196 results about "Construction site safety" patented technology

The invention discloses a railway intelligent construction site safety penetration type management messenger platform which comprises a multi-modal data fusion processing module, an edge computing node cluster module, a three-dimensional visual penetration type management interface module, an intelligent early warning and emergency response module, a self-adaptive network transmission module and the like. Real-time cleaning, alignment and correlation analysis are realized by using a dynamic data calibration algorithm, and a data island is broken; the edge computing node cluster carries out localization preprocessing and the like on data in a key area, so that the load of a central server is reduced; the three-dimensional visual interface is based on a digital twinborn construction model, supports drilling type viewing and realizes three-dimensional monitoring; the intelligent early warning system adopts a reinforcement learning model to automatically trigger multi-channel early warning; and the adaptive network transmission module dynamically switches communication modes to ensure low-delay transmission of key data. The platform realizes real-time acquisition and integration of construction site data and reduces manual intervention.

The invention provides an intelligent management and control system for real-time monitoring and early warning of construction site safety risks. The intelligent management and control system comprises a data acquisition module, a data preprocessing module, a risk identification module, a risk assessment module and an early warning and management and control module. According to the invention, through deep fusion of multi-module cooperation and multi-algorithm, full-process, high-precision and intelligent management and control of the safety risk of the construction site are realized, and the construction safety management efficiency and the risk early warning capability are effectively improved.

The invention relates to the technical field of construction safety assessment, and discloses a construction safety assessment method based on multi-model fusion. The method comprises the following steps: acquiring a multi-source safety monitoring data set of a construction site, wherein the multi-source safety monitoring data set covers image sequence data, sensor time sequence data and operation log text data; performing parallel feature extraction processing on the multi-source data to generate an image feature vector, a sensor feature vector and a text feature vector; calling a multi-model fusion framework to carry out cross-modal fusion processing on the three feature vectors to obtain a fusion security feature set; carrying out security risk assessment analysis based on the fused security feature set, and generating a security risk prediction value and a high-risk region identifier; and finally, generating a security policy adjustment instruction according to the risk prediction value and the high-risk area identifier, and feeding back the security policy adjustment instruction to the field control system to trigger real-time intervention operation. According to the method, a high-risk area can be recognized in time, intervention is triggered, and intelligent support is provided for safety management of a construction site.

The invention provides a construction site safety supervision method and system based on multi-mode intelligent perception, and relates to the technical field of highway construction supervision. A construction area is scanned through a 5G camera, multi-mode intelligent sensing data such as videos, positioning and environment sensors are fused, a three-dimensional model of a construction site is constructed, and the positions of equipment and personnel are accurately determined. Beidou positioning and safe area setting are combined, real-time safety detection is carried out, illegal intrusion of external vehicles and personnel is effectively prevented, and construction of equipment and personnel outside a safe area is avoided. According to the method, illegal behaviors of not wearing safety helmets and the like are intelligently identified by using AI, post-accident tracing is realized through the Internet of Things, accurate traffic guidance information is published in combination with Atdes and Baidu maps, the collision risk and potential safety hazards are reduced, and the safety of a highway construction site is comprehensively improved.

The invention relates to a construction site hidden danger identification method and system based on a visual language large model. The method comprises the following steps: acquiring real-time acquired image data of high-place operation on a construction site; preprocessing the image data to obtain a to-be-detected image; inputting the to-be-detected image into the construction safety visual language model to obtain a hidden danger recognition result; the hidden danger recognition result comprises a high-altitude falling safety hidden danger type and standard natural language image subtitles. By adopting the method, intelligent technical support can be provided for safety management of the construction site, and the occurrence rate of high-altitude falling accidents is effectively reduced by identifying high-altitude falling hidden dangers of the construction site and generating standardized image subtitles.

