1991 results about "Safety risk" patented technology

The invention provides a mine abnormal event real-time identification method and system based on time sequence characteristics, and relates to the technical field of mode identification, and the method comprises the steps: carrying out the time-space alignment and semantic annotation of multi-modal monitoring data, and constructing a time sequence knowledge graph; calculating a dynamic association weight between entities, and analyzing a risk propagation path; predicting a risk situation based on a historical evolution rule; and dynamically generating a differential early warning strategy and establishing a closed-loop tracking system. According to the invention, early identification, accurate prediction and efficient disposal of mine safety risks can be realized, and the mine safety management level is improved.

The invention relates to the technical field of safety early warning, and particularly discloses a vehicle driving safety early warning method fused with meteorological data, which comprises the following steps: acquiring real-time multi-modal data of meteorological, traffic flow and vehicle state of a target road area, performing exception handling, space-time alignment and standardization to form a standardized data sequence, then constructing a multi-modal fusion tensor, and finally performing data fusion on the multi-modal fusion tensor. Extracting each modal dynamic mode, fusing cross-modal features, outputting a joint feature vector, inputting the joint feature vector into a safety risk prediction model to calculate a dynamic safety risk value, combining digital twin simulation risk conduction, generating graded early warning according to a preset threshold value, and performing management and control through vehicle-road collaborative network publishing and high-risk scene linkage traffic facilities. And finally, collecting feedback data evaluation effects, associating decision data to generate hash records, recording the hash records in the block chain, and carrying out federated learning incremental training optimization model based on feedback. According to the invention, accurate early warning under multi-factor coupling can be realized, data privacy is guaranteed, closed-loop optimization is formed, and road traffic safety and stability are improved.

The invention discloses a maritime accident prediction method and device based on interpretable integrated machine learning, and relates to the technical field of maritime affair safety risk analysis, and the method comprises the steps: obtaining accident investigation data, carrying out the preprocessing, balancing the data through a ten-fold layered oversampling method, and carrying out the cross verification training, and determining a performance optimal model by using the test set and carrying out interpretable analysis to explain the influence of the characteristics on the accident prediction result. By constructing a closed-loop'data processing-model optimization-explanation output 'process and adopting SMOTE oversampling and ten-fold layered cross validation training and a heterogeneous base model ensemble learning strategy, the processing capacity of the data imbalance problem of accident categories is improved, the data leakage problem of oversampling is avoided, and the possible bias of a single model is overcome. The interpretability analysis of the model prediction result can quantitatively display the contribution degree of each feature to prediction globally and locally, reveal the nonlinear relationship and interaction effect between the features, and provide transparent interpretation of model decision.

The invention discloses an unmanned aerial vehicle inspection method and system applied to foundation pit accumulated water monitoring, and the method comprises the steps: determining an optimal route node sequence, and generating an unmanned aerial vehicle control instruction set; acquiring internal and external parameters of a multi-source sensor, and performing time alignment compensation on image frames and sensor data; acquiring a ponding area image coordinate set and attribute data; a three-dimensional projection point set of the ponding area is obtained, and the real coverage area of the ponding area is calculated; generating a three-dimensional overview map of the foundation pit by using the multi-view image of the foundation pit area; determining a three-dimensional coordinate point set and an area estimation result of the ponding area; and projecting the three-dimensional coordinate point set of the ponding area to the three-dimensional overview map of the foundation pit to generate a hidden danger monitoring report. By integrating the unmanned aerial vehicle, the sensor data and the advanced image processing technology, the accuracy, stability and visualization effect of foundation pit accumulated water monitoring are greatly improved, more reliable and comprehensive technical support is provided for safety management of foundation pit engineering, and the safety risk in the construction process is remarkably reduced.

The present application relates to the technical field of safety management, in particular to a mine air compressor safe operation management method and system based on deep learning, comprising the following steps: collecting output pressure and input current, calculating slope change rate and direction switching frequency, clustering fluctuation amplitude and frequency by time period, generating working condition fluctuation label, identifying state mutation point and duration, calculating risk level label, analyzing load fluctuation coefficient, judging periodic fatigue early warning, combining synchronous rate to identify abnormal section, judging the need to switch state and generating instruction set. In the present application, by constructing the load risk level sequence on the basis of state duration and mutation point identification, the quantitative identification of multi-period load deviation is realized, the early warning period is marked by combining risk level and fatigue deviation ratio, and the abnormal load trend is judged according to the change direction of pressure and return gas flow, which strengthens the capture ability of abnormal operation section and improves the accuracy of operation safety risk identification and the adaptability of response strategy.

The invention provides a time-space evolution data processing method and system applied to tunnel deformation analysis, and the method comprises the steps: obtaining a time sequence monitoring sequence of different monitoring dimensions in a tunnel construction process, constructing a time-space evolution correlation model of tunnel deformation based on the time sequence monitoring sequence, and analyzing a dynamic coupling relation between structure response data and geological environment action data, generating a space-time correlation feature matrix, generating a feature evolution sequence of tunnel deformation based on the space-time correlation feature matrix, and performing nonlinear mutation feature extraction on the feature evolution sequence to obtain a mutation feature index set; according to the sudden change characteristic index set and a preset accumulated deformation rate threshold value, performing dynamic division of tunnel deformation safety levels, generating a safety level division result, and based on the safety level division result and the spatial distribution data of the tunnel construction section, executing spatial positioning and boundary contour extraction of the potential risk damage area. And obtaining a risk damage area distribution map. According to the invention, the tunnel construction safety risk can be accurately evaluated and effectively controlled.

The invention relates to the technical field of computer application, and particularly discloses a chemical industry park safety risk assessment method based on an agent model. The method comprises the following steps: constructing a multi-agent collaborative perception framework, and fusing multi-source heterogeneous data of equipment, environment, materials and personnel to form a park panoramic view under a unified space-time reference; establishing an agent behavior model library embedded with the chemical process mechanism and the safety interlocking logic; an intelligent agent decision strategy is optimized through deep reinforcement learning, and multi-target training is carried out by taking a safety index as a reward function; executing dynamic risk deduction and accident chain simulation in a digital twin environment, and quantifying risk probability and consequences in combination with Monte Carlo sampling; and finally, a visual thermodynamic diagram and an interpretable risk assessment report are generated, weak links are identified, and intervention suggestions are provided. According to the technical scheme, dynamic, accurate and prospective evaluation of the safety risk of the chemical industry park can be realized, and the early warning timeliness and the evaluation reliability are remarkably improved.

The invention relates to the technical field of data analysis and control, in particular to a hydraulic climbing frame safety control method based on digital monitoring, which comprises the following steps: acquiring monitoring data in real time through a sensor group arranged on a hydraulic climbing frame, preprocessing and analog-to-digital conversion are carried out on the monitoring data, then the data are formed and sent to a remote monitoring center through a wireless transmission technology, and the preprocessed monitoring data are input into a pre-stored structure safety evaluation model and a hydraulic system performance evaluation model so as to be analyzed in real time; analyzing the hydraulic climbing frame, judging a safety characterization state of the hydraulic climbing frame according to an analysis result, determining a safety risk type based on an abnormal state of the hydraulic climbing frame, sending early warning information through a user interaction platform when a risk is judged to exist, and executing a corresponding safety control instruction; automatic real-time monitoring, intelligent early warning and active control of the safety state of the hydraulic climbing frame are achieved, and construction safety and operation reliability are remarkably improved.

The invention discloses a power transformation and distribution station room operation method and device based on a force control dexterous hand, and belongs to the technical field of robots. According to the technical scheme provided by the embodiment of the invention, the distributed multi-source sensor array is utilized to obtain the consistent environment data, the operation situation description is dynamically generated to adapt to the environment change, the complete operation scheme is intelligently generated in combination with the cross-domain strategy knowledge graph, and real-time feedback is performed through the second multi-mode sensing data flow acquired during control, so that the control accuracy is improved. The problems of low efficiency and safety risk coexistence in the operation of the power transformation and distribution station room are solved.

The invention discloses a building construction safety monitoring and early warning system based on the Internet of Things, and relates to the technical field of construction safety monitoring, and the system comprises an intelligent sensing module which generates a multi-source heterogeneous safety monitoring data flow with a space-time label; the risk research and judgment module is used for performing deep feature extraction and cross-modal fusion analysis on the multi-source heterogeneous safety monitoring data stream, and outputting a refined regional risk level quantized value by using a trained construction safety risk assessment model; the dynamic early warning module is used for intelligently matching a dynamically updated early warning strategy library based on correlation analysis of a regional risk level quantized value and a construction process time sequence; and the security center module is used for storing non-tampered historical monitoring data and early warning records by adopting a distributed block chain storage architecture. According to the scheme, adaptive sampling, cross-modal risk assessment, hierarchical early warning linkage and block chain and knowledge graph management technologies are fused, and the whole-process intelligent and precise upgrading of building construction safety from data acquisition to decision support is realized.

The utility model relates to the technical field of gas extraction, in particular to an underground gas extraction blowout preventer, and mainly solves the technical problems that the existing gas extraction blowout preventer is high in spewing control difficulty, high in safety risk and high in sprayed slag cleaning workload. The device comprises a movable chassis, a deslagging box, a multi-way pipe, a drilling machine through pipe, a coal seam drilling pipe, a negative pressure pipe, a pressure relief buffer cover, a deslagging pipe and a pressure sensor. According to the device, gushing is detected through the pressure sensor, on-off of the negative pressure pipe and the slag discharging pipe is controlled in a feedback mode, dependence on manual experience is not needed, it can be guaranteed that gushing and corresponding equipment are started consistently, and the gushing control difficulty is greatly reduced; meanwhile, water and residual materials for slag spraying can be discharged into the slag discharging box through the slag discharging pipe in time to be collected, and the workload of slag spraying and cleaning is reduced; the device is further provided with a pressure relief buffer cover, when the pressure is too large due to gushing, buffering can be conducted through the pressure relief buffer cover, and damage to the device caused by the too large pressure is avoided.

The invention provides a driving authority management method and device based on biological feature verification and a medium, and belongs to the technical field of vehicles. The method comprises the steps of collecting facial features, voiceprint data and driving behavior data by detecting a starting operation of a current driver; the identity of the current driver is recognized by using a multi-modal biological recognition algorithm, and the accuracy and anti-counterfeiting capability of identity verification are improved by adopting a bimodal fusion scheme of face recognition and voiceprint recognition; when it is determined that identity recognition of the current driver succeeds, a driving account is determined to judge whether the driver has the use permission or not, a binding relation of driver identity-account-function permission is established, and accurate matching of the intelligent driving permission and the driver qualification is achieved; the open state of the intelligent driving function is dynamically controlled based on a permission verification result, if the permission exists, the intelligent driving function is automatically prepared, if the permission does not exist, the function is locked, a clear prompt is given, and the safety risk that the intelligent driving function is used before being learned is avoided from the source.

The invention relates to a method, a system and equipment for intelligently identifying unsafe behaviors of coal mine operating personnel and a medium. The method comprises the following steps: extracting individual trajectory data from an underground video stream through a multi-target tracking algorithm, decomposing continuous actions into an atomic behavior sequence with a space-time mark by using attitude estimation and a space-time diagram convolutional network, and capturing space-time relevance of a behavior chain through an attention mechanism enhanced long and short term memory network to generate a feature vector; risk reasoning is carried out in combination with the coal mine safety knowledge graph to predict the risk event type and probability, and finally early warning information is generated based on a multi-level early warning strategy. According to the scheme, the crossing from isolated action recognition to behavior chain risk prediction is realized, and the early warning capability of potential safety risks in coal mine operation is remarkably improved through fusion of space-time correlation analysis and domain knowledge of the behavior sequence.

The invention belongs to the technical field of intelligent manufacturing and production line operation safety control, and particularly provides an intelligent production line-oriented modular safety protection and operation guarantee system, which comprises a multi-dimensional state sensing modeling module, an intelligent production line operation area multi-source safety state parameter acquisition module, an intelligent production line operation area multi-source safety state parameter acquisition module and an intelligent production line operation area multi-source safety state parameter acquisition module, constructing a dynamic security situation data set covering the whole operation period of the production line; the security risk factor evaluation module is used for constructing a security risk factor based on the dynamic security situation data set and calculating a security risk factor growth rate index; the control instruction generation decision module is used for generating a control instruction oriented to the physical protection execution linkage module; and the physical protection execution linkage module is used for triggering a multi-mode emergency response mechanism and physical isolation based on the control instruction to complete modular safety protection and operation guarantee of the intelligent production line. According to the technical scheme, key guarantee is provided for high-reliability and high-toughness operation of an intelligent production line.

The invention relates to the technical field of power grid dispatching, in particular to a power grid dispatching operation ticket intelligent generation and risk pre-judgment system based on multi-modal data fusion. The method comprises the following steps: a weather-time window mapping unit associates and identifies weather data on that day, and judges the rationality of planned operation time and steps by establishing a mapping relationship between a meteorological condition and an operation time window and utilizing a meteorological influence factor matrix; after the historical operation ticket analysis unit confirms meteorological condition operation, information is extracted from the meteorological condition operation through the NLP technology, and a set of standard operation ticket template library is constructed; the four-dimensional risk association unit establishes a four-dimensional risk association model based on power safety regulations, historical accident cases and equipment defect data. According to the design of the invention, through multi-dimensional data fusion technologies such as meteorological-time window mapping, four-dimensional risk association analysis and dynamic topology simulation, safety risks existing in the operation process can be comprehensively identified and evaluated.

The invention relates to the technical field of risk assessment, in particular to a multi-modal information coupled chemical safety risk assessment system which comprises a multi-modal data fusion module, a dynamic knowledge graph construction module, a risk assessment model training module, a risk assessment and judgment module, an operation specification mapping module and a control instruction generation module. Fusing the equipment process and operation data of chemical production with preset safety expert knowledge to generate multi-modal coupling information; constructing a dynamic knowledge graph representing a flow node dependency relationship and a risk evolution path, and cooperatively training and updating a whole-flow risk evaluation model; coupling information is input to complete risk assessment and grade judgment, a result is mapped to an operation specification knowledge base and then cooperatively encoded with a distributed control operation instruction, and a standardized control instruction is output; according to the invention, the efficiency of chemical safety risk assessment based on multi-modal information coupling can be improved.

The invention relates to the technical field of electrical wiring. The invention discloses a method for generating a cable wiring path of a power distribution cabinet, and the method comprises the steps: firstly obtaining the multi-dimensional environment parameters of a to-be-wired power distribution cabinet, wherein the structural parameters comprise the three-dimensional layout in the cabinet, the positions of components and wire duct guide rails, the electrical parameters comprise the connection relation of the components, the cable specification and the like, and the physical constraint parameters are the allowable bending radius and the minimum electrical gap; constructing a digital twinborn model according to the parameters, and performing risk analysis on the model to obtain a safety risk level; and finally, a double-path scheme is generated and output in combination with the model and the grade, the first path is the optimal path of the prediction model, the second path is the standby path of the historical case, and the priority of the first scheme is higher. According to the method, the security risk is quantified in advance through multi-dimensional parameter acquisition, digital twin modeling and risk assessment. The current situation that traditional manual wiring depends on experience is changed through intelligent generation of an optimal path, reuse of a standard scheme of a standby path and real-time monitoring and early warning.

The invention discloses a vision-based industrial safety production full-cycle monitoring method and system, and belongs to the technical field of industrial safety, and the method comprises the steps: collecting the visual state data and interaction information of a production scene entity in real time, and generating an initial risk conduction link in combination with a pre-constructed working condition rhythm safety risk topological map; based on the initial risk conduction link, adopting a hidden Markov algorithm, a forward diffusion model and a Bayesian model to respectively obtain forward and reverse risk path sequences; inputting the forward and reverse risk path sequences into a risk monitoring map, and outputting risk probability, type and path characteristics through analogue simulation and risk threshold judgment; positioning a core risk node according to the path characteristics, and calling a matched initial prevention and control strategy; generating a secondary prevention and control strategy through an analogue simulation strategy effect, and carrying out iterative optimization until the risk probability is reduced to a safety threshold value; according to the invention, dynamic perception and accurate traceability of industrial production risks are realized, and the real-time performance and reliability of safety production monitoring are improved.

The invention relates to the technical field of robot control, in particular to a self-adaptive joint control method and system for a humanoid robot. Comprising the following steps: acquiring relative motion information, motion state information and internal mechanical state information to form comprehensive sensing data; performing contact prediction based on the comprehensive perception data, completing contact risk judgment and determining a potential contact area; and in response to the contact prediction result, a softening control instruction is sent to a joint driver in the potential contact area through a rapid instruction channel independent of a conventional trajectory position control process, so that the joint is adjusted to a low-rigidity state from a high-rigidity state within a preset response time. The problems of safety risks and equipment damage possibly caused by accidental contact between the humanoid robot and the outside when the humanoid robot executes a high-precision assembly task in a high-rigidity control state are solved, and the technical problem that an existing safety protection mechanism lags in response is solved.

The invention discloses a system for detecting blockage of a blanking port in a dry coal shed and dredging by an unmanned grab crane, which comprises a data processing module for acquiring and preprocessing a video stream of the blanking port and generating a core area image of the blanking port; the feature definition module extracts multi-dimensional feature vectors such as the falling speed, the flow density, the contour integrity and the gray scale stability of the falling material; the anomaly detection training module selects and trains an anomaly detection model based on the feature vector; the abnormity judgment module judges material blockage through multi-feature fusion; and the dredging control module controls the grab crane to execute a dredging program when materials are blocked. The problems of high risk and low efficiency of manual operation are solved, and unmanned operation is realized, the efficiency is improved and the safety risk is reduced by constructing a normal blanking model to identify abnormity and automatically dredge.

The invention relates to an intelligent control method, system and device for an industrial robot and a medium. The method comprises the steps that high-dimensional coding is conducted on the environment and the body state of the robot to generate a unified hidden state vector; generating a task-driven original control instruction through a deep reinforcement learning network based on the hidden state vector, predicting the safety risk of the instruction through a safety network in a prospective manner, and outputting safety measurement and gradient data; when the risk is predicted to exist, constructing and solving an optimization model taking minimization intervention as a target, and calculating to obtain a safety correction value; fusing the correction value and the original instruction to generate a final safety control instruction and executing the final safety control instruction; and updating the security prediction network online by using the executed real security feedback. The method solves the problems that in existing deep learning control, the decision risk is unpredictable in physical deployment, and a traditional safety protection mechanism extensively interrupts tasks, and the intelligent decision-making ability of the robot is reserved to the maximum extent while absolute safety is guaranteed.

The invention provides a parking lot safety operation intelligent monitoring and self-adaptive scheduling management platform based on cloud edge cooperation AI, and the platform comprises a data analysis and alarm module which carries out the analysis of the behaviors of a driver and the surrounding environment of a vehicle based on an AI model disposed on a parking lot intelligent terminal, and carries out the instant alarm when an abnormal driving behavior or a safety risk exists; the global analysis index determination module is used for uploading the behavior recognition result obtained through analysis and the time series data of the environmental risk information to the cloud platform, and aggregating and mining the time series data of each parking lot vehicle in the space region based on the cloud platform to obtain the global analysis index of the working efficiency and the safety situation of the parking lot vehicle; and the scheduling management module dynamically generates a task scheduling instruction and a path planning instruction of each parking lot based on the global analysis index, and issues the task scheduling instruction and the path planning instruction to the parking lot intelligent terminal for cooperative management of operation behaviors. The operation safety and the operation efficiency are remarkably improved, and human errors and resource waste are reduced.

The invention discloses a steel structure construction safety risk dynamic assessment method and system based on artificial intelligence. The method comprises the steps of obtaining a multi-dimensional data set in real time; based on the multi-dimensional data set, a deep learning algorithm is adopted to fuse an image and a sensor signal, a structure defect is accurately identified, and a defect feature vector is extracted; when the defect feature vector exceeds a preset threshold value, a machine learning classifier is introduced to construct a risk assessment model, classification quantification of defects is realized, and an initial risk level is output; for the initial risk level, analyzing the space-time fusion data by using a probability state transition model, simulating the dynamic evolution trend of the risk and judging the stability of the risk; a mapping model is constructed based on a virtual simulation technology, dynamic simulation deduction is carried out on risk changes, and a visual visualization report and a graded early warning signal level are generated. According to the invention, full-chain intelligent closed loop from defect perception to risk prospective early warning is realized, and the safety management and control level and accident prevention capability of a construction site are greatly improved.

The invention provides a safety risk control method and system for a shield tunnel to underpass a highway roadbed, and relates to the technical field of shield construction risk control, and the method comprises the steps: obtaining three-dimensional geological parameters, shield tunneling parameters, roadbed layering characteristics, historical settlement data, traffic load data and meteorological and hydrological data; modeling is carried out according to the three-dimensional geological parameters, roadbed layering characteristics and shield tunneling parameters, and structural response characteristic data are generated; carrying out evolution prediction according to the structural response characteristic data, the traffic load data and the historical settlement data, and generating a multi-stage deformation prediction field; performing risk analysis according to the deformation prediction field and the meteorological and hydrological data to obtain joint probability distribution; and generating a risk control strategy sequence based on the joint probability distribution. According to the method, based on a co-evolution mechanism of dynamic traffic load frequency and meteorological and hydrological disturbance, multi-stage prediction of roadbed long-term settlement and segment damage cumulative effect is realized, and spatial-temporal resolution limitation of deformation field prediction under a static load assumption is broken through.

The invention provides a power station safety management and control method and system based on multi-modal sensing, and relates to the technical field of power station intelligent management. According to the method, multi-modal sensing data of real-time operation of a power station are synchronously collected through heterogeneous sensors deployed in a predetermined area of the power station with a unified time reference, space-time alignment is conducted, feature vectors are extracted, and the feature vectors are extracted; carrying out fusion analysis by adopting an evidence theory, and calculating and outputting a global risk confidence coefficient and a dominant risk type identifier which represent the safety state of the power station; inputting the global risk confidence and the dominant risk type as boundary conditions into a high-fidelity digital twinborn model, performing finite-time event deduction based on a physical rule embedded in the digital twinborn model, predicting a risk evolution path and a deduction result, and calculating a quantified comprehensive safety risk level; if the comprehensive safety risk level exceeds the preset threshold value, a linkage instruction containing the target three-dimensional coordinates is automatically generated, cross-system co-processing actions are triggered, and the safety operation and maintenance level of the power station can be remarkably improved.

The invention relates to the technical field of intelligent construction, and discloses a building machine jacking system based on operation state data. The invention aims to solve the technical problems of high safety risk and low maintenance efficiency caused by extensive monitoring means, dependence on a single threshold value and lack of early warning capability of an existing building machine jacking system. According to the method, the multi-dimensional heterogeneous data sensing network is constructed, multi-dimensional operation data are deeply fused, and accurate evaluation and prediction of the health state of the jacking system are achieved. The core of the method is that a health model capable of dynamically representing an ideal running track of equipment is established through an initial calibration and self-learning mechanism, and closed-loop management from accurate capture of early abnormality to automatic execution of a hierarchical response strategy is realized. By constructing an active predictive equipment health management system, the safety and stability of the jacking operation of the building machine are remarkably improved, and the scientificity of equipment maintenance decision and the refinement level of full-life-cycle management are greatly enhanced.

The invention relates to the technical field of combined machining, and discloses a five-axis five-linkage turning, milling and drilling combined machining center which comprises an equipment box, a mounting frame is mounted on the rear side of the inner wall of the bottom end of the equipment box, an automatic tool changing system is arranged on the left side of the mounting frame, and a transmission system is arranged in the middle of the mounting frame. A feeding table is slidably mounted on the front side of the inner wall of the bottom of the equipment box through sliding rails. The five-axis five-linkage turning, milling and drilling combined machining center can effectively solve the problems that in the prior art, a five-axis turning, milling and drilling combined machining center mostly adopts the mode that two sets of clamps are matched, parts need to be repeatedly clamped in the machining mode, the machining period of a single piece is directly prolonged through repeated positioning operation, and machining efficiency is high. The clamping precision and the consistency of the two sets of clamps cannot be guaranteed; in the prior art, a worker needs to stretch the upper body into a closed machining area of equipment and manually righting a part to enable the part to be aligned to a clamping hole position and keep a horizontal state, and the process is tedious in operation steps and has safety risks.

The invention relates to the technical field of truss building construction, and discloses a large-span truss construction method which comprises the following steps that fringe trusses are fixed to steel pipe concrete columns at the two ends respectively; masts are arranged on the fringe trusses in the vertical direction, the masts and the fringe trusses are connected through back pull rods to form a triangular supporting structure, and then tension suspension cables are installed between the masts on the two sides; after the fringe truss in the middle is installed, closed and accepted, all the telescopic mechanisms, the suspension cables, the back pull rods and the masts are sequentially and alternately dismantled, the overhanging ends of the secondary trusses are suspended through the telescopic mechanisms to be alternately installed, the limitation of an overall lifting method on the lifting height, the lifting path and a large-area assembly site is broken through, and the construction efficiency is improved. The potential safety hazard caused by post-repair mounting of the support area is avoided; meanwhile, the high-altitude work amount of component installation, welding and the like in a high-altitude bulk method is greatly reduced, the safety risks of falling, object hitting and the like are reduced, the construction process is simplified, the construction efficiency is improved, and the construction period is shortened.

The invention relates to the technical field of traffic engineering management, and particularly discloses a traffic engineering quality safety dynamic supervision and management system and method, and the method comprises the steps: constructing a multi-dimensional sensing network which comprises an entity, an environment and a behavior sensing layer, and collecting data; a three-dimensional model is built based on the BIM technology, and standardized data and model millisecond mapping are fused and visualized; an LSTM and random forest hybrid algorithm is adopted to establish a three-level early warning model, and risks are identified and pushed in a graded manner; matching the disposal flow according to the early warning level, tracking the progress by the mobile terminal, and checking the closed loop in a grading manner; and key node data are stored through a multi-level block structure, an alliance chain permission, a consensus mechanism and an intelligent contract, so that reverse tracing is realized. According to the invention, real-time acquisition, dynamic modeling, intelligent early warning, co-processing and credible tracing of quality safety data in the traffic engineering construction process are realized, the quality safety risk is reduced, and the management efficiency and supervision transparency are improved.

The invention discloses a vertical shaft duct piece splicing device and a construction method thereof, and belongs to the technical field of vertical shaft engineering construction.The vertical shaft duct piece splicing device comprises a moving trolley, an overturning assembly, a clamping mechanism and a lifting mechanism, a support of the overturning assembly is vertically arranged on the moving trolley, and an overturning plate is rotationally connected to the support and provided with two guide columns; the overturning oil cylinder is used for driving the overturning plate to be switched between the horizontal state and the vertical state. A base of the clamping mechanism is provided with two guide holes for two guide columns to penetrate through, a clamping piece is rotationally arranged in a rotating groove of the base, an elastic piece is installed in the rotating groove and connected with the clamping piece, and when a duct piece is placed on a turnover plate in a horizontal state, the clamping piece can be pushed to be rotationally inserted into a hanging hole of the duct piece, and the elastic piece is stretched. The lifting mechanism is arranged on the overturning plate and can drive the base to drive the clamping piece to vertically move downwards. The vertical shaft duct piece splicing construction method adopts the vertical shaft duct piece splicing device. The device is high in stability, high in assembly precision and low in safety risk.