181results about How to "Reduce casualties" patented technology

The invention provides a digital twinborn model and method for a coal mine disaster scene based on multi-modal data. The method comprises the following steps: acquiring original data in a mine; performing cleaning, normalization and de-noising preprocessing on the original data; fusing different types of preprocessed original data by using a data fusion algorithm, and constructing a comprehensive digital twinborn model; establishing a virtual mirror image of the mine by using a digital twinning technology, and mapping the mine in the real world into a digital twinning model; real-time mapping and simulation analysis are carried out based on the established digital twinborn model, and the disaster development trend is predicted by monitoring the internal environment and the equipment state of the mine; and according to a model analysis result, optimizing prevention and control measures of coal mine disasters, establishing a real-time monitoring and early warning system of the disasters, and taking measures in time when abnormal conditions are found. The system comprises a data acquisition module, a model construction module and a model analysis module. The pertinence and effectiveness of coal mine disaster prevention and control can be improved.

The invention relates to the technical field of earthquake monitoring, in particular to a geological disaster early warning device based on earthquake waveform recognition, which comprises an earthquake data receiving module, an earthquake wave type analysis module, a disaster mode recognition module, an early warning signal construction module, a risk judgment module and an early warning information issuing module. According to the invention, through identifying and analyzing the seismic waveform data, the characteristics of the earthquake and the type of the caused disaster, such as landslide, rock collapse, debris flow, ground fissure, surface collapse and the like, are rapidly judged, and through comprehensively evaluating the emergency and the influence range of the earthquake event, the targeted early warning information is generated and issued in time. The geological disaster response speed and efficiency can be remarkably improved, earthquake monitoring is not limited to recording of earthquake activities, disaster risks can be evaluated in real time, more accurate disaster prediction and early warning can be provided, for areas where geological disasters frequently occur, the early warning accuracy and timeliness can be remarkably improved, casualties and property losses can be reduced, and the economic benefit is improved. And the method has extremely high practical value and social significance.

The invention belongs to the technical field of fire detection, and discloses a method and a system for artificial intelligence identification, research and judgment of fire alarm, and the method comprises the following steps: carrying out the framing of input video data, converting a video stream into continuous image frames frame by frame, carrying out the timestamp marking of each frame of image, and obtaining a framed image; according to the method, flames are positioned by analyzing red and orange tones of the images, early signs of a fire can be more accurately captured by analyzing flame shapes and diffusion behaviors, the area change rate of flames and smoke in continuous frames can be analyzed, the dynamic change of flame diffusion speed and smoke coverage can be beneficially understood, and the method is suitable for popularization and application. The fire hazard early warning is more timely and accurate, the fire hazard risk level is evaluated according to the flame dynamic model, and the system is allowed to adjust the sensitivity and threshold of the early warning signal according to the real-time data, so that the fire hazard early warning scheme is more targeted and effective, faster response can be provided at the initial stage of the fire hazard, and casualties and property loss are greatly reduced.

The invention discloses a distributed optical fiber monitoring system for monitoring a rock mass change state, relates to the field of monitoring equipment, is applied to an underground cavity and can monitor axial and radial fracture signals of the underground cavity at the same time so as to facilitate three-dimensional positioning of a seismic source at a fracture occurrence position. The distributed optical fiber monitoring system comprises a first optical fiber and a second optical fiber. The first optical fibers are arranged on the cavern wall in the axial direction of the underground cavern and can monitor fracture signals distributed in the axial direction of the underground cavern; the second optical fiber is mainly composed of a peripheral side optical fiber section and a reversing transition section, and the peripheral side optical fiber section is used for being arranged on the cavern wall along the cross section contour of the cavern wall and can monitor fracture signals distributed in the radial direction of the underground cavern. Therefore, the distributed optical fiber monitoring system can simultaneously monitor the axial and radial fracture signals of the underground cavern, realizes the space monitoring of the underground cavern, and facilitates the more accurate three-dimensional positioning of a seismic source at the fracture occurrence position.