The invention relates to the technical field of artificial intelligence, and provides a construction site safety detection method, device and equipment and a medium, which can utilize a mobile wearable portable camera to collect real-time images of a target construction site so as to provide comprehensive site pictures; the real-time image and the building information model are aligned according to the external parameters of the camera, it is ensured that follow-up detection is conducted on the same view angle and pixel, and the detection accuracy is improved; an improved Transform network architecture is used for carrying out difference detection on a real-time picture frame and a building information model frame, and the problems that manual inspection is not high in accuracy and low in efficiency are solved; and mapping the difference mask to a three-dimensional engineering coordinate system of the building information model according to the external parameters of the camera and the depth map of the real-time image to obtain a three-dimensional coordinate point, and identifying a difference component and generating a three-dimensional thermodynamic diagram according to the three-dimensional coordinate point so as to realize comprehensive, accurate and efficient safety detection of the target construction site.

The invention discloses a construction hidden danger automatic detection method and system based on point cloud and image multi-mode. The method comprises the following steps: step 1, acquiring point cloud data and RGB image data and respectively preprocessing the point cloud data and the RGB image data; 2, safety equipment detection is carried out by establishing an improved YOLOv5 model; establishing a 3D-CNN model to qualitatively judge the abnormal behavior of the personnel; 3, combining a semantic segmentation model and point cloud data processing to carry out quantitative judgment of height calculation and coordinate conversion of the fence; and 4, realizing three-dimensional live-action model reconstruction based on point cloud feature matching, Poisson surface reconstruction and GPU rendering, and detecting potential safety hazards of a construction site. The accuracy of qualitative detection is further improved, the precision error of quantitative detection is reduced, the recognition efficiency and precision of potential safety hazards are greatly improved, intelligent and all-around guarantee is provided for safety management of a construction site, remote decision is assisted, the management cost is reduced, and the method is suitable for various construction scenes.

The invention discloses a building construction safety monitoring system based on the Internet of Things technology, and the system comprises the following steps: collecting various types of data of construction personnel, equipment and environment, constructing a unified data sequence, extracting feature information through employing an incremental principal component analysis method, and further employing an elliptical envelope line algorithm by the system, the method comprises the following steps: establishing an anomaly discrimination model in a plurality of local feature spaces, realizing the recognition of abnormal behaviors, integrating noise suppression processing and a dynamic parameter updating mechanism by a system, improving the monitoring accuracy and adaptive ability, combining the states of personnel, equipment and environment on a construction site, generating a corresponding risk level and an early warning control instruction by the system, and improving the early warning efficiency. Real-time risk identification and response linkage is realized, and the safety guarantee level of a construction site is improved. The method has the key characteristics of multi-source data fusion, boundary adaptive updating, risk response linkage and the like, and intelligent monitoring and dynamic intervention of construction site risks are realized.

The application relates to the technical field of artificial intelligence, and provides a construction site safety detection method, device, equipment and medium, which can collect real-time images of a target construction site by using a mobile wearable portable camera to provide comprehensive site pictures; the real-time images and a building information model are aligned according to camera external parameters, so that subsequent detection is carried out on the same visual angle and pixels, and the accuracy of detection is improved; the improved Transformer network architecture is used for difference detection of real-time picture frames and building information model frames, so that the problem of low accuracy and low efficiency of artificial inspection is solved; the difference mask is mapped to a three-dimensional engineering coordinate system of the building information model according to the camera external parameters and a depth map of the real-time images to obtain three-dimensional coordinate points, and the difference components are identified according to the three-dimensional coordinate points and a three-dimensional heat map is generated, so that comprehensive, accurate and efficient safety detection of the target construction site is realized.

According to the invention, an unmanned aerial vehicle platform is creatively combined with the improved YOLOv11, and the unmanned aerial vehicle construction site safety helmet intelligent inspection method based on the improved YOLOv11 is provided. The method comprises the following steps: firstly, carrying out digital planning on an inspection route, and generating an executable flight path file; the unmanned aerial vehicle carries high-definition camera equipment, and carries out omnibearing video acquisition on a construction area according to a preset route; and the ground control center receives the real-time video stream data and inputs the real-time video stream data into a specially-designed safety helmet identification model to complete intelligent analysis of the state of personnel safety equipment and record of violation events. The detection model is subjected to triple optimization on the basis of a YOLOv11 architecture: a C3k2SPD module is adopted to replace a C3k2 module in an original backbone network, an ASPP module is introduced to replace an original SPPF module, and meanwhile, an NWD loss function is fused at a detection head part, so that the small target recognition capability and the complex scene fitness are remarkably improved.

The invention provides an intelligent construction site safety monitoring and early warning method and system, and the method comprises the steps: fusing the multi-source data, such as personnel positioning, equipment state and environment parameters, and building a structured time sequence feature set with spatial semantic annotation; constructing a symbolized rule base in combination with a field safety specification; applying a dynamic graph attention network, dynamically modeling personnel, equipment, environment nodes and an interaction relationship thereof, and deducing potential causal association strength between spatial entities; through path semantic distillation and anti-fact intervention, a compliant and accurate multi-stage causal reasoning path is formed, and key risk nodes and propagation channels are visualized in a three-dimensional BIM scene; and finally, dynamic early warning event generation and natural language interpretation are realized, model closed-loop optimization based on field feedback is supported, and the timeliness, interpretation and adaptive capability of construction site safety risk discovery are improved.

The invention belongs to the field of intelligent construction site construction safety management, and provides a construction abnormity allocation method and system based on the Internet of Things and artificial intelligence, and the method comprises the steps: collecting construction environment data, equipment states and historical data in real time, and carrying out the fusion to generate a dynamic risk score; executing dual-threshold early warning according to the dynamic risk score, and performing personnel matching according to an early warning result; and after personnel matching, generating an optimal scheduling path according to the physical distance between the matched personnel and the abnormal point. According to the scheme, multi-source sensor fusion and intelligent dynamic risk assessment are utilized, the system automatically identifies high-risk events and triggers the emergency process in real time, and compared with a traditional manual scheduling mode, the response time is greatly shortened, and it is ensured that key abnormal events are rapidly handled.

The invention provides a construction site safety pre-warning system based on AI model identification. The construction site safety pre-warning system comprises a multi-source sensing layer, an edge calculation layer, a pre-warning decision module, a data middle platform and a report center, the invention discloses a construction site safety early warning method based on AI model identification. The method comprises the following steps that S1, a multi-source sensing layer collects field state data in real time; s2, the edge calculation layer receives and processes the field state data, and sends the field state data to an early warning system core and a command large screen for real-time feedback; s3, the face recognition unit dynamically monitors personnel access, updates on-site personnel information, and counts individual working hours according to the on-site personnel information for management personnel to check; s4, an early warning decision module performs intelligent analysis by using a configured early warning model according to the field state data; and S5, if the early warning rule is triggered, the system immediately displays early warning details on the commanding and dispatching large screen and gives out an alarm sound. According to the invention, full-process digital management of second-level intelligent sensing and risk early warning and disposal of potential safety hazards on a construction site is realized.

The invention discloses a water conservancy construction site safety early warning system which comprises the components of S1, a multi-source heterogeneous sensor module which is used for outputting an original data stream with a timestamp; s2, an edge processing module which is used for generating a time sequence feature vector sequence; s3, a graph modeling module which is used for mapping each node in the weighted graph into a low-dimensional fusion feature graph; s4, a risk identification module which is used for realizing risk level inference through side attention weight adjustment; s5, a self-adaptive early warning module which is used for outputting a multi-level early warning signal according to a risk level inference result; s6, a response module which is used for controlling an audible and visual alarm, a field terminal and a remote platform to execute response operation according to the multi-stage early warning signal; and S7, a cooperative control module which is used for realizing data acquisition interval adjustment through sliding mean deviation control. The method has the advantages of being comprehensive in sensing dimension, low in response delay and high in risk adaptability.

The invention provides a construction site safety protection intelligent monitoring system based on AI image recognition, and relates to the technical field of safety protection. Comprising the following steps: collecting video image data and historical video image data of a construction site in real time; preprocessing the video image data to obtain preprocessed image data; obtaining illegal behaviors and potential safety hazards according to the safety identification and the target track; constructing a risk prediction model based on an improved AHP-TOPSIS algorithm, taking illegal behaviors as core evaluation indexes, dynamically correcting index weights through an entropy weight method, and outputting early warning information and risk diffusion prediction; and according to early warning information and risk diffusion prediction, triggering a layered response mechanism, and optimizing equipment control by adopting deep reinforcement learning. According to the method, the occurrence of accidents is effectively reduced through measures such as constructing a safety protection identification model by using the improved OfficientDet, the safety of personnel and equipment is guaranteed, and the overall construction safety management level is improved.

The invention provides a construction site safety training management and control method and system based on behavior recognition, and belongs to the technical field of construction safety training management and control. According to the invention, training legality is ensured based on dual authentication of keynote speaker biological feature verification and trainee historical record determination; synchronously acquiring voice transcription content with a time label and a head lowering and eye closing illegal behavior monitored in real time through a time sequence binding technology; according to the training text, dynamically generating examination question push examination containing counter example interference; generating an authority decision by adopting a triple verification mechanism of answer correct rate benchmark screening, answer question corresponding time period behavior concentration degree verification and trained state mark comparison; and finally, triggering an access control passing green light lightening instruction or intercepting a red light lightening alarm instruction in a second level, synchronously generating a targeted complementary training task binding an original teaching fragment and a real-time behavior record for forced learning, solving the problems of training assessment failure, cheating loopholes and control lag, and realizing real-time conversion of safety training effectiveness to a high-risk area physical authority to form a closed loop.

The invention discloses a non-inductive adaptation method, system and equipment for a construction site safety monitoring model, relates to the technical field of vision, and aims to solve the problems that an existing construction site safety monitoring technology is poor in model adaptability, needs a large amount of retraining to adapt to a new environment, and is poor in adaptability. According to the construction site safety supervision model non-inductive adaptation method and system, the problems that in the prior art, the construction site safety supervision model does not need to consume a large amount of cost to mark data again and perform training when being deployed to a brand new construction site are solved, and the construction site safety supervision model non-inductive adaptation method and system are provided. Therefore, the internal processing logic can be adjusted without perception to identify the target, the generalization ability and the deployment economy of the model are both considered, and the efficient and accurate safety supervision model non-inductive adaptation method suitable for the construction site is provided.

The invention discloses an electronic fence dynamic generation method and system for a construction site safety area, and relates to the technical field of intelligent construction site safety management, and the method comprises the steps: a system calculates a reference safety distance value based on the topographic data of a construction site and the coordinates of real-time operation equipment; the fence offset is dynamically calculated in combination with operation equipment parameters, and real-time data flow of an environment sensor is accessed; the offset and the environment data are fused, and multi-dimensional calibration is completed through environment influence factor correction calculation; generating a dynamic electronic fence with a gradient alarm function according to the calibrated safety boundary, and mapping the dynamic electronic fence to a digital map of a monitoring platform; and fence data are distributed to a terminal alarm device through an Internet of Things communication protocol, so that closed-loop safety management is realized. According to the invention, the accuracy and real-time performance of construction site safety management can be effectively improved, the accident rate is reduced, and the safety level of construction site operation is remarkably improved.

The invention discloses a construction site safety supervision agent system based on a multi-modal large language model. The construction site safety supervision agent system comprises a multi-modal fusion module, a task analysis module and a hybrid execution engine. The multi-modal fusion module extracts multi-modal features such as images / videos and acoustics, and the multi-modal features are aligned to an LLM semantic space through a projection layer; the task analysis module analyzes the alignment features to generate a task graph (nodes are sub-tasks, and edges are dependency relationships); and the hybrid execution engine dynamically fuses the features according to the task graph and drives the execution of the subtasks through a gating attention mechanism. Cross-modal semantic alignment is realized through multi-modal and LLM deep fusion, the complex task understanding ability is improved through task graph structured analysis, gating attention dynamically deploys feature resources, the execution efficiency and flexibility of the system are enhanced, the method is suitable for space task automatic processing of scenes such as intelligent robots and smart homes, and the method has wide application prospects. And an efficient scheme is provided for constructing a multi-modal interaction and autonomous decision-making agent.

The invention relates to the technical field of digital twinning, in particular to a construction process digital twinning online deduction method and device.The method comprises the steps that a structured knowledge base covering safety risks, potential quality hazards and a process dynamic interaction mechanism is constructed, and a core knowledge basis is provided for generation of scheduling responses; aiming at the uniqueness of construction process scheduling, a reinforcement learning network special for dynamic scheduling of the construction process is designed, and intelligent and near-real-time construction scheduling decision is realized; aiming at the problem that a reinforcement learning model is difficult to effectively learn and respond to sudden construction disturbance, an adaptive model training method is researched, so that the model can learn how to dynamically adjust a process network when disturbance occurs, and the influence caused by process adjustment is reduced as much as possible while an engineering construction target is ensured. Therefore, the problems that most of dynamic change elements of the construction site considered in the prior art are resource change, weather change and the like, and response to events such as safety problems or potential quality hazards of the construction site is not achieved are solved.

The invention provides a construction site safety management and control method and system based on enhanced retrieval generation, and belongs to the technical field of data processing. Comprising the following steps: constructing a construction safety knowledge base, and vectorizing text data in the knowledge base; field self-adaptive fine tuning is carried out on the visual language large model through a low-rank self-adaptive method, and perception, cognition and decision-making processes of construction site safety management and control are executed: in a perception stage, construction safety hidden dangers are dynamically identified and positioned, and violation descriptions containing hidden danger types are generated; in the cognition stage, an enhanced retrieval generation process is started, and construction specification information associated with hidden dangers is obtained through multi-level semantic matching and retrieval; in a decision-making stage, based on violation description and construction specification information, a visual language large model is driven to automatically generate a structured security log through a preset structured cue word template. According to the invention, unsafe behaviors of the construction site can be timely and accurately warned, and effective data support is provided for the safety of the construction site.

The application relates to a construction site safety management method and system, an access control system and a safety helmet, and belongs to the field of engineering construction. After a construction personnel issues an access request to an access control system of a construction site, the access control system cooperates with the safety helmet of the construction personnel to understand the current wearing condition of the protective tool of the construction personnel, and processes the access request of the construction personnel based on the wearing condition of the protective tool, so that the construction personnel who do not wear the protective tool in a qualified manner can be prevented from entering the construction site, and in turn, the safety hazards caused by the non-compliance of the wearing of the protective tool can be reduced. Moreover, the safety helmet is the most basic and common protective tool, a radio frequency card reader is arranged on the safety helmet, the construction personnel do not need to carry an independent radio frequency card reader, the burden of the construction personnel is reduced, and the radio frequency card reader can also ensure that the radio frequency tags on other protective tools worn by the construction personnel can be normally read.

An AI-based construction site safety monitoring and emergency response system belongs to the technical field of high-altitude operation and artificial intelligence, and comprises a plurality of high-definition cameras and environment adaptive sensors, a real-time monitoring platform, an emergency response module and a site command module. The high-definition camera and the environment adaptation type sensor meet the industrial grade standard, the design of a protective cover, a wind-resistant and rain-proof shell and an anti-seismic fixing structure is adopted, and the installation positions are arranged according to the high-altitude operation characteristics. The real-time monitoring platform is formed by constructing a digital twinborn model integrating high-altitude monitoring data and a construction site BIM model based on an environment adaptive sensor and a high-definition camera, and combining with an intuitive graphical interface. The emergency response module is linked with the on-site command module, and when the real-time monitoring platform monitors that the equipment is abnormal or the environment is suddenly changed, the emergency response module and the on-site command module are triggered to send out a high-altitude operation personnel evacuation prompt, an equipment maintenance instruction and on-site rescue scheduling.

The present application relates to the technical field of construction safety detection, in particular to a construction site safety risk non-inductive inspection method and system. The method comprises the following steps: reading historical inspection image data of a preset inspection route, and marking risks on the historical inspection image data; establishing a risk identification template according to the historical inspection image data with risk marks; receiving real-time inspection image data of the preset inspection route; comparing the real-time inspection image data with the risk identification template to obtain a risk identification result, effectively reducing the inspection operation process of construction personnel and reducing the complexity of inspection operation; sending the risk identification result to the person in charge, and converting the real-time inspection image data into a storage hash value, storing the risk identification result and the storage hash value, avoiding tampering with the inspection result by others, and improving the supervision effectiveness of the construction site safety inspection.

To provide an object identification method capable of monitoring an abnormal operation of a construction image in real time, saving labor costs required for safety monitoring at a construction site, improving an accuracy rate of the safety monitoring at the construction site, and reducing an occurrence rate of safety accidents.SOLUTION: The method includes: collecting a construction image; recognizing the construction image based on a target detection model to obtain a recognition result, the recognition result including a category of a construction object and location information of the construction object; and determining whether the construction image is associated with an abnormal action based on the recognition result. The ith feature segmentation module in the feature segmentation network performs convolution processing and segmentation processing on the ith type of first image feature, and outputs the jth type of sub-image feature to the jth feature fusion module connected to the ith feature segmentation module.SELECTED DRAWING: Figure 2

The invention discloses a bimodal construction scene target detection and tracking method, which provides a comprehensive and accurate data basis for detection and tracking, reduces missing detection and false detection, and improves the detection accuracy by acquiring bimodal videos, mutually complementing data and enriching target information features. A target detection and tracking model is independently constructed according to the bimodal video, so that detection and tracking are more professional and efficient, and the tracking stability and accuracy are enhanced; collecting video stream data in real time and inputting the video stream data into the model to ensure that the system timely obtains latest dynamic information; detection and track results are input into a fuzzy control system, key indexes are selected as input variables, multiple factors are comprehensively considered, and by reasonably constructing related elements and accurately judging the mechanical collision risk level, a reliable and accurate decision basis is provided for construction safety management, and the safety of a construction site is powerfully guaranteed.

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.

The utility model relates to a construction site safety warning device which comprises a base, a warning board and a folding mechanism, two supporting frames are rotated out of the base through rotation, one supporting frame is pulled to be away from the other supporting frame, at the moment, a crossed combination rod deforms, a sliding seat slides in a sliding groove, the distance between the two supporting frames is adjusted, and the warning board is folded. Two clamping blocks at the bottom of the warning board can be inserted into two clamping grooves respectively, two extension rods are rotated out, the two clamping blocks can be inserted into the two clamping grooves respectively, first bolts penetrate through the ends of the extension rods and are in threaded connection with mounting holes in the clamping blocks, the warning board is fixed to the ends of the two extension rods, and at the moment, a supporting rod is rotated out, so that the warning board is fixed. The lower end of one supporting rod abuts against the interior of the base, the other supporting rod is located at the supporting table, second bolts in the supporting table and slotted holes in the supporting rods are installed in a threaded mode, the supporting rods are fixed, the supporting rods, the supporting frame and the base form a triangle, and installation and use of the warning board are achieved.

The utility model provides a construction site safety propaganda device, which belongs to the technical field of construction and comprises a propaganda display board, a main support frame hinged to the top of the propaganda display board, a storage groove formed in the bottom of one side of the main support frame, an auxiliary support frame hinged to the top of the inner wall of the storage groove, and support bottom plates hinged to the bottoms of two sides of the main support frame. A first spring pin is arranged in the middle of the supporting bottom plate, a first limiting hole is formed in one side of the bottom of the auxiliary supporting frame, the first spring pin is connected into the first limiting hole in an inserted mode, a second spring pin is arranged on one side of the supporting bottom plate, second limiting holes are formed in the two sides of the bottom of the propaganda display board, and the second spring pin is connected into the second limiting holes in an inserted mode. According to the propaganda device for the safety of the construction site, the propaganda display board and the main supporting frame are unfolded and supported by turning over the propaganda display board and the main supporting frame and making contact with the ground, an inner supporting structure and an outer supporting structure are formed, and the wind resistance of the whole device is improved.