1645 results about "Surveillance camera" patented technology

The invention relates to the technical field of safety monitoring, in particular to a smart park safety management method and system based on AI visual identification, and the method comprises the following steps: obtaining a video stream captured by a monitoring camera of a smart park, extracting the movement track data of a person through AI, and carrying out the statistics of the stay duration and access times of a target object in each grid; and constructing retention heat distribution of the grid region to obtain a personnel trajectory retention heat value. According to the invention, through the data processing of the monitoring video stream and the combination of the gridding analysis of the personnel motion trail, the accurate capture of the abnormal cruise behavior is enhanced, and through the calculation of the change rate of the access times between the adjacent grids, the statistics of the round-trip condition of the target among the plurality of areas and the combination of the passing authority data, the abnormal screening is carried out. The method improves the recognition capability of abnormal movement modes, achieves the dynamic adjustment of regional safety risk levels, improves the response capability of park security, enables the recognition capability of abnormal behaviors to be enhanced, and reduces the false alarm rate.

The invention discloses a human abnormal behavior monitoring method based on a large-model multi-agent, which is executed by a modular multi-agent system deployed on a back-end server, obtains information through a monitoring camera, and comprises the following steps: obtaining a video stream from the monitoring camera by a sensing agent and extracting human body posture features; analyzing the key frame by a scene understanding agent by using a visual large model, and constructing a time sequence dynamic scene graph; the core reasoning agent evaluates the scene semantic conformity based on the pre-trained large model and performs abnormal preliminary judgment; performing fine-grained classification, interpretation generation and risk assessment on the abnormal behaviors; and the report and action agent generates an alarm and records event data. According to the invention, through multi-agent cooperative work and a large model technology, efficient and accurate monitoring of human abnormal behaviors is realized, and the intelligent level of the monitoring system and the abnormal behavior identification accuracy are improved.

The invention discloses an intelligent emergency and management and control method for a stadium, and belongs to the technical field of stadium safety management and control. According to the method, video stream data are collected through a monitoring camera array, and coverage areas of all cameras in the array have 15%-20% of overlapping areas; inputting the data into a pre-trained people density identification model to generate a real-time people flow distribution thermodynamic diagram; the retention index of each partition is calculated, and an emergency evacuation mode is activated in combination with a preset safety threshold value; in the emergency mode, the infrared sensor group monitors the instantaneous pedestrian flow speed, and an optimal evacuation path is generated by a dynamic path planning algorithm; the path is converted into a control instruction to drive an electromagnetic access control gate, emergency lighting and an electronic guide identifier, feedback data triggers a path re-calculation mechanism, and a two-dimensional code scanning device counts the number of evacuated people so as to start manual re-checking. Real-time situation awareness, dynamic risk assessment and intelligent evacuation guidance of the stadium are achieved, emergency response and evacuation efficiency is effectively improved, and crowd safety is guaranteed.

Disclosed is a image processing apparatus and a method for controlling the image processing apparatus. The image processing apparatus according to an embodiment of the present disclosure may identify an object from an acquired image, determine whether the object is hidden by another object by using an aspect ratio of a bounding box of the detected object, and based on the object being hidden, estimate an entire length of the object based on coordinate information of the bounding box. Accordingly, the size information of the hidden object may be efficiently identified while a large amount of database is applied or resources of the apparatus is minimized. The present disclosure may be in connection with a surveillance camera, an automotive driving vehicle, an artificial intelligence module of at least one of a user terminal or a server, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like.

The invention relates to the technical field of anti-terrorism early warning, in particular to an intelligent anti-terrorism early warning system based on image recognition and big data analysis, and the system comprises a multi-source data collection module which is used for collecting video streams, image data and environment parameters through a monitoring camera, a sensor and an Internet of Things device in a deployment region, executing target detection and desensitization operation at the edge computing node, and caching original data to a distributed message queue; and the data standardization processing module is connected with the message queue and is used for carrying out space-time alignment and semantic mapping on the multi-modal heterogeneous data. According to the invention, monitoring videos, sensor data, Internet of Things information and the like are preprocessed and desensitized at edge nodes, space-time alignment and semantic unification are completed through a data standardization processing module, and cross-modal feature fusion is realized through a multi-modal fusion analysis module, so that barriers among different types of data are broken through, and the accuracy of data fusion is improved. Scattered information forms an organic whole, and a comprehensive and consistent data basis is provided for abnormal behavior recognition.

