. Therefore, the energy consumption and the operation cost of the cloud server are reduced, and the energy efficiency ratio of the whole system is improved.

The invention discloses an ecological garden intelligent management method and system, and belongs to the technical field of landscaping management, and the method specifically comprises the steps: monitoring environmental parameters in real time through multiple sensors, and uploading the environmental parameters to a cloud server; at the cloud end, a dynamic data analysis model is constructed by utilizing historical irrigation data, a plant growth model and real-time monitoring parameters, the water demand of plants is predicted, and an irrigation decision is made; an irrigation threshold value is set according to the decision, and an irrigation program is automatically started and executed through remote control equipment; after irrigation, detailed information is recorded and fed back to the data analysis model, the irrigation effect is evaluated by adopting a data analysis method, and an irrigation decision is finely adjusted accordingly; intelligent and precise irrigation management is realized, and the utilization efficiency of water resources and the growth quality of plants are improved.

The invention provides a smart home management method and system based on the Internet of Things. The method comprises the steps that indoor and outdoor environment parameters, user physiological data, home equipment operation states and energy consumption data are collected; based on the data, learning behavior preferences of the user in different time and environments, and establishing a personalized behavior prediction model; deploying the model at an edge computing node, combining big data analysis of a cloud server to form a hierarchical intelligent decision-making architecture, and outputting a preliminary decision-making result; the real intention of the user is understood and an intention confidence evaluation mechanism is established; when the consistency of the identification results of the multiple modes is lower than a threshold value, confirmation is actively carried out on the user, and a primary intention is obtained; according to the intention, a control strategy is dynamically generated in combination with the environment state and the equipment capacity; and based on the operation data and the energy consumption data, establishing an equipment health degree evaluation model, and predicting a fault risk and a maintenance demand. Through the scheme of the invention, the intention of the user can be accurately identified, and accurate personalized services are provided, so that the user experience is improved.

The invention discloses a data security transmission method for an edge gateway of the Internet of Things, particularly relates to the technical field of data transmission, and is used for solving the problem that an encryption mode of existing low-power-consumption equipment is not flexible in data encryption transmission. Sensing data are collected and preprocessed through low-power-consumption equipment; analyzing a multi-dimensional load state of a communication channel through network state tensor decomposition, and evaluating a future load change risk; time distribution of encryption and decryption links is analyzed through a distributed time optimization game, and link delay balance is evaluated; and dynamically adjusting whether to use the lightweight encryption algorithm based on the analysis result. When a lightweight encryption algorithm is selected, the equipment encrypts data to generate preliminarily encrypted data and transmits the preliminarily encrypted data to the edge gateway; and the edge gateway performs high-strength encryption upgrading on the preliminary encryption data to generate deep encryption data and transmits the deep encryption data to the cloud server. According to the invention, the energy consumption of the low-power-consumption equipment is reduced while the data security is ensured, and the reliability of data transmission is improved.

The invention provides a mobile robot operation efficiency evaluation method and system based on the industrial internet, and relates to the technical field of operation efficiency evaluation, and the method comprises the steps: collecting multi-mode operation data, selecting a transmission channel, uploading the data to an edge calculation node, carrying out the preprocessing, completing the information, and carrying out the filtering operation, generating a first semantic feature and an operation feature descriptor; selecting an optimal transmission path and a service function chain, uploading the operation characteristic descriptor to a cloud server, carrying out real-time analysis on the operation characteristic descriptor, generating an initial operation efficiency evaluation result, carrying out analysis and comparison, determining an efficiency bottleneck, and generating an initial optimization improvement suggestion; sending an initial operation efficiency evaluation result to an edge computing node, performing semantic enhancement and logical reasoning, generating a refined efficiency evaluation report, analyzing an incidence relation and an influence mechanism among different evaluation indexes, determining key efficiency bottleneck factors, generating a comprehensive optimization suggestion, collecting operation data in real time, and generating a comprehensive efficiency evaluation report.

