3587 results about "Edge device" patented technology

Provided is a road crack detection method, a medium and a product. The lightweight YOLO-MCS road crack detection model is constructed based on an improved yolov8 network. A method of improving the yolov8 network includes: replacing a feature extraction network backbone with a lightweight convolutional neural network MobileNetV3, and embedding a Coordinate Attention Mechanism (CA) module in the lightweight convolutional neural network MobileNetV3; adding a small target detection layer and a Squeeze and Excitation (SE) module at a neck end; and introducing a loss function of power Intersection over Union (IoU) into a head prediction structure. The road crack detection method overcomes the problem that the existing detection algorithm has low detection precision for small cracks and is difficult to be applied to edge devices with limited computing resources.

A system and method for adaptive edge-cloud data processing dynamically distributes computational tasks between edge devices and cloud infrastructure in response to changing conditions. The system continuously monitors resource availability, network parameters, and workload characteristics while predicting future conditions using hierarchical forecasting models. A multi-objective optimization approach determines optimal task distribution, balancing processing latency, energy consumption, bandwidth utilization, and result quality. The system implements a partitionable processing pipeline that enables seamless task migration through state synchronization protocols and checkpoint mechanisms. During migration, the system preserves processing continuity by establishing dependencies, creating execution checkpoints, and verifying successful state transfer. Performance metrics may be continuously collected and analyzed to improve future decision-making. The system maintains operational resilience during connectivity disruptions through local decision-making capabilities and eventual consistency protocols, making it suitable for diverse applications including industrial IoT, connected vehicles, healthcare wearables, and smart city infrastructure.

An AI-enhanced distributed system for neural network-based data compression leverages reinforcement learning optimization and privacy-preserving computation across edge and central computing devices to autonomously optimize efficiency and quality. The system includes a lightweight compression subsystem at edge devices that applies privacy-preserving preprocessing and partially compresses input data before securely transmitting it to central computing devices. A reinforcement learning agent continuously monitors system performance and automatically optimizes compression parameters, model selection, and task allocation based on multi-objective rewards. The central compression subsystem processes data using AI-optimized parameters and temporal modeling components. The system incorporates hardware detection capabilities that automatically select optimal compression models based on available processing resources and implements homomorphic encryption for computation on encrypted data while coordinating federated learning across distributed devices. This AI-enhanced distributed approach improves bandwidth efficiency, energy consumption, and adaptability while ensuring data privacy and security.

The invention relates to the technical field of special equipment intelligent monitoring and maintenance, in particular to a special equipment monitoring and maintenance method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting and processing multi-source heterogeneous sensor data; constructing a dynamic digital twinborn model based on sensor data and a cross-domain term mapping rule; associating the sensor data with the digital twinborn model to establish a lightweight analysis model, performing health state simulation analysis on the edge equipment, and generating an analysis result; the data quality of the edge device is monitored through the analysis model, when the data quality is lower than a threshold value, a co-simulation process is started, a simulation analysis task is transferred to the cloud digital twin platform, and the edge device continues to conduct simulation analysis based on the analysis model; and constructing a simulation design decision rule base, and generating a maintenance suggestion. Through the multi-dimensional data fusion and edge cloud co-simulation architecture, the accuracy, real-time performance and reliability of monitoring and maintenance of the special equipment are improved.

The invention provides a micro-grid fault diagnosis and dynamic recovery method based on deep reinforcement learning, and the method focuses on the multi-modal features of key nodes through a graph attention mechanism, extracts fault features through a multi-layer graph attention layer, and captures the spatial dependence relation of micro-grid nodes to recognize a fault propagation path. Meanwhile, spatial features and historical multi-modal data are fused with the help of a gating circulation unit, space-time joint feature representation is constructed, pre-fault symptom time sequence evolution is captured, intermittency and early fault detection capacity are enhanced, the multi-modal feature data fusion problem is solved, and high-precision fault diagnosis is achieved. A knowledge distillation technology is adopted to deploy a lightweight student model at edge equipment, millisecond-level emergency response is realized, fault diffusion is prevented, and meanwhile, the accuracy of diagnosis and repair strategies is guaranteed. The optimal repair strategy is generated at the cloud through the teacher model by using the global data, the system can adapt to the topological change of the micro-grid and novel faults, and the fault processing capability is continuously improved.