The invention discloses a locomotive sanding detection method. The method comprises the following steps: step 1, judging whether a license plate number is identified or not; 2, broadcasting sanding notifications of all the positions according to a preset time difference; step 3, acquiring a video shot by the monitoring camera; 4, dark light enhancement is carried out through a U-Net model, a YOLOV5 model is input, and whether the monitoring camera is shielded or not is judged; step 5, carrying out illumination adaptation processing on a plurality of continuous frames of images of the video obtained in the step 3 through a CLAHE algorithm; step 6, carrying out image enhancement through a Retinex algorithm and a U-Net model; 7, inputting a 3D-CNN model to carry out feature extraction and time sequence modeling; and step 8, inputting a Transform Encoder model, and outputting the probability of each sanding flow rate. According to the method, the detection of the sand spraying amount can be completed, and the detection of whether the monitoring camera is shielded or not can be completed before the detection of the sand spraying amount.

The invention provides a video feature analysis method based on a multi-modal large model, and relates to the technical field of video feature analysis methods. Comprising a video data acquisition module, a multi-modal data preprocessing module, a multi-modal large model construction and training module, a video feature extraction and fusion module, a feature analysis and classification module, a result visualization module, a model evaluation and optimization module, a data storage and management module, a user interaction module and a system security protection module. The video data acquisition module can improve the accuracy of feature analysis from various video sources such as a monitoring camera, a network video platform and a local video file library, features and semantics in videos can be captured more comprehensively by fusing multi-modal information and a powerful multi-modal large model, and compared with a single-modal analysis method, the video data acquisition module has the advantages that the accuracy of feature analysis is improved. The recognition accuracy of objects, scenes and behaviors in the video is remarkably improved, misjudgment and missed judgment conditions are reduced, and a more reliable basis is provided for subsequent decision making.

The invention relates to the technical field of camera control, and discloses an outdoor monitoring camera control method and device based on solar panel power supply, and the method comprises the steps: inputting original image data collected by a monitoring camera into a Light-EMA module for feature enhancement and Haar wavelet downsampling processing, obtaining a target feature matrix, and obtaining a target feature matrix; the optimal parameters of the holder control are calculated; collecting solar voltage and current values and environment illumination intensity, and performing SOC calculation and LSTM-GRU neural network prediction to obtain an energy availability index; performing multi-objective optimization solution through a DSAC-CAL algorithm to obtain an optimal decision vector of a system action space; according to the optimal decision vector, the camera frame rate, the processor frequency, the wireless transmission power and the sleep period are regulated and controlled, a multi-module cooperative control signal is obtained, the position prediction precision is improved, the system energy consumption is effectively reduced, the working time of the solar camera is prolonged, and the all-weather operation reliability is improved.

The invention discloses an intelligent terminal monitoring system and method based on edge calculation, and relates to the technical field of terminal monitoring, and the method comprises the steps: calculating the important coefficient of a camera through obtaining the monitoring region data of a traffic monitoring camera corresponding to each fault edge calculation center; screening out a target edge calculation center capable of receiving data of the fault edge calculation center from the non-fault edge calculation centers; calculating an adaptation coefficient of the target edge calculation center and the fault edge calculation center; according to the adaptation coefficient and the importance coefficient, determining a final target edge calculation center of each fault edge calculation center, and transmitting traffic monitoring camera data to the center for analysis; and finally, the target edge calculation center analyzes the data by combining the important coefficient of the camera, and monitors whether the camera has a fault or not. The traffic monitoring camera data can be timely and efficiently processed, the edge computing center can reasonably distribute the load, and the stability and the accuracy of the monitoring system are guaranteed.

The invention relates to a high-definition monitoring camera tracking method and system for multi-angle face detection. The method comprises the following steps: preprocessing an original image collected by a high-definition monitoring camera to obtain a first preprocessed image and a second preprocessed image; performing feature extraction and feature fusion on the first preprocessed image and the second preprocessed image to obtain a multi-scale fusion feature map; face positioning and posture analysis are carried out through a position sensitive convolutional network, and a face detection result is obtained; performing face region subdivision and angle characteristic analysis on the face detection result to obtain enhanced face representation; and calculating a cross-camera matching cost matrix based on the enhanced face representation, and performing trajectory prediction and updating processing to obtain a face tracking result in the multi-camera environment. According to the method, the capability of capturing facial micro-expressions and fine features is improved, high-precision face association and smooth trajectory handover among different cameras are realized, and the problem of trajectory fragmentation in view crossing areas of the cameras is solved.