The invention belongs to the technical field of bridge and road crack recognition, and discloses an improved semantic segmentation network construction method and system for bridge and road crack recognition. According to the method, a convolution block, a down-sampling layer and an up-sampling layer in a UNet network are improved, a novel space-channel attention mechanism is introduced, jump connection and a U-shaped structure in the UNet network are reserved, an improved semantic segmentation network is constructed, and crack recognition is carried out. According to the invention, a highly-specific crack semantic segmentation network model with stronger generalization ability and higher precision is designed, on CRACK500, SDNET2018 and CFD public data sets, IoU is 73.3%, F1 reaches 82.6% and is far higher than that of other models, and the method is suitable for detecting cracks on bridge body structures such as road surfaces and bridge towers of bridges.

The invention relates to the technical field of coal mine safety, in particular to a coal mine major disaster risk prevention and control system and method based on artificial intelligence, and the system comprises a data collection module, a data transmission module, a data processing module, a disaster prediction module, a decision support module and a user interaction module. The data transmission module transmits collected environment data to a ground control center, the data processing module ensures the accuracy and consistency of data through data preprocessing, data fusion and anomaly detection, the disaster prediction module predicts potential disasters and generates a risk assessment report, and the decision support module determines the risk assessment report according to the prediction result. According to the system, multiple intelligent technologies are integrated, comprehensive prevention and control of major disasters of the coal mine are achieved, and the safety management level of the coal mine is improved.

The invention relates to the technical field of tunneling parameter prediction, in particular to a complex stratum TBM tunneling parameter real-time prediction method. According to the technical scheme, the method comprises the steps of data collection and preprocessing, model selection and training, model prediction and evaluation, and result application and feedback. According to the data collection and preprocessing, operation parameters are collected in real time through various sensors on TBM equipment, wherein the operation parameters comprise the cutterhead rotating speed, the penetration degree, the thrust, the torque, the total thrust, the gripper shoe pressure, the propelling speed, the current and the power. Real-time prediction of the TBM tunneling parameters of the complex stratum is achieved by fusing the machine learning algorithm, the tunneling parameters can be optimized in real time, and the construction efficiency is improved; the tunneling process is stabilized to guarantee the engineering quality; tool abrasion is reduced, energy consumption is reduced, and the construction cost is reduced; equipment faults and accidents are prevented, and construction safety is guaranteed; and construction technology innovation can be promoted, intelligent construction management is achieved, the comprehensive benefits of TBM construction are comprehensively improved, and efficient, safe and intelligent development of tunnel (roadway) engineering is promoted.

The invention discloses a 5G communication data transmission management system, which relates to the technical field of communication data transmission and comprises a real-time resource monitoring module, an intelligent evaluation module, a feature extraction module, a transmission process division module, a normal transmission management module and an overload transmission regulation and control module, through a deployed network monitoring tool and performance monitoring software, the resource occupation condition in the video monitoring data transmission process is monitored in real time. According to the invention, the system intelligently evaluates the resource occupation condition through real-time monitoring and a machine learning model, and dynamically regulates and controls the resources, reduces the delay, accelerates the analysis speed and guarantees the emergency response in the case of high load by using the queue expansion index, the link fluctuation index and the queue theory model. Through transmission process division, efficient management in a normal stage and dynamic optimization in an overload stage are realized, timely response and resource release are ensured, waste is avoided, and the stability and efficiency of the system under various loads are improved.

An indoor fire hazard flashover risk prediction method based on a flame form and remote sensing data comprises the following steps: using fire hazard simulation software to simulate fire hazard data, and constructing a fire hazard data set; ignition is conducted, an indoor fire flame image is obtained through a high-definition camera, and the obtained image is processed; outputting the real-time heat release rate HRR of a fire point from the processed image through a residual network Resnet model; a methane laser remote sensing detector is used for monitoring and acquiring methane gas concentration data of an indoor flue gas layer at a specific height in real time; and building a fire hazard flashover risk prediction model based on the fire source heat release rate and the methane gas concentration, and predicting the flashover risk in advance. According to the invention, accurate identification and prediction of the flashover risk of the fire in the early stage of fire development are realized, and an early warning signal is sent when the fire is about to enter the flashover stage, so that casualties and property loss caused by the fire are reduced.