The invention relates to the technical field of intelligent sleep monitoring blankets, in particular to an intelligent sleep monitoring blanket based on multi-modal physiological signal fusion and an optimization method of the intelligent sleep monitoring blanket. The signal acquisition end is used for acquiring multi-modal physiological signal data; the cloud server is used for processing and analyzing the physiological signal data, optimizing a signal acquisition process, performing sleep staging, performing sleep quality evaluation according to a sleep staging result, and providing a sleep quality report and suggestion for a user; the signal acquisition end transmits data to the cloud server end by using a Bluetooth protocol, and optimizes the acquisition process of each sensor according to a specific hierarchical architecture; the cloud server receives various multi-modal physiological signals and performs sleep data fusion, a deep learning algorithm is utilized to establish a sleep staging model to optimize a sleep environment, and an innovative multi-scale time sequence attention network and a multi-model integration framework are combined, so that the sleep staging accuracy is greatly improved, and a reliable basis is provided for sleep quality evaluation.

A system for controlling a charging of an electric vehicle, wherein a charging at one electric vehicle charging station affect a charging at another electric vehicle charging station is disclosed. The system includes: an electric power grid, a first electric vehicle charging station connected to the electric power grid, and a second electric vehicle charging station connected to the electric power grid, wherein the first electric vehicle charging station facilitates a charge transfer for an electric vehicle at the second electric vehicle charging station using a mobile device. The mobile device relays communication from the electric vehicle charging stations to the cloud server. The charge transfer request received at the cloud server is authorized using identification information and credit account information received from the mobile device. The charge transfer at the first electric vehicle charging station is adjusted based on a charging level at the second electric vehicle charging station.

To provide a blockchain-based commodity anti-counterfeit traceability method, an apparatus, and a cloud server that implement a highly reliable anti-counterfeit traceability system.SOLUTION: A method is applied to a cloud server equipped with a blockchain module, the method including: receiving an anti-counterfeit code for a commodity transmitted from a terminal; searching the blockchain module for commodity information corresponding to the anti-counterfeit code; feeding back commodity inquiry failure information to the terminal when no commodity information corresponding to the anti-counterfeit code exists in the blockchain module; and feeding back commodity information to the terminal, the commodity information including shipping information and a serial number of the commodity, when the commodity information corresponding to the anti-counterfeit code exists in the blockchain module.SELECTED DRAWING: Figure 2

The invention provides a task scheduling method and system based on an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the field of task scheduling. The method comprises the following steps: acquiring tasks, resources and user behavior data from a user side and a cloud server through an information collection module, and storing the tasks, the resources and the user behavior data into an information base; the task evaluation module analyzes the data and generates a task value score; the scheduling strategy generation module formulates a preliminary execution scheme in combination with a preset rule, and optimizes through a reinforcement learning algorithm to obtain a dynamic optimal scheduling decision; the task redundancy analysis module is used for identifying mergeable redundant tasks and generating a report for further optimizing a scheduling decision; the dynamic priority adjustment module adjusts task priorities and resource allocation according to the real-time monitoring data; and the scheduling execution module is responsible for performing specific execution and generating a state report. The multi-module collaborative design realizes the intellectualization and adaptivity of task scheduling, and improves the utilization rate of system resources.

According to the technical scheme, the BIM model Web end lightweight display method comprises the following steps that S1, a project drawing is created; s2, modeling by using BIM software, and constructing a model hierarchical structure; s3, performing multi-level integration compression on the BIM model; s4, the lightweight BIM model is imported into Unity, and rendering is carried out; s5, UI interface element design is carried out by using a Unity UI; s6, a WebGL platform is set in the Unity, and WebGL items are packaged; and S7, deploying the file to a cloud server. The method has the beneficial effects that multi-level integration lightweight is adopted, and the integrity of geometric data and material texture information of the BIM model is guaranteed. The rendering detail level of the model is adjusted through Lod multi-level detail management built in Unity, and the rendering burden is reduced while the quality of the BIM model is ensured. And by utilizing a Unity asynchronous loading function, the transmission data volume is effectively reduced, and the loading speed and the rendering performance are improved.