The present invention discloses an intelligent terminal monitoring system and method based on edge computing, which relates to the technical field of terminal monitoring. By obtaining the monitoring area data of traffic monitoring cameras corresponding to each faulty edge computing center, the importance coefficient of the cameras is calculated; and a target edge computing center capable of receiving the data of the faulty edge computing center is screened out from each non-faulty edge computing center; the adaptation coefficient between the target edge computing center and the faulty edge computing center is calculated; according to the adaptation coefficient and the importance coefficient, the final target edge computing center of each faulty edge computing center is determined, and the traffic monitoring camera data is transmitted to this center for analysis; the final target edge computing center analyzes the data in combination with the importance coefficient of the camera to monitor whether the camera is faulty; it ensures that the traffic monitoring camera data can be processed in a timely and efficient manner, and at the same time ensures that the edge computing center can reasonably allocate the load, guaranteeing the stability and accuracy of the monitoring system.

A cabinet may include a body, a bracket connected to a rear surface of the body, and a cover connected to a front surface of the body to be opened and closed, wherein the body may include a first accommodation groove at a rear edge of the body, the bracket may include an insertion protrusion corresponding to the first accommodation groove and a support protrusion at the edge of the bracket to face a rear surface of the body at a position spaced apart from the first accommodation groove. The body may slide to a second position from a state where the body is temporarily fixed to the bracket at a first position.

The invention provides an elderly nursing method and system, which is applied to elderly nursing services, and comprises the following steps: receiving a video link generated when a monitoring camera monitors an identification object through a message push service; acquiring user configuration information from a database; sending the video link and the user configuration information to an artificial intelligence cloud service, so that the artificial intelligence cloud service analyzes the dangerous behavior and the daily behavior of the identified object to obtain an analysis result; an analysis result sent by the artificial intelligence cloud service is received, and daily state information of the recognition object is counted according to the analysis result; comparing the daily state information with the user configuration information to obtain a comparison result; through combination of the intelligent camera and AI behavior analysis, abnormal or high-risk behaviors of the elderly can be fed back in real time and pushed to family members in time for first-time processing, so that the family members can comprehensively master the living state of the elderly, potential health risks or living abnormalities can be found in time, and the user experience is improved.

The invention relates to the technical field of locomotive roof monitoring, in particular to a locomotive roof omnibearing intelligent monitoring device which comprises a roof high-tension porcelain insulator monitoring module, a pantograph monitoring module, a display module and a data analysis module. By monitoring the state of the high-tension porcelain insulator on the roof in real time, the situation that the current cannot be blocked after the high-tension porcelain insulator is damaged in the running process of the locomotive is avoided, and accidents are avoided. The high-definition monitoring camera and the infrared monitoring camera are used for respectively collecting a view and a thermal imaging diagram, so that the judgment result is more accurate. The detection result is analyzed and judged through data, the vehicle roof high-tension porcelain insulator monitoring unit judges the breakage state of a porcelain insulator to ensure the driving safety of a locomotive and the efficiency of traffic transportation, and the pantograph monitoring unit ensures the accuracy of the detection result through two times of verification in a layered and emphasized manner. And the arc discharge monitoring unit judges the arc discharge duration and the arc discharge times, so that the safety and the service life of the line are further guaranteed.

The invention discloses an unmanned aerial vehicle identification and tracking system, which can integrate multi-source visual information by calling to-be-identified images shot by a plurality of camera units, thereby determining accurate position information of a target unmanned aerial vehicle. The plurality of camera units can be monitoring camera networks which are arranged all over, the camera networks are arranged and execute tasks such as public security monitoring at ordinary times, and unmanned aerial vehicle identification and tracking can be executed, so that the hardware use cost is reduced, and the camera units can be quickly spread and implemented under the existing hardware condition; the visual field range of the camera shooting unit is matched with the low-altitude flight height of the target unmanned aerial vehicle, the blind area of the flyer detection radar can be effectively covered, and the target unmanned aerial vehicle can still be effectively recognized and tracked under the condition that the target unmanned aerial vehicle flies at a low altitude and the flyer detection radar cannot effectively detect. Supervision of the unmanned aerial vehicle is improved, and use safety of the unmanned aerial vehicle is guaranteed. The method is widely applied to the technical field of unmanned aerial vehicle management.

