34 results about "Construction site safety" patented technology

The present application relates to a kind of construction safety risk early warning method and system based on multi-source data fusion, first acquisition including positioning trajectory, sensor signal, inspection text and video features and so on multiple types of real-time data, in combination with the construction field knowledge graph of semantic consistency verification, realize unified space-time datum and semantic association mapping;Through multimodal feature enhancement, space-time coding and cross-modal fusion, high-dimensional hidden state sequence is established;Causal structure model is constructed using knowledge graph weak causal relationship edge, and counterfactual disturbance response modeling is carried out on high-dimensional data, and the association of context and risk event is extracted;Dynamic adjustment risk assessment result, in combination with risk threshold realizes closed loop early warning response, and through feedback self-optimizing model parameter.The present application improves the precision and self-adaptive ability of construction site safety risk identification and response.

This invention relates to the field of safety risk identification technology, specifically disclosing an artificial intelligence-based safety risk identification and intervention system for construction sites. The system includes: a data acquisition module that collects personnel operation videos, images, and sensor data from multiple nodes within a key area, and performs preliminary processing to obtain raw multimodal perception data streams; a scheduling and analysis module that uses a first neural network model to perform real-time analysis of the raw multimodal perception data streams, generating risk urgency scores and pre-classification results, generating key risk feature vectors, and triggering corresponding data transmission paths; a risk identification module that receives the key risk feature vectors of the current data transmission path and uses a second neural network model to identify associated risks, generating a risk report; and a system that coordinates and optimizes the first and second neural network models based on the risk report; and an execution feedback module that receives and executes intervention instructions from the risk report through a preset hierarchical strategy library, performing intervention actions.

This invention relates to the field of construction site safety technology, and particularly to an active safety protection method and system for construction sites based on dynamic projection of a crane boom. This solution utilizes the boom length and elevation angle parameters to map its geometric envelope on the work surface in real time, and introduces inertial compensation based on motion state variables and environmental compensation based on wind speed and direction. This ensures that the generated dynamic danger zone can realistically cover the swaying space of the hoisted object caused by physical inertia or external interference. This follow-up modeling method effectively eliminates blind spots in safety supervision caused by boom extension, retraction, and rotation, reducing false alarm and missed alarm rates.

The application provides a kind of construction site safety risk real-time monitoring and early warning intelligent management and control system, including data acquisition module, data preprocessing module, risk identification module, risk assessment module and early warning and control module.The application aims to realize the whole process, high precision, intelligent control of construction site safety risk through multi-module cooperation and multi-algorithm deep integration, effectively improve the efficiency of construction safety management and risk early warning capability.

The application belongs to the technical field of video processing, and particularly relates to a video slicing method and system based on computer vision, which can accurately identify the behaviors of personnel or vehicles in a construction site under complex lighting conditions, and effectively distinguish whether the behaviors belong to part of the normal work process, thereby significantly reducing the false alarm rate and improving safety; by calculating the lighting change parameters in the active area, adjusting the contrast of the video frame, detecting the behaviors in the unexpected area and the frequency and duration thereof, and then evaluating the potential risk level according to the position information and triggering a warning signal when the preset threshold is exceeded, the method greatly improves the efficiency and accuracy of the construction site safety management, effectively prevents potential safety hazards, and ensures the safety of personnel and equipment in the construction site.

This invention relates to a method and system for managing engineering hazards based on multimodal interaction, belonging to the field of engineering management. It collects voice information from the construction site to obtain voice data, uploads it to the cloud for adaptive interpretation processing to generate semantic data; combines the semantic data with image acquisition equipment to mark and locate hazard positions to obtain image data; constructs a safety hazard knowledge graph using a safety standard clause library as real-time constraints, and classifies hazards based on a hazard level prediction model to obtain response levels; maps response levels to a collaborative processing flow list to extract the current execution role, presets a processing time threshold, and if the processing time of the current execution role exceeds the preset processing time threshold, increments the execution role based on the collaborative processing flow list, and dispatches multi-level work orders based on role flow information. This solves the problems of low efficiency, lack of management closure, insufficient compliance, and insufficient accuracy and efficiency in hazard identification during construction site safety management.

This invention discloses a safety monitoring and early warning system for construction workers, relating to the field of construction early warning. It includes a monitoring and early warning system and a safety helmet. The monitoring and early warning system comprises an identity binding module, a signal receiving module, an early warning judgment module, a mobile push module, and a data storage module. The safety helmet consists of a helmet shell, a helmet liner, and a floating top pad. A pad is located below the floating top pad at the perforation. A mounting shell is fixed to the lower surface of the helmet liner. The pad and the mounting shell are connected by a compression assembly. A control chip is fixed to the inner bottom wall of the mounting shell by screws. An infrared heat source sensor and a pressure sensor are respectively installed on the top plates of the pad and the compression assembly. This invention accurately identifies violations such as not wearing a helmet or wearing it improperly through dual detection. Combined with the monitoring and early warning system, it constructs a comprehensive safety management mechanism, facilitating managers to statistically analyze historical data, summarize patterns, and improve the level of safety management at construction sites.

This disclosure presents a construction site safety risk early warning method and device, electronic equipment, and storage medium. Through this application, by integrating real-time sensing data from the construction site, linking it with the building information model and the current construction progress to dynamically assess risk sources and levels, and using artificial intelligence models to identify personnel and equipment violations or entry into dangerous areas in real time, and executing graded early warnings and even equipment interlocking controls according to the risk level, a closed-loop safety management and control system of pre-judgment and in-process intervention is constructed. This achieves the technical effect of eliminating blind spots in construction site safety inspections, realizing real-time identification and graded handling of risk sources and violations, improving the timeliness and accuracy of safety management and control, reducing the probability of safety accidents, and thus meeting the technical requirements of improving the inherent safety of the construction industry.

The application discloses a construction site safety risk real-time early warning method and system based on artificial intelligence, relates to the technical field of safety risk early warning, and comprises the following steps: acquiring pixel displacement amplitude, contour edge jump frequency and local occlusion overlap ratio in a multi-person dense picture of a construction area, generating a separation driving identifier based on the pixel displacement amplitude, the contour edge jump frequency and the local occlusion overlap ratio; performing gradient cutting processing on continuous adhesion regions in the picture based on the separation driving identifier, and introducing direction difference constraints at each adhesion boundary position. Through the separation driving identifier, the gradient cutting and the direction difference constraint, the application realizes separation of adhesion regions in a dense scene, restores individual action information, avoids group covering influence, and improves the integrity of abnormal action recognition; meanwhile, through dynamic shrinkage definition, reverse mapping backfilling and adjustment of an early warning threshold, the application realizes abnormal action stripping and accurate positioning, and improves the timeliness and determination accuracy of risk early warning.

The application discloses a highway concrete guardrail construction site safety control system, comprising: a video acquisition module for acquiring image frame sequences; a target detection and tracking module for obtaining the behavior feature sequence of the candidate target based on the boundary box; a behavior fingerprint modeling module for obtaining the abnormal score and behavior fingerprint vector of each candidate target; an abnormal window determination module for obtaining the abnormal window and constructing the abnormal behavior multi-modal sample; a semantic judgment module for obtaining the semantic judgment result based on the abnormal behavior multi-modal sample; and a safety control instruction module for generating the safety control instruction vector according to the semantic judgment result. The application introduces the video behavior fingerprint modeling of ViT and SRU++, can stably obtain the individual behavior features with higher discrimination in the highway concrete guardrail construction site, and thus significantly improves the refinement, intelligence and traceability of the construction safety control.

This invention relates to the field of construction safety monitoring technology, and discloses a construction site safety early warning system based on UWB and AI cameras. Through multi-source sensor calibration and synchronization, it collects UWB ranging and video frames, and outputs aligned multi-source data. Based on time-frequency domain features and residual networks, it identifies non-line-of-sight locations and compensates for deviations, combining adaptive unscented Kalman filtering to output worker 3D coordinates. It detects targets and behaviors in video frames, achieving cross-modal binding through trajectory constraints and depth metric learning. It analyzes the BIM model to obtain hazardous zone boundaries, using instance segmentation and point cloud reconstruction for correction, and sets dynamic warning zones based on location covariance. It outputs tiered warnings through three-level risk assessment and attention fusion. Edge nodes trigger alarms locally, and edge-cloud collaboratively synchronizes data to the cloud, outputting records and reports. This invention can achieve multi-modal fusion perception and tiered early warning of worker location, behavior, and hazardous zones in complex environments such as tunnel construction.

This application relates to a construction safety monitoring device based on BIM and the Internet of Things (IoT). The device includes a data acquisition unit, a processing unit, a standardized physical interface, and an early warning unit. The data acquisition unit includes at least two different types of sensors. The processing unit communicates with the data acquisition unit via the standardized physical interface to receive site environmental data sent by the data acquisition unit and obtain risk analysis results. The processing unit also communicates with a BIM interaction interface to update the BIM model. The early warning unit communicates with the processing unit for risk warning. This device automatically monitors construction site safety without human intervention, solving the problems of low efficiency and reliability in construction site safety monitoring and reducing accident risks. The standardized physical interface supports flexible access to sensors and equipment, adapting to different construction site scenarios. Through the BIM interaction interface, dynamic IoT data is combined with the BIM model to achieve real-time updates of the BIM model.

The utility model belongs to the technical field of security equipment, and relates to a construction site safety protection device for construction, which comprises a base plate, a cross beam plate, a U-shaped plate and a safety flat net, expansion bolts are arranged at the left end and the right end of the base plate; the front ends of the two cross beam plates are detachably connected to the left end and the right end of the base plate through bolts correspondingly. The number of the U-shaped plates is two, the two ends of one U-shaped plate are connected to the left side of the left cross beam plate, and the two ends of the other U-shaped plate are connected to the right side of the right cross beam plate; the left end of the safety flat net is connected to the left U-shaped plate, and the right end of the safety flat net is connected to the right U-shaped plate. The safety protection device is installed in the elevator shaft to prevent falling, the base plate, the stand column, the cross beam plate and the U-shaped plate form the support used for supporting and installing the safety flat net in the elevator shaft, when the safety flat net is disassembled and assembled, an operator does not need to enter the elevator shaft to conduct disassembling and assembling construction, the risk that the operator falls from a high place when entering the elevator shaft to conduct construction can be reduced, and the safety protection device is convenient to use. And the high-altitude operation fear of operators is reduced.

The application discloses a construction site safety compliance low-power-consumption AI camera device and method based on edge side risk chain determination, and relates to the technical field of construction site safety visual monitoring.The device comprises an event trigger wake-up module, a dual-light acquisition module, a construction site scene feature filtering module, a lightweight AI inference module, a human-equipment association module, an edge risk chain determination module, a hierarchical early warning module, a network interruption buffer module and a multi-mode power supply module; the adaptive fusion of visible light and infrared images improves the robustness in a dust raising, night or strong light environment; fixed interference such as scaffolding and building materials is removed by a construction site scene mask; a lightweight deep learning model (INT8 quantization improved YOLO-Tiny) is used to realize synchronous detection of personnel, safety helmets, reflective clothing and safety belts; and accurate area matching of personnel and protective equipment is completed based on human skeleton key points; finally, a three-level risk chain of "protective equipment wearing state-operation area attribute-personnel time sequence moving track" is constructed on the edge side, and local real-time hierarchical early warning is realized.The application does not depend on cloud computing throughout the process, can independently operate in a network interruption state, has low power consumption, low false alarm rate and low deployment cost, is suitable for complex construction site scenes such as building construction, municipal engineering and mine operation, and has significant novelty, creativity and practicality.

This application discloses a dynamic early warning method, electronic device, and readable storage medium for construction site safety, belonging to the field of image communication technology. The method includes the following steps: acquiring a real-time video stream collected by a visual perception unit at the construction site; based on the real-time video stream, calling a machine learning model to identify abnormal behavior; generating early warning information when abnormal behavior is identified; wherein the early warning information is displayed on a display terminal at the construction site to alert personnel in the vicinity of the display terminal. This application reduces the difficulty of safety management and the risk of accidents.

This invention discloses a smart construction site safety inspection device and system based on image recognition, belonging to the field of machine inspection technology. The system acquires audio, video, and environmental parameters from the construction site through a multimodal acquisition unit. Combined with image recognition, eye tracking, semantic fusion, and permission verification technologies, it achieves full-process automation of target interaction personnel identification, effective voice command filtering, target device positioning, and control command generation and execution. The device provides hardware support for the system, ensuring the stable operation of each functional module. The two work together to comprehensively cover construction site inspection scenarios, solving the problem of single data acquisition in existing inspection equipment. Furthermore, this invention combines eye tracking and a three-level device physical-semantic map to achieve precise positioning of target devices. Even with multiple candidate devices, it can quickly pinpoint user intent, improving the level of interactive intelligence.

This invention relates to the field of oilfield testing technology, specifically to a well test winch alignment device, comprising: a Christmas tree, with a blowout preventer fixedly connected to its upper end, and upper and lower ends of the blowout preventer respectively connected to a top pulley and a bottom pulley; a well test winch, with an adjustable skew steering drum device connected to its upper end, and steel wires overlapping the adjustable skew steering drum device, the top pulley, and the bottom pulley; the adjustable skew steering drum device includes a skew steering drum, a drum baffle, a skew steering drum shaft, a lateral movable joint, and a lateral adjusting rod. Through the overall structure of the equipment, by adding an angle-adjustable skew steering drum device between the well test winch and the wellhead, steel wires coming from different angles first bypass the skew steering device before entering the winch drum, thereby ensuring that the well test winch can be aligned with the wellhead at multiple angles during construction. Simultaneously, video monitoring and alarm devices are added to ensure the safety of the construction site.

The application discloses a construction site safety situation awareness method and system based on a deep learning network, relates to the technical field of building construction safety management, and comprises the following steps: acquiring multi-source sensing data of a construction site, extracting a multi-dimensional feature vector through a deep learning network; identifying a work stage based on a feature component; extracting a hazard source feature component and constructing an interaction matrix, and obtaining a coupled risk factor through coupling operation; calculating a current risk quantitative value according to a work stage identifier and a risk propagation rule; determining a stage evolution sequence and calculating a subsequent stage risk quantitative value to generate a risk evolution sequence; and generating an early warning signal when the risk quantitative value exceeds a safety threshold. The application realizes dynamic perception and prediction of the safety situation of a construction site, improves the accuracy and foresight of risk early warning, and provides an intelligent solution for construction site safety management.

This invention belongs to the field of safety monitoring technology, specifically disclosing an AI-based abnormal behavior recognition and early warning system for construction site safety supervision. Through an intelligent visual safety monitoring system comprising a monitoring server, data communication equipment, alarm terminals, and several monitoring cameras, it can collect monitoring images of personnel within corresponding work scenarios on the construction site. Based on the collected personnel monitoring images, it performs target key point detection and calculates key point spatial feature parameters to determine whether personnel in the work scenario exhibit abnormal behavior, thereby achieving targeted safety early warnings. This can improve the accuracy and real-time performance of abnormal behavior recognition in complex construction site scenarios, significantly reducing the false alarm rate. This invention can automatically achieve full coverage and uninterrupted long-term monitoring of abnormal behavior of construction site workers, saving inspection manpower, greatly improving the level of construction site safety management, and effectively reducing the incidence of construction site safety accidents.

This invention provides a steel pipe lifting device for disc-lock scaffolding, belonging to the field of construction technology. The main body of the lifting device includes a first column and a second column, the bottoms of which are fixedly connected by a crossbar to form an upward-opening U-shaped frame. A first positioning hole a is provided on the side wall of the second column facing the first column, and a second positioning hole b is provided below the first positioning hole a. One end of a first steel wire rope is fixed to the first column, and the other end of the first steel wire rope is provided with a lifting ring A. One end of a second steel wire rope is fixed to the top of the second column through the first positioning hole a, and the other end of the second steel wire rope is provided with a pin. This invention, by adding a fall protection safety device, makes the lifting of disc-lock scaffolding more standardized, reduces the difficulty of on-site safety management, greatly improves the safety index of the construction site, reduces the occurrence of hidden accidents, and also improves lifting efficiency and reduces labor costs.

The application relates to the technical field of safety management of communication engineering construction, and discloses a construction site safety risk intelligent identification and early warning method based on communication engineering. The method comprises the following steps: acquiring visual monitoring data, environment and equipment operation parameter data and equipment safety state logic quantity; time aligning and space correlating the visual monitoring data, the environment and equipment operation parameter data, the equipment safety state logic quantity and process state information, and inputting the data into a multi-modal fusion model; obtaining a hazard source set and a prevention and control barrier set corresponding to a current construction process through a knowledge graph, performing causal reasoning based on real-time verification results of the prevention and control barrier, and generating a graded early warning in combination with a fused risk characteristic value. The application realizes deterministic verification through construction of a safety state machine based on internal electrical signals of equipment, solves false alarm and missed alarm by fusing internal and external data through gate modulation, outputs hierarchical early warning in combination with causal reasoning of the knowledge graph process and parameter closed-loop regulation and control, and self-evolves rules.

This invention discloses a method and apparatus for identifying safety helmets at construction sites based on an independent training framework. The method includes using images of construction site workers as input to a complementary feature learning framework; performing convolution on the input images to obtain abstract representation information (conv); feeding conv into parallel feature extraction channels, an AttentionCNN model and an AttentionLSTM model, for feature learning; the AttentionCNN model outputs color information F1 about the safety helmet, and the AttentionLSTM model outputs contour information F2 about the safety helmet; merging the outputs of the AttentionCNN and AttentionLSTM models into F, where F = [F1, F2], and inputting F into a fully connected layer; and finally, inputting the output of the fully connected layer into a support vector machine to determine whether the safety helmet is being worn. The complementary feature learning framework proposed in this invention can learn richer information about safety helmets and has stronger robustness in safety helmet recognition tasks under complex scenarios.

This invention belongs to the field of computer vision and artificial intelligence technology, specifically relating to a helmet-wearing detection method in complex environments based on an improved YOLOv11. The method includes the following steps: acquiring a helmet detection dataset containing positive and negative samples, where positive samples represent helmet-wearing and negative samples represent helmetless / ordinary head wear, forming a complete and balanced helmet detection dataset; constructing a YOLO-MSF model, systematically optimizing it from three levels: feature extraction, feature fusion, and detection head optimization, significantly enhancing the model's detection performance in multi-scale, occluded, lighting variation, and dense target scenes; and obtaining the optimal model through setting training parameters and iterative training for inference and detection. This method can effectively adapt to the characteristics of construction site environments such as lighting variations, target overlap, and complex backgrounds, providing a feasible technical path for building an intelligent, low-cost, and highly reliable construction site safety supervision system.

This invention discloses a construction site safety risk identification method based on multimodal spatiotemporal alignment and causal hypergraph, comprising the following steps: feature extraction from real-time collected multimodal construction site data; spatiotemporal alignment and normalization of the multimodal features to obtain a normalized multimodal state vector, and constructing a causal hypergraph based on this vector; risk evolution inference under multi-causal synergy based on the causal hypergraph to obtain a comprehensive risk score; and accurate identification of the construction site safety risk status by comparing the comprehensive risk score with a preset risk threshold. This invention solves the problem of existing construction site safety risk identification methods based on deep learning models, which rely on statistical correlation between data for risk determination, only outputting risk identification results but failing to trace and explain the root causes of the risks, leading to a significant reduction in the credibility and interpretability of the risk identification results.

This utility model discloses a scaffolding device for preventing the collapse of stacked composite slabs, relating to the field of construction technology. The utility model includes: uprights, which are rectangular steel pipes with a base welded to their bottom. Multiple mounting holes are provided on the base for fixing the uprights to the ground using anchor bolts. The uprights are evenly distributed along the length and width of the stacked composite slab area, forming a rectangular frame structure; and multiple crossbeams, connecting adjacent uprights. This utility model, through the use of uprights and a rigid frame structure secured with anchor bolts, can resist the gravity and lateral forces generated by stacking composite slabs, preventing the scaffold from collapsing. Adjustable baffles and limiting holes effectively prevent lateral slippage of the composite slabs. Cross-bracing beams and triangular connecting blocks enhance the scaffold's resistance to torsion and lateral displacement, enabling it to cope with complex load conditions and comprehensively ensuring safety at the construction site.

The embodiment of the application provides a construction site safety supervision unmanned aerial vehicle system based on AI identification and real-time shouting, which comprises: an unmanned aerial vehicle platform, wherein the unmanned aerial vehicle platform is provided with a multi-modal sensor, the multi-modal sensor comprises a visible light camera, a thermal imager and a laser radar; an edge computing module integrated with an identification model, wherein the identification model is trained by combining an improved YOLOv5 algorithm with an attention mechanism, the identification model is used for real-time analysis on collected sensor data, identifies non-wearing of protective equipment, intrusion in a dangerous area and abnormal state of equipment, and generates structured data of a hidden danger type, a position and a risk level based on an identification result; a communication module, which adopts 5G and WiFi6 double-link transmission, synchronizes the structured data and real-time video stream to a cloud management platform, and receives a task instruction issued by the platform; and a directional audio device, which is triggered to project a voice alarm to a hidden danger occurrence area in response to the task instruction.

The application relates to the technical field of building construction safety monitoring, and particularly discloses a construction area safety hidden danger early warning system and method based on AI visual identification, which comprises an end-side collection layer, a side-side calculation layer, a cloud-side management layer and an application layer. The method comprises the following steps: image collection and transmission; image preprocessing and enhancement; dividing the construction area into grids and extracting quantifiable parameters; calculating the quantifiable scores of safety hidden dangers and determining the hidden danger grades by using a quantifiable evaluation model; triggering a graded early warning according to the grades; and closed-loop disposal management. The application establishes quantifiable evaluation models for different hidden dangers, calculates comprehensive quantifiable scores by using a multiplication fusion model, and realizes multi-hidden danger superposition risk evaluation. By means of accurate extraction of quantifiable parameters and a graded early warning mechanism, the application solves the technical problems of single identification dimension, lack of quantifiable evaluation and inability to form a closed-loop management in the prior art, and significantly improves the intelligent level of construction site safety management.

This invention relates to the field of construction site safety fencing technology, and particularly to a prefabricated, reusable, green construction safety fencing device for slopes. It includes several sets of fencing frames connected by universal joints. Several sets of protective netting frames are installed on the fencing frames. The anti-slip netting frames are fixedly connected to the fencing frames by bolts. Two sets of angled seat shells are installed on the rear end face of the anti-slip netting frames, and the head of the anti-slip netting frame is detachably and rotatably connected to the angled seat shells. An adjustable extension bracket is slidably installed at the lower end of each angled seat shell. This device adopts a fully prefabricated structure; all components can be prefabricated in the factory. On-site installation only requires splicing, eliminating the need for on-site pouring, significantly shortening the construction cycle. After installation, it can be transported to other construction sites for reuse, generating no construction waste, meeting the requirements of green construction development, and offering high reusability and reduced construction costs.

The application provides a method for safety analysis of power grid construction site by using intelligent video technology, which extracts personnel skeleton key point coordinate sequence frame by frame through deep learning skeleton point detection on the continuous video stream of the site, and then identifies each atomic operation action and accurately locates its start and end frame, so as to decompose the continuous video into a step sequence with timestamp; then the sequence is matched with the standard operation step set and its timing logic chain defined in the regulation knowledge base in advance, and the abnormal action of type inconsistency and the order violation of violating the sequence dependence relationship are automatically found by using the checking mechanism based on directed graph or timing logic; the abnormal points are further matched with the violation mode library to distinguish the known typical violation from the unknown new type of deviation, and finally it is determined whether the operation meets the safety requirements, realizing the safety compliance analysis of the whole process of the complex operation of the power grid site, and greatly improving the timeliness and accuracy of the violation discovery.