The invention discloses a power grid abnormal flow detection method based on multi-modal data fusion, and the method comprises the steps: collecting the multi-source heterogeneous data of a power grid through an edge calculation node, including the current waveform of an intelligent electric meter, the state variable of an SCADA system, network protocol metadata and an equipment log event; performing space-time alignment preprocessing on the original data, converting an unstructured log into a time sequence by adopting a sliding window mechanism, and eliminating sensor noise through an LSTM auto-encoder; constructing a multi-dimensional feature space which comprises a frequency domain feature, a spatial-temporal feature and a protocol feature, and obtaining a feature vector; the feature vectors are input into a hybrid detection model, the model is composed of an isolated forest algorithm, an improved CNN-LSTM classifier and an information entropy-based rule engine which are connected in parallel, and a dynamic weight fusion strategy is adopted to output an abnormal probability; and when the abnormal probability exceeds a dynamic threshold, triggering a multi-level response mechanism: sending a traffic shaping instruction to the edge device, generating a device fingerprint portrait on the cloud platform, and storing abnormal event features through a block chain.

The invention discloses a multi-agent non-cooperative game driven high-concurrency task reasoning method and system, and the method comprises the steps: decomposing a heterogeneous task into standardized task units, and carrying out the quantitative modeling of the calculation resource demands of the task; constructing a task auction mechanism of the multi-agent non-cooperative game model, performing task allocation by adopting an anti-strategic auction rule and a fragmentation asynchronous protocol, and constraining resource declaration behaviors of the agents through a credit pledge mechanism; the real-time resource utilization rate of the system is monitored, the task priority is dynamically adjusted in combination with the task preemption historical state, a resource soft preemption strategy is triggered, and a compensation queue is established; a self-adaptive model splitting strategy is adopted to distribute reasoning tasks to end side equipment and edge computing nodes, cooperative execution is achieved through data compression and transmission, and task rescheduling is conducted according to feedback of an execution result; according to the method, the task execution efficiency, the resource utilization rate and the system stability can be remarkably improved in a high-concurrency scene.

The invention relates to a power edge cloud cooperative management method based on a swan OS, and the method comprises the following steps: S1, deploying intelligent edge nodes, setting an embedded AI acceleration unit, operating the swan OS on all the intelligent edge nodes, and achieving the seamless cooperation of equipment through the distributed characteristics of the swan OS; and S2, realizing self-organizing connection of edge devices by adopting a Mesh network technology, designing a direct communication path between the edge devices, and realizing rapid processing of local problems. S3, aiming at the real-time requirement of a power system, optimizing an MQTT / CoAP protocol stack, improving the efficiency and reliability of message transmission, and allocating special network resources for different types of power applications by utilizing the 5G network slice characteristics, S4, constructing a security architecture based on a zero-trust principle, enhancing the equipment access control and data transmission security, and S5, establishing a security architecture based on a zero-trust principle. And identity authentication and data integrity protection are performed by using a block chain technology, so that data tampering and counterfeiting are prevented. According to the method, the local distributed decision-making capability and the global cooperation capability are effectively improved.

A system and method for temporal acceleration encoding in Lorentzian latent space enables real-time event forecasting within navigable spatiotemporal media. The system encodes media data into compact Lorentzian latent patches using variational autoencoders and organizes them within a multi-dimensional hyperspace spanning spatial, temporal, orientation, scale, and spectral coordinates. Temporal acceleration encoding computes velocity and acceleration vectors along geodesic trajectories, extracting event signatures through multi-scale aggregation over sliding windows. An acceleration-indexed memory stores dynamic descriptors with composite keys comprising hyperspace coordinates and motion characteristics. Event forecasting retrieves similar historical patterns and conditions a forecast head to produce event probabilities and time-to-event estimates with uncertainty calibration. The system streams forecast metadata to edge devices for real-time prediction and adaptive navigation, supporting applications in surveillance, autonomous systems, predictive media exploration, and anomaly detection where both temporal forecasting and multidimensional navigation capabilities are essential.

The invention provides a multi-feature fusion rumor detection method, system and device based on knowledge distillation, and mainly solves the problems that an existing model is high in calculation overhead, insufficient in feature fusion and insufficient in emotion utilization. The method comprises the steps of firstly obtaining multi-dimensional data such as social media original texts and comments; extracting deep semantic representation by using a pre-training model, and analyzing comment emotion features in combination with a hybrid neural network; then, features such as semantics, emotions, emoticons and populations are input into a hierarchical gating interactive fusion network (GIFN), and weights are dynamically adjusted to achieve effective fusion of multi-granularity features; in order to reduce complexity, a knowledge distillation framework is designed: a deep GIFN is used as a teacher network to generate a soft label, and a lightweight student network (LSTM) is guided to perform training. According to the trained student model, the parameter quantity is remarkably reduced, meanwhile, good detection performance is kept, the student model can be conveniently deployed in an actual content auditing system or edge equipment, and social content rumors can be efficiently recognized and judged.

The invention discloses a bearing residual life prediction method based on a multi-teacher element weight knowledge distillation network, and the method comprises the following steps: (1) carrying out the normalization, noise reduction and segmentation of a multi-dimensional vibration time sequence signal collected by an original sensor, and dividing the signal into a training set and a verification set; (2) inputting the processed training set data into a multi-teacher element weight knowledge distillation network, training teacher models, and fixing parameters of the three teacher models after training is completed; (3) performing knowledge distillation training on the student model by using an adaptive meta-weight strategy gradient learning algorithm; and (4) the student model after distillation training is used for bearing residual life prediction. According to the method, the number of network model parameters is small, the calculation complexity is low, the prediction precision is high, and the method can be actually deployed on edge equipment.

An artificial intelligence based Offensive Cybersecurity Appliance or OCA for launching a cyber-offensive countermeasure aimed at effectively mitigating sophisticated cyber threats such as real-time Ransomware as a Service (RaaS) directives and emergent killware instigated by malicious-threat actors or cyber attackers. The system covertly uses a vast array of penetrative counter cyber attacks, based on an Advanced Persistent Threat or APT model, to circumvent encountered information security controls instituted by security-based components of a cyber attacker's Local Area Network or LAN. Intrinsically, the penetrative counter cyber attacks are constituted of multiple blended artificial intelligence based cyber attacks. The OCA subsequently uses these blended artificial intelligence based cyber attacks to render the hardware (including the cyber attacker's computer system used to instigate the cyber attack, discovered peripheral smart devices, and network-edge device such as a modem-router) interconnected to the cyber attacker's LAN inoperable by destroying the electronic components associated with that hardware.

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 relates to the field of building intellectualization, in particular to a digital twinborn mixed cloud edge collaborative intelligent house building group operation and maintenance intelligent system, which establishes a digital twinborn scene database by collecting building basic information, equipment operation data and environmental parameters, synchronizes the digital twinborn scene database to edge equipment, and uses BIM, GIS, Internet of Things, 5G and AI technologies to establish a digital twinborn scene database, so as to realize the intelligent operation and maintenance of a building group. A virtual-real combined digital intelligent building scene is constructed, real-time synchronization of a virtual scene and a physical environment is realized through AR / VR equipment, and an operation and maintenance module comprises multi-source heterogeneous data fusion, edge intelligent analysis decision, adaptive model training and iterative optimization, a predictive maintenance algorithm of virtual-real mapping and a multi-level collaborative decision and autonomous scheduling mechanism. And the monitoring module monitors the state and operation condition of the edge equipment, provides data service and supports visualization of management decisions, and the system effectively improves the intelligence and digitization level of operation and maintenance of the building group.

An intelligent anomaly detection system (100) for IoT networks using edge computing and deep learning, comprising: (a) a data collection and pre-processing module configured to collect data from a variety of heterogeneous IoT devices and perform pre-processing operations, including normalization, denoising, and feature extraction; b) an edge intelligence and model deployment module configured to deploy deep learning models optimized for edge devices to perform local data analysis; (c) an anomaly detection and classification module configured to identify abnormal behavior in the processed data using deep neural networks and to classify the anomalies into predefined threat categories; (d) an adaptive learning and model update module configured to update the deployed models through incremental or federated learning mechanisms without transmitting raw data to a centralised server; (e) an alert and response management module configured to generate alerts and execute predefined mitigation actions based on the type and severity of the detected anomalies; f) and a system monitoring and visualization module configured to display real-time insights, alerts and system analysis through a user interface, g) the system operates in a decentralised manner using edge computing to achieve scalable and privacy-preserving anomaly detection in IoT environments in real time.

The invention discloses an image classification method and system based on CNN-Transform staged fusion, and the method specifically comprises the steps: inputting an input image into a lightweight CNN backbone network after preprocessing, and extracting the local features of a shallow layer, a middle layer and a deep layer; non-overlapping image blocks are divided on the middle-layer feature map, learnable position codes are embedded, and global semantic features are generated through linear attention Transform; a dynamic gating fusion module is designed, channel attention and space attention branches are combined to generate adaptive weights, and local and global features are subjected to weighted fusion; the CNN shallow detail features and Transform deep semantic features are fused through cross-level jump connection, and the classification robustness is improved. The system supports model quantization, operator fusion and edge device deployment, and the classification precision is remarkably improved while the calculation complexity is reduced.

The embodiment of the invention provides a dynamic allocation method and device for AI reasoning tasks, and the method comprises the steps: monitoring the workload, memory availability, network delay, bandwidth and energy consumption of each computing node in a cloud edge cooperation system in real time, the computing node comprising a local edge device, an edge server and a cloud platform; according to the delay requirement, the calculation requirement and the data privacy requirement of the task and the real-time state of each calculation node, dynamically distributing the task to different calculation nodes; adjusting the complexity of an AI model used for executing the task according to the resource use condition of the computing node; and according to the historical performance data and the real-time network condition in the task execution process, optimizing a task allocation strategy.

The invention discloses an end-cloud cooperative detection method and system for traffic anomalies, and relates to the technical field of intelligent traffic control. The method comprises the following steps: collecting traffic video streams through edge equipment, and identifying abnormal behaviors and generating structured event data by using a lightweight YOLOv3-tiny model; when the confidence exceeds a dynamic threshold and the event type is a high-risk type, uploading a video clip and data to a cloud; the cloud integrates a historical road condition map, meteorological data and a real-time traffic flow state, reconstructs a three-dimensional event scene by adopting a space-time attention pyramid network, and verifies the authenticity of an event in combination with a traffic flow sudden change detection algorithm; and generating a signal lamp forced switching instruction for the risk level overrun event, and issuing the signal lamp forced switching instruction to roadside equipment within 3 seconds to execute emergency response. According to the invention, full-link closed-loop control of traffic accidents from identification to response is realized, and the false alarm probability is greatly reduced while the identification accuracy is guaranteed.

The invention discloses a battery full life cycle intelligent management method and system based on a large model, and the method comprises the following steps: S1, collecting the operation data of a battery, and carrying out the preprocessing; s2, inputting the pre-processed sample into a pre-trained Transform encoder model, and extracting a time sequence and cross-stage characteristics; s3, executing model reasoning, outputting a state index, and generating a battery state vector; s4, identifying an abnormal category and a position in combination with the working condition information; s5, generating a dynamically optimized battery management strategy based on the battery state index and the abnormity identification result; s6, deploying a lightweight model at the edge device, executing local reasoning and uploading data; s7, the cloud updates the model through self-supervised training and issues the model; and S8, repeatedly executing the steps S1 to S7, and carrying out optimized closed-loop management. According to the invention, large model modeling and an edge cloud cooperation mechanism are fused, and intelligent sensing and dynamic management of the whole life cycle of the battery are realized.

The invention relates to the technical field of big data, and discloses an audio and video monitoring and early warning method and system based on multi-modal model driving, and the method comprises the steps: S1, collecting audio and video signals in a monitoring scene, and extracting the energy distribution characteristics of the audio and video signals, wherein the energy distribution characteristics at least comprise the frequency energy distribution of the audio signal and the brightness and color change rate of the video signal; s2, multi-modal feature extraction of energy distribution features is carried out based on audio and video signals, and environment background feature vectors are constructed in combination with environment background information; s3, dynamically adjusting anomaly detection thresholds of the audio signal and the video signal based on the environmental background feature vector; s4, comparing the audio and video signal energy distribution characteristics extracted in real time with an anomaly detection threshold value, and when the audio and video signal energy distribution characteristics exceed the range of the anomaly detection threshold value, determining an abnormal event and obtaining early warning information; and S5, sending the early warning information from the edge equipment to a monitoring center through a low-bandwidth communication protocol.

The invention provides a multi-mode sensor fusion algorithm for multi-signal processing and a system thereof, and relates to the technical field of sensor data processing, and the system sequentially generates standardized features through Sp1, generates a fusion feature set through Sp2, optimizes fusion parameters through Sp3, generates compensation features through Sp4, outputs perception information through Sp5, and outputs abnormal conditions such as sensor failure and overload calculation. Through feedback mechanism processing, a termination condition of perception information output or new task triggering, and breakthrough in feature retention and data consistency, a fusion result can still keep high precision in a high noise or complex scene, through failure detection and feature association degree priority ranking, other sensor data with high association degree are used for reconstruction, and a fusion result is obtained. According to the method, the accuracy of feature reconstruction is greatly improved, the high real-time performance of edge equipment and the computing power of the cloud are fully utilized by an edge-cloud cooperation mechanism, the resource utilization rate is optimized, and the stability and adaptability of the system in practical application are greatly improved.

The invention relates to the technical field of geological survey and measurement, in particular to a rock mass anomaly detection method based on vibration and visual data multi-modal fusion, which comprises the following steps: firstly, respectively acquiring vibration data and image data, and then performing data feature extraction and fusion through a deep learning model; specifically, a combined model of a long short-term memory network LSTM and a convolutional neural network CNN is used to extract time sequence features and frequency information of vibration data, a Swinin-T model is used to extract spatial features of image data, and then a shared Transform encoder and a cross attention mechanism are used to realize multi-modal data alignment and fusion. And inputting the fused features into an anomaly detection module for training and tuning, and finally deploying the trained model to edge equipment for anomaly detection of the rock mass. According to the invention, through multi-modal data fusion, abnormity can be accurately identified in various complex environments, so that the probability of false alarm and missing alarm is reduced, and meanwhile, by combining with an edge computing architecture, field deployment and real-time monitoring are facilitated, and the method is suitable for various application scenes.

The invention relates to the technical field of edge device security policy deployment, in particular to an edge device-oriented lightweight AI security policy adaptive deployment method. The method comprises the following steps: collecting operation state information of edge equipment, and constructing a current multi-dimensional environment vector and a sliding window feature vector; constructing a strategy candidate library, constructing a strategy adaptability scoring function based on the current multi-dimensional environment vector and a real-time perceived network threat event, and scoring and sorting all candidate strategy items to obtain a strategy execution candidate set; and performing scheduling optimization on the strategy execution candidate set by adopting a multi-objective optimization algorithm, and selecting a strategy combination with the highest deployment score as a deployment result. According to the method, a multi-dimensional strategy adaptability scoring function is constructed, candidate strategy items are screened according to current network threat events and system resource conditions, a strategy screening mechanism from fixed template type configuration to resource awareness and attack scene linkage is converted, and the pertinence and accuracy of strategy deployment are improved.

The invention discloses an edge computing-oriented lightweight large-model intelligent customer service deployment method, and relates to the technical field of artificial intelligence and edge computing. The method comprises the following steps: acquiring a pre-trained large language model, and pruning based on a lottery hypothesis to obtain a winner ticket sub-network; remapping the sparse structure into a V: N: M structured sparse format, and loading the V: N: M structured sparse format together with the weight and the mask to an on-chip memory; inserting a lateral branch outlet in the converter layer and carrying out token pruning; monitoring the confusion degree and the confidence degree, and dynamically recovering the pruned weight which does not exceed 5% when the load is high; and uploading the 8-bit quantization hidden state of the low-confidence or sensitive token to the large cloud model for collaborative reasoning, and combining the result with local reasoning output. According to the method, the reasoning delay and the communication overhead of the edge equipment are reduced, and the response efficiency and the deployment economy of the intelligent customer service system in a low-computing-power environment are improved.

The invention provides a large model lightweight reasoning deployment method under limited hardware resources, and the method comprises the steps: quantifying the weight importance of a large model through a composite index of gradient sensitivity and activation frequency, and carrying out pruning operation in combination with an improved index weighted moving average strategy, thereby obtaining a structured sparse model; the sparse model is divided into sub-networks by adopting double rules, a routing decision network is trained, and an adaptive feature shunting architecture model is constructed; a multi-precision weight set is generated through a nested quantization technology, quantization bit width is dynamically adjusted, and edge equipment hardware parameters are adapted to complete reasoning environment initialization; after a reasoning request is received, an optimal sub-network is selected based on the trained routing decision network, corresponding weights are loaded in parallel, and a reasoning result is fused and output; and converting a reasoning result format, and dynamically optimizing a scheduling strategy based on a system real-time monitoring index. The method is compatible with a mainstream large model and a hardware platform, and an efficient and universal deployment scheme is provided for end-side AI engineering landing.