The invention discloses an intelligent campus student activity trajectory tracking method and system based on front and rear end fusion learning, and relates to the technical field of intelligent campus security. An embedded front end collects videos of a plurality of monitoring cameras of a campus, carries out target detection and extracts primary features of student targets; uploading the extracted primary features and the video to a back-end server; and the back-end server extracts advanced features of human body postures and gaits from the video by using a human body posture estimation algorithm and a gait recognition algorithm, fuses the advanced features and the primary features to obtain fused features, and clusters all detected student targets by using the fused features, thereby realizing cross-camera identity unification of the same student under different cameras, and improving the identification accuracy of the student. Face detection and recognition are carried out in each clustering group, a student information base is matched to determine student IDs, and the student IDs, video timestamps and monitoring camera position information are stored; and the client retrieves target information according to the input student ID or photo, and automatically clips and generates a time-space continuous complete cross-camera trajectory tracking video. According to the invention, efficient and intelligent analysis of campus monitoring data is realized, and targets can be accurately associated and tracked in a cross-camera scene.

The invention belongs to the technical field of bridge structure defects, and particularly discloses an underwater bridge structure defect identification method based on machine vision, and the method comprises the steps: firstly, employing a water surface monitoring camera to detect water surface moving objects around a pier, and carrying out the recognition of the underwater bridge structure defects based on the types, distribution density and motion trails of the objects; constructing a static distribution interference factor and a dynamic motion interference factor, and performing weighted fusion to generate a water body detection interference coefficient. Meanwhile, the water turbidity and the real-time flow velocity are obtained by means of an underwater sensor, and the light supplementing distance, the red light proportion and the shutter speed are dynamically adjusted in combination with the water detection interference coefficient. Then, the ROV is controlled to descend with the axis of the pier as the spiral center, visible light and ray transmission images are synchronously collected according to the adjusted parameters, a panoramic image is formed through fusion splicing, finally, defect danger indexes are output through image recognition and fed back, efficient detection and accurate evaluation of the state of the pier are achieved, and reliability and effectiveness of defect recognition are enhanced.

The invention relates to the technical field of computer vision and target detection, and particularly discloses a lightweight real-time two-wheeled vehicle helmet detection method. According to the method, firstly, a video stream is collected and preprocessed through a traffic monitoring camera, then feature extraction and fusion are carried out through a lightweight backbone network, an encoder and a neck network in sequence, finally, a detection result is output through a decoder, a StripCGLU module is introduced to reduce the calculation complexity, a Pola Former module is adopted to enhance the feature interaction capability, and finally, the detection result is output through a decoder. And a GLBiFPN network is designed to optimize multi-scale feature fusion. According to the method, the calculation complexity and parameter quantity of the model are remarkably reduced, the requirement of edge calculation equipment for efficiency is met while high detection precision is guaranteed, and an effective technical means is provided for safety monitoring in an intelligent traffic system.

The invention provides an aligned pedestrian re-identification system and method based on boundary progressive sample optimization and a DDT model, and the system comprises an image preprocessing module which is used for carrying out the feature extraction of an existing pedestrian data set based on the DDT model, and obtaining a feature map; the target feature measurement model training module is used for calculating the feature distance of the feature map, performing boundary progressive sample optimization on the existing pedestrian data set based on the feature distance, and training a target feature measurement model; and the cross-camera retrieval module is used for performing target pedestrian recognition and positioning on pedestrian images collected by the monitoring camera based on the trained target feature measurement model in combination with a multi-scale feature alignment technology, obtaining a recognition and positioning result and completing aligned pedestrian re-recognition based on boundary progressive sample optimization and a DDT model. According to the technical scheme of the invention, pedestrians with different backgrounds, different angles and different postures shot by a camera are accurately classified and positioned, and a basis is provided for personnel monitoring and target tracking tasks.