The invention discloses a fire early warning method and system based on multi-source information fusion and a medium. The method comprises single vehicle model training, model deployment, fire risk assessment, BIM modeling display and abnormal information notification. According to the fire early warning method and system based on multi-source information fusion and the medium, deep fusion and model training are carried out on the image, positioning and temperature information of the vehicle, the vehicle temperature fault mode can be rapidly and accurately recognized, the accuracy of fire early warning is improved, multi-source information is obtained in real time, and when the vehicle has an overtemperature phenomenon, the fire early warning efficiency is improved. A worker can quickly locate the vehicle and nearby the vehicle, measures can be taken for the overtemperature vehicle in time, fire disasters are prevented or the fire disaster damage degree is reduced, and powerful support is provided for fire prevention and control work. A bridge fire risk map is displayed on a display device or a user terminal by establishing a bridge BIM model, a command department, workers and drivers can quickly grasp information such as a disaster site, a fire reason, a disaster situation degree and a vehicle density degree of a current bridge fire through the bridge fire risk map, and the environment adaptability of the system is high; according to the invention, the fire risk can be evaluated in different environments, the decision-making efficiency of a command department is improved, a worker can quickly arrive at a rescue site to carry out fire extinguishing and rescue work, and a driver in a low-risk area can quickly evacuate from a bridge or assist the worker in personnel evacuation. And casualties and economic losses caused by fire disasters can be reduced as much as possible.

The invention provides a maritime traffic accident severity prediction method based on feature engineering, and the method comprises the steps: constructing a maritime accident risk influence factor data set according to risk influence factors and accident severity in historical data, and carrying out the preprocessing of the data; coupling the influence factors through a feature fusion algorithm, and mining association rules; carrying out data balance analysis by using an SVM-SMOTE oversampling method, and balancing the sample proportion of serious accidents and non-serious accidents; the original influence factors are trained by using multiple machine learning models, and key influence factor subsets are screened out by calculating the importance degrees of the influence factors; and S4, evaluating an optimal model in the machine learning model through a UAR evaluation index, and predicting the severity of the marine traffic accident by using the optimal model and combining the key influence factor subset in the S4. By reasonably matching the combination of the prediction model and the oversampling method, the model performance is remarkably improved, and a comprehensive and profound insight is provided for predicting the severity of the marine accident.

The invention relates to a new energy vehicle power battery protection flame-retardant system and method, and belongs to the technical field of new energy vehicle protection. The system comprises a controller, a power source, an unfolding device and sensors, the power source, the unfolding device and the sensors are electrically connected with the controller, the unfolding device comprises a gas generator and flame-retardant cloth, and the sensors comprise a temperature sensor arranged on a vehicle power battery, a smoke sensor and collision sensors arranged on a vehicle body and the power battery; and the unfolding device is arranged corresponding to a power battery of the vehicle. The invention has the following beneficial effects: when the vehicle power battery burns due to high temperature or accident collision, flames, high temperature and a large amount of smoke generated by burning are isolated, the escape time of passengers is prolonged, the medical cost caused by casualties is reduced, and the economic loss is effectively reduced; and meanwhile, by reducing the number of casualties in traffic accidents, the overall safety level of the society is improved, and the social stability is maintained.

The invention belongs to the technical field of mountain torrent disaster monitoring, and discloses a mountain torrent disaster monitoring data source fusion processing method and system based on deep learning. The method comprises the steps of multi-source rainfall data collection, multi-source data fusion and fusion processing of multiple monitoring data sources, including data integration, data alignment, data fusion algorithm and other operations, and through fusion processing of the multiple monitoring data sources, a high-precision rainfall monitoring result is obtained. Analyzing rainfall data, and predicting the probability and range of mountain torrent disasters possibly occurring in the future according to an analysis result; and releasing mountain torrent early warning information. Based on a multi-source data fusion technology, the rainfall condition can be monitored more accurately, and the accuracy and reliability of early warning are improved. Early warning information is sent out 2 hours in advance, enough time can be provided for relevant mountain torrent defense departments to take countermeasures, and casualties and property losses are reduced.

The invention discloses a coal mine water flowing fractured zone observation method, device and system and a storage medium, and belongs to the technical field of coal mine safety monitoring and early warning. The method comprises the following steps: constructing an initialized transparent geologic model; acquiring historical fracture electrification method data; the initialized transparent geologic model is used for processing historical fracture electrification method data so as to construct a roof water flowing fractured zone virtual mirror image; processing the virtual mirror image of the roof water flowing fractured zone by using a numerical simulation algorithm to obtain simulation monitoring data; obtaining monitoring difference data according to the simulation monitoring data and the electrical method data characteristics corresponding to the early warning threshold; optimizing the initialized transparent geologic model according to the monitoring difference data to obtain a target geologic model; acquiring fracture electrification method data through a drilling electrical method technology; inputting the fracture electrification method data into the target geologic model to obtain monitoring data; judging whether the monitoring data exceed an early warning threshold value or not; and if yes, triggering an early warning mechanism.

The invention discloses a comprehensive pipe gallery monitoring and early warning analysis method based on multimode sensing fusion reasoning, relates to the technical field of comprehensive pipe gallery monitoring and early warning, and solves the technical problem that in the prior art, only the risk at the current moment is processed, the potential chain reaction of the risk is ignored, and the risk is spread. According to the method, risk event evolution chains are constructed through development nodes of risk events, and a comprehensive pipe gallery risk evolution network is constructed according to overlapped nodes and index description sets of the risk event evolution chains; the early warning state score of the comprehensive pipe gallery is analyzed; analyzing an early warning consequence score of the comprehensive pipe gallery according to the consequence score of the risk event corresponding to the alarm node; based on the early warning state score and the early warning consequence score, the early warning grade of the comprehensive pipe gallery is analyzed; according to the invention, through the comprehensive pipe gallery risk evolution network, potential risk chain reactions are predicted in advance, and early warning is carried out in time.

The invention discloses a flood inundation area prediction method based on land survey data, and relates to the technical field of flood inundation area prediction.The method comprises the steps that when a dam breach risk coefficient of a target dam is not smaller than a preset dam breach risk coefficient, a plurality of preliminary inundation sub-areas are simulated according to flood water flow; obtaining a first flood water storage capacity of each preliminary submerging sub-region; taking the current vegetation density, underground water level height and soil permeability coefficient of each preliminary submerging sub-region as a data set, and clustering the data set to determine a water storage capacity influence index of each preliminary submerging sub-region; the second flood storage capacity is obtained according to the water storage capacity influence index and the first flood storage capacity, and the final flood inundation area is determined according to the second flood storage capacity, so that the flood inundation area in the future period can be predicted, the flood control plan and the emergency plan are normal, flood can be effectively prevented and evacuated before the flood comes, and the flood inundation efficiency is improved. And casualties and property loss are reduced.

The invention relates to the technical field of emergency rescue and disaster relief plan auxiliary recommendation, in particular to a disaster relief plan generation method and device based on disaster characteristics. According to the plan recommendation model, current disaster information is matched with a self-defined plan and a historical disaster plan to obtain a first matching probability and a second matching probability, and a recommendation result is obtained according to the first matching probability and the second matching probability; according to the disaster relief plan recommendation method and the disaster relief plan recommendation system, different types of disaster characteristics are quickly and accurately identified by combining a plan recommendation algorithm, and the personalized disaster relief plan is recommended and generated according to the disaster characteristics, so that the disaster response efficiency and effect can be remarkably improved, and the disaster relief plan recommendation method and the disaster relief plan recommendation system are high in practicability. And economic loss and casualties caused by disasters are reduced.

The invention belongs to the technical field of gas diffusion safety engineering, and relates to a H2S leakage diffusion range prediction method and system, and the method comprises the steps: arranging a plurality of different sensors in a predicted leakage region; calculating predicted concentration values of different sensors according to weather information and H2S mass fraction of a predicted leakage region; a target function is set according to the predicted concentration values of different sensors, and the leakage point position and the gas mass release speed are obtained; and according to the unsteady instantaneous point source puff superposition model, simulating the leakage point position and the gas mass release velocity diffusion to obtain the H2S leakage diffusion range. The system comprises a setting module, a calculation module, an acquisition module and a diffusion simulation module. According to the method and the system, the problems of small monitoring range, high possibility of missing report, insufficient H2S early warning capability and the like of the existing point type sensor are effectively solved; and the leakage source information is inversed by using the linear sensor information, so that the prediction precision can be further improved.

The invention relates to a mine micro-seismic event time sequence prediction method, in particular to a mine micro-seismic event time sequence prediction method based on a KF-LSTM-BO network, and the method comprises the following steps: firstly, performing logarithm processing on time-space intensity data to enhance autocorrelation; meanwhile, in order to reduce the influence of accidental high-strength micro-seismic events on the data and reduce the difference between the data, a maximum and minimum value normalization method is adopted for normalization. In order to enhance the prediction effect of the irregular data, a KF filter is adopted to carry out de-noising reprocessing on the processed data. In consideration of inconvenience of manual parameter adjustment and high degree of dependence on deep learning knowledge, Bayesian optimization is introduced to perform hyper-parameter automatic adjustment and optimization on the KF-LSTM. According to the invention, the monitoring data can be acquired in real time for prediction, the safety of mining operation is improved, the production safety is ensured, and casualties are reduced at the same time.

The invention discloses a coal dust influence suppression method for coal mine gas laser detection and a detection device, and the method comprises the steps: extracting gas in a coal mine goaf through a gas pump and a beam tube of the coal mine gas laser detection device, and detecting a characteristic absorption spectrum of the gas in the goaf by adopting a TDLAS technology; correcting a second harmonic signal and a peak value in the characteristic absorption spectrum by considering the attenuation influence of the coal dust in the goaf gas on the second harmonic signal of the laser penetrating through the goaf gas; and obtaining the accurate concentration value of each component gas in the goaf gas by using the corrected second harmonic signal peak value. According to the invention, the influence of coal dust on gas concentration detection can be greatly reduced, the sensitivity of gas detection is improved, and good detection effect and precision can be achieved.

The invention discloses an emergency rescue rapid ad hoc network positioning method and system, and the method comprises the steps: arranging initial positioning nodes in an emergency scene, and carrying out the initial positioning networking; setting a dynamic node distribution model, calculating the number of nodes in real time, and updating the positioning nodes in the initial positioning network; calculating a signal intensity attenuation index of the positioning node at each position in the updated initial positioning network; calculating the total signal strength index received by the positioning node at each position in the updated initial positioning network; calculating a positioning error index of the positioning node at each position in the updated initial positioning network; and comparing the signal strength attenuation index and / or the positioning error index with a corresponding threshold value, and when the signal strength attenuation index and / or the positioning error index exceed the corresponding threshold value, adjusting the positions and the number of the positioning nodes in the updated initial positioning network until the positions and the number of the positioning nodes are smaller than the corresponding threshold value.

The invention provides a potential safety hazard discovery and early warning method for limited space operation, and the method comprises the steps: collecting a video image through setting a camera at a construction site, carrying out the feature point extraction through a detection model by employing a convolutional neural network, obtaining the operation information in a construction fence, and recognizing a construction effective region; on the basis, the opening condition of the well lid, the detection result of the toxic gas monitoring equipment and the safety feature of well descending operation are recognized; and generating an alarm signal for the detected and identified abnormal information.

The invention relates to the technical field of slope disaster monitoring, in particular to a high and steep slope geological disaster monitoring method and system based on multi-source data fusion. The method comprises the following steps: tracking surface deformation through PS-InSAR time sequence analysis and atmospheric phase correction to obtain surface deformation characteristics; identifying the apparent diseases of the slope, and extracting spatial and temporal distribution characteristics of the apparent diseases of the slope; analyzing spatio-temporal evolution characteristics under geologic structure constraints by utilizing slope rock mass structure characteristics and the earth surface deformation characteristics; according to underground water level dynamic and potential sliding surface weakening features and the slope apparent disease spatial and temporal distribution features, extracting disaster-causing key factor features after cooperative correction; and combining the disaster-causing key factor characteristics, the true three-dimensional geological environment background characteristics and the spatio-temporal evolution characteristics to analyze the comprehensive early warning grade and the potential instability mode of the landslide. Scientific support is provided for high and steep slope geological disaster prevention and control, and engineering construction and operation safety can be guaranteed.

The invention discloses a state monitoring and instability emergency device for a mine hydraulic support. The state monitoring and instability emergency device comprises a monitoring mechanism, an anti-falling mechanism and an anti-toppling mechanism. The monitoring mechanism is used for monitoring and obtaining the inclination angle of the hydraulic support and the falling speed of the top beam and transmitting operation instructions to the falling prevention mechanism and the toppling prevention mechanism. The anti-falling mechanism is used for providing anti-falling supporting force for the hydraulic support top beam in the falling state. The anti-toppling mechanism is used for pushing and resetting the inclined hydraulic support. According to the device and the method, intelligent data analysis capability is achieved, multi-parameter fusion monitoring is achieved, the condition of the hydraulic support can be monitored in time, and damage to the support and related equipment caused by the conditions that the hydraulic support topples over due to excessive inclination, a hydraulic system of the support breaks down or a top beam suddenly falls due to other external factors is effectively prevented. The wireless communication technology is applied, early warning signals can be sent out in time, and relevant information and distress signals are sent to an overground dispatching department so that rescue can be carried out in the first time.

The invention discloses an alarm pushing method and device for AI identification drowning prevention and a medium, and relates to the technical field of image identification. The method comprises the following steps: identifying and analyzing a real-time video stream based on a preset anti-drowning alarm pushing model to detect whether an anti-drowning event exists; when it is detected that the anti-drowning event exists, continuously generating an alarm message of which an alarm corresponds to a preset number of coherent video frames, and determining an alarm type corresponding to the alarm message; in the process of continuously generating the alarm messages, continuously judging whether adjacent alarm messages meet message merging logic or not; wherein the message merging logic is that the time interval of the adjacent alarm messages is not greater than a preset merging time threshold and the alarm types of the adjacent alarm messages are the same; and in the process of continuously generating the alarm messages, if the alarm messages meeting the message merging logic reach the merging upper limit number, merging the alarm messages with the merging upper limit number into an upper limit alarm packet, and pushing the upper limit alarm packet to an alarm platform.

The invention discloses a gas cut overflow prediction method and system, electronic equipment and a storage medium, and relates to the technical field of drilling safety guarantee, and the method comprises the steps: determining a plurality of target factors from a plurality of factors related to gas cut overflow, the correlation degree between each target factor and the gas cut overflow quantity is higher than the correlation degree between each factor except the target factor and the gas cut overflow quantity; constructing a data set according to all the target factors, and training a preset network model in combination with a multivariate universe optimization algorithm to obtain a gas cut overflow prediction model; and predicting the gas cut overflow by using the gas cut overflow prediction model. According to the invention, the multi-objective optimization algorithm is applied to parameter optimization of the preset network model, the global search capability of the multi-objective optimization algorithm, the automatically adjusted parameters of the preset network model and the trained gas cut overflow prediction model can be fully utilized to predict the gas cut overflow, and the prediction accuracy of the gas cut overflow can be improved.

The invention discloses an emergency escape control system and method based on a sudden accident and a vehicle, and belongs to the technical field of vehicle safety, the system comprises a collision sensing module which comprises at least two collision sensors, and the collision sensors are arranged on the front portion and the rear portion of the vehicle respectively and used for sensing the collision of the vehicle when the vehicle encounters a collision sudden accident; perceiving and generating a collision signal; the vehicle information processing unit (VIU) is used for receiving the collision signal from the collision sensing module and processing and analyzing the collision signal to confirm the severity of a collision event; and the execution control module is used for sending displacement control instructions to the skylight controller, the seat controller and the suspension controller according to the severity of the collision event analyzed by the VIU. According to the invention, the survival rate can be improved when passengers in the vehicle are concentrated on the position, close to the skylight, of the middle part of the vehicle under the condition that the vehicle is crashed or a collision accident of continuous rear-end collision occurs.

The invention discloses a brand-new fire emergency command system and method, which comprehensively applies advanced technologies such as artificial intelligence, multi-source perception, Internet of Things communication and man-machine interaction. The system achieves the functions of fire early-stage accurate recognition, fire risk level-to-level management, visual interactive alarm, intelligent linkage equipment control, efficient communication cooperation, visual command and information feedback, and can comprehensively improve the fire emergency response capability. The method comprises the steps of multi-source detection, artificial intelligence identification and early warning, hierarchical intelligent alarm and active visual alarm, equipment linkage control, interactive visual cooperative command, and emergency response whole-process recording and process optimization. The system can be widely applied to industrial emergency management of various mines, mines, chemical plants and the like, emergency command of natural disasters, emergency management of urban operation and other scenes, can realize early accurate disaster discovery, quick response and cooperative command and disposal, and greatly improves the emergency response speed and the processing efficiency.

The invention discloses an electric multifunctional operation vehicle with a folding arm crane. The folding arm crane is fixedly arranged on a chassis girder between a vehicle head and a multifunctional operation compartment; the folding arm crane comprises a bearing base, a slewing bearing, a large arm, a middle arm, a telescopic arm, a large arm oil cylinder, a rotation detection assembly and a control assembly, the bearing base is fixedly arranged on the chassis girder, the large arm is rotationally connected with the top of the bearing base through the slewing bearing, the large arm, the middle arm and the telescopic arm are sequentially connected, and the two ends of the large arm oil cylinder are connected with the large arm and the middle arm respectively; the control assembly is electrically connected with the slewing bearing and the big arm oil cylinder, and drives the folding arm crane to rotate in the horizontal direction and the vertical direction through the slewing bearing and the big arm oil cylinder; the control assembly is further electrically connected with the rotation detection assembly, the telescopic length of the large arm oil cylinder is obtained through the rotation detection assembly, and when the telescopic length is within the safe telescopic range, the slewing bearing is controlled to rotate normally to drive the folding arm crane to act so as to avoid the vehicle head and the multifunctional operation compartment.

The invention relates to the technical field of prefabricated stairs, and discloses an anti-seismic concrete prefabricated stair which comprises stair flight plates, low-end stair beams, high-end stair beams, buffer springs, vertical adjusting devices and transverse adjusting devices. The device has the following advantages and effects that when the controller judges that an earthquake occurs, the vertical adjusting device is controlled to adjust the upper sliding seat to descend, the pre-positioning supporting plate is separated from the vertical sliding hole after being stressed, and the pin roller of the lower sliding seat extends out of the vertical sliding hole to be in contact with the lower connecting steel plate; the transverse adjusting device drives the adjusting seat to move into the vertical sliding hole from the transverse sliding hole, the vertical adjusting device adjusts the upper sliding seat to ascend until the vertical adjusting device is separated from the upper end face of the upper sliding seat, and at the moment, the compressed buffer spring elastically supports the stair flight plate. When the stair flight plates are subjected to vertical earthquake action in the earthquake process, vertical moving space is provided through the buffer springs, damage to the stair flight plates is reduced, the anti-seismic performance is improved, an escape way in the earthquake is guaranteed as much as possible, and casualties are reduced.