The invention discloses a federated learning backdoor defense method based on pruning and fine tuning in the technical field of artificial intelligence and network security, the method realizes defense through two core mechanisms of dynamic pruning and gradient constraint fine tuning, and the method comprises the following steps: firstly, calculating a sensitivity score based on a neuron activation frequency and a weight outlier degree; dynamically identifying and cutting redundant neurons utilized by a backdoor, and blocking an abnormal activation path; secondly, gradient direction consistency detection and amplitude constraint are introduced in the fine tuning stage, and a malicious client is inhibited from reconstructing a back door through an abnormal gradient; the server continuously purifies model parameters and enhances robustness by cyclically executing pruning, fine tuning and aggregation operations; the method does not need to depend on an extra clean data set, strictly follows a federated learning privacy protection principle, reduces communication overhead through lightweight pruning, maintains main task performance in combination with gradient constraint, is suitable for a federated learning scene in which edge equipment participates, and effectively balances a defense effect and model stability.

The invention relates to the technical field of neural network model optimization, and discloses an edge vision AI neural network model optimization method and system, and the method comprises the steps: carrying out the visual feature layering adaptive analysis of an input image, and obtaining the importance distribution of visual features, constructing an initial neural network model adapted to the edge device according to the importance distribution of the visual features; performing energy consumption and precision balance parameter optimization on the initial neural network model to obtain edge device optimization parameters; training the initial neural network model to obtain a trained edge vision model; performing visual semantic perception model pruning on the trained edge visual model to obtain a target network structure; according to the method, deployment optimization adaptive to hardware characteristics is executed according to the target network structure to obtain a visual model for efficient operation of the edge device, so that the model can adapt to heterogeneous characteristics of different edge computing platforms, and the application feasibility of a visual AI technology on diversified edge devices is improved.

The invention discloses a road safety early warning method and system based on a mixed precision quantification visual large model, and the method comprises the steps: collecting road traffic safety videos and pictures, and carrying out the preprocessing, data enhancement and marking, thereby forming a diversified data set; a pre-trained visual large model is selected as a teacher model, after fine tuning, output layer and middle layer knowledge is extracted, key features are weighted, and meanwhile, a lightweight neural network is taken as a student model, same input is received, and prediction and middle feature maps are output. And inputting data into the two models and the student model to carry out mixing precision quantification forward propagation, constructing a total loss function containing tasks, knowledge distillation and quantification learning loss, and updating parameters through back propagation. And after training is completed, exporting a quantitative model, and deploying the quantitative model to an edge computing platform to realize safety early warning. The lightweight model can realize rapid reasoning on edge equipment such as a vehicle-mounted road side, and the problem that performance and efficiency are difficult to consider in a traditional model compression method is solved.

The invention belongs to the technical field of smart city construction and intelligent construction management, and particularly relates to an intelligent scheduling and cooperation system and method for the whole life cycle of city reconstruction. The system comprises an edge sensing layer, a digital twinning network module, a space-time brain module, a block chain collaborative trust layer and an XR collaborative interaction layer. The system collects multi-source data of a construction site in real time through a 5G edge device, realizes fusion modeling of BIM, GIS and TIN models by using a graph convolutional neural network, and performs multi-target scheduling optimization in combination with meta reinforcement learning and a dynamic graph neural network; and meanwhile, construction task verification and fund payment linkage is realized through a block chain smart contract, and immersive cooperation and visual acceptance are provided in cooperation with an AR / VR platform. According to the method, the problems of model splitting, scheduling response lagging, low data collaboration efficiency, insufficient credibility of the performance process and the like in the urban reconstruction project are solved, and the intelligence, transparency and automation level of the construction process is remarkably improved.

The invention provides a multi-target tracking method based on a YOLOv8 model and a Byte Track algorithm, and relates to the technical field of computer vision and edge equipment. The method specifically comprises the following steps: acquiring data of a plurality of images, performing format conversion, and constructing an image data set; constructing a DC-YOLOv8 network structure, and performing training by using the image data set to obtain a target detection model based on the DC-YOLOv8 network structure; obtaining a to-be-detected video stream, extracting continuous image frames from the to-be-detected video stream, and preprocessing the extracted image frames; and inputting the preprocessed image frames into the target detection model for target detection, and performing target tracking on all detected targets by adopting a Byte Track algorithm to obtain tracking trajectories of the tracked targets in all the image frames and generate a video stream. According to the invention, target tracking can be carried out on a video with many targets more smoothly.