The invention provides an intelligent target supervision method and system based on data analysis. The method comprises the steps that visible light and thermal infrared image sequences are synchronously collected through a coastline monitoring camera, and plastic garbage contour features and temperature distribution differences are recognized; segmenting the contour, extracting displacement to generate an ocean current trajectory prediction path, arranging a buoy collection device, and driving surface water flow to converge by using a propeller to guide garbage to move along the path to form a dynamic accumulation area; plastic garbage difficult to decompose is recognized and marked based on visible light reflection difference, offset is calculated in combination with temperature difference to generate a path correction instruction, the track of a collecting device is adjusted to preferentially intercept a target, and real-time images are synchronized to update closed-loop supervision. According to the technical scheme, intelligent recognition, dynamic gathering and precise interception closed-loop supervision of marine plastic garbage difficult to decompose are achieved, the pollution treatment efficiency is improved, and secondary diffusion is restrained.

The invention discloses a deep learning target detection and counting optimization method in a low-light environment, which runs in a GPU (Graphics Processing Unit) and comprises the following steps of: acquiring image data of an area needing to be detected by using a monitoring camera, then preprocessing the acquired data, and dividing into a training set, a test set and a verification set; training a YOLO11 target detection model, and respectively training a model of the to-be-detected area and a detection model of the drill rod in the detection area; and deploying a reasoning low-light compensation model and a target detection model to realize a counting task. According to the invention, a deep learning small target detection algorithm adapting to different illumination conditions is realized, a more efficient and more accurate target counting task is realized in actual production, and the production efficiency and the quality control level are further improved.

The invention relates to the technical field of automobile safety detection, and discloses a fatigue driving real-time detection method and system based on a monitoring camera. The method comprises the following steps: acquiring a multi-source sensing data stream in a face area of a driver, and performing time sequence alignment processing to generate a synchronized physiological signal set; multi-modal feature extraction is carried out on the face muscle activity intensity image to obtain an eye movement track feature vector, a head posture change sequence and a face muscle activity intensity map; respectively generating an eye movement fatigue index and a head behavior anomaly score, and obtaining a comprehensive fatigue grade index in combination with a weighted decision algorithm; generating a graded early warning trigger strategy according to the comprehensive fatigue grade index and the real-time driving environment parameter, and calling a corresponding alarm instruction to generate a multi-stage warning signal output stream; and monitoring the change trend of the physiological state of the driver in real time, carrying out dynamic priority adjustment on a warning signal output flow, updating an alarm instruction set, and completing fatigue driving intervention closed-loop control.

The invention relates to the technical field of video recognition, in particular to a smart home emotion interaction method and system based on a monitoring camera, and the method comprises the following steps: deploying the monitoring camera in a living space, capturing continuous video frames in the living space, and generating preliminary biological feature data; and based on the preliminary biological characteristic data, analyzing expressions and postures of the residents, and generating expression and posture analysis results. According to the invention, facial expressions and posture changes of residents are captured through video streams, a multi-dimensional emotion recognition system is established, and dynamic evaluation of short-term and long-term emotional states is realized. Expression and posture sequences are processed independently, emotion changes can be recognized, and misjudgment caused by singleness of facial features is avoided. The dynamic early warning mechanism improves the recognition precision of the abnormal state through the comprehensive evaluation of the behavior pattern and the emotion trend, so that the detection of the abnormal emotion not only depends on the single-frame image information, but also carries out the judgment in combination with the time dependence characteristic of the behavior sequence.

The invention discloses a zero-perception dairy cow behavior observation system based on AI vision, and the system comprises a data collection module which is used for collecting a video image through a monitoring camera; the image preprocessing module is used for generating a standardized image frame sequence; the visual Transform feature extraction module is used for outputting a space-time feature vector; the deep forest behavior classification module is used for behavior classification and label output; the behavior analysis module is used for generating a behavior track and group distribution; the model adaptive training module is used for periodic incremental training; and the abnormity early warning module is used for judging and outputting abnormal behaviors. According to the invention, cow behavior automatic identification and abnormity intelligent early warning based on AI vision are realized, and the management efficiency of a farm and the cow health guarantee level are improved.

The utility model relates to the field of sluice foundation detection, and discloses a sluice foundation subsidence displacement observation device which comprises a base, a stand column fixed in the middle of the top of the base, scale marks engraved on the side wall of the stand column, a connecting seat sleeved on the side wall of the stand column, a pointer fixed at one end of the top of the connecting seat close to the scale marks, and a circular seat fixed at the top end of the stand column. A buzzer is fixed to the top of the circular base, monitoring mechanisms are arranged at the two symmetrical ends of the bottom of the circular base, a transparent protective cover is fixed to the top of the connecting base, a monitoring camera is arranged in the transparent protective cover, and the monitoring camera and the connecting base are fixed. According to the utility model, through the combination of the monitoring mechanism and the monitoring camera, the real-time monitoring of the settlement condition of the sluice base can be realized, continuous image data can be provided, the settlement height of the foundation can be analyzed and recorded conveniently, and when the settlement of the base exceeds a set threshold value, the buzzer gives an alarm sound to remind related personnel to take measures in time, so that the working efficiency is improved. The safety is ensured.

The invention provides a moving object two-stage target detection acceleration method and system based on a motion vector, relates to the technical field of target detection, performs frame-by-frame detection on a video compression video stream, and specifically comprises the following steps: in the first stage, performing clustering analysis on the motion vector of a current frame image by adopting a parallel mode of candidate box generation and feature extraction to obtain a clustering result; generating a candidate box, and performing feature extraction on the current frame of image to generate a feature map; in the second stage, on the basis of the candidate box, feature blocks with fixed sizes are extracted from the feature map, the feature blocks are input into a full connection layer for classification and regression, and classification and positions of moving objects in the compressed video stream are obtained; according to the invention, on the basis of the Faster RCNN, the reasoning speed for identifying the moving object in the coded video can be effectively improved and the reasoning time for target detection of the compressed video by a target detection algorithm can be reduced by facing a detection scene of the moving target object in the coded video data collected by mass monitoring cameras.

The invention relates to the technical field of video monitoring, in particular to a method for quickly searching a linkage monitoring camera in a panoramic video, which comprises the following steps of: obtaining monitoring equipment information and panoramic video picture information through a panoramic monitoring system; constructing a panoramic video picture correction mechanism to adjust the panoramic video picture information to obtain corrected panoramic video coordinate information; introducing a geographic coordinate and pixel rectangular coordinate transformation expression based on the basic imaging information, and analyzing a corresponding relation between a panoramic video picture and a physical space coordinate based on the geographic coordinate and pixel rectangular coordinate transformation expression and the corrected panoramic video coordinate information; combining monitoring equipment information, corrected panoramic video coordinate information and the corresponding relation to build a panoramic area and monitoring camera mapping library; and carrying out panoramic target area frame selection and panoramic video picture display according to the panoramic area and the monitoring camera mapping library. According to the invention, linkage display of a panoramic video and a camera local picture is realized through space calibration and an intelligent algorithm.

A method of video surveillance comprising capturing video data from a plurality of video surveillance cameras identified by unique identifiers; acquiring metadata generated by performing captioning of the video data, wherein the metadata comprises semantic data to represent content in the video data in combination with the unique identifiers; and identifying user-relevant content using either one of a user-defined semantic query and a user-selected Video Anomaly Detection (VAD) model, in combination with either one of a unique identifier and a subset of the unique identifiers.

Disclosed is a surveillance camera capable of detecting a color of a vehicle using vehicle logo detection. This specification describes a method that, instead of processing an entire image of a vehicle, detects the vehicle's logo and expands a bounding box of the detected logo to analyze a dominant color within the expanded bounding box for higher accuracy. During a dominant color analysis process, color correction may be performed based on metadata, environmental factors, and a lighting condition. In addition, more accurate vehicle color detection may be achieved through color distance measurement. According to this specification, at least one of a surveillance camera, an autonomous vehicle, a user terminal, and a server may be associated with an Artificial Intelligence (AI) module, a robot, an Augmented Reality (AR) device, a Virtual Reality (VR) device, and a 5G service.

The utility model discloses a cleaning isolation cabin for installing a modular integrated reactor. The cleaning isolation cabin comprises a main cabin body, a movable ceiling, a bearing base, air purification equipment, an electric control box body, an air shower, an illuminating lamp and a monitoring camera, the main cabin body is mounted on the bearing base, the upper end of the main cabin body is opened to form a hoisting opening, and the movable ceiling is correspondingly and horizontally arranged on the hoisting opening; an isolation cabin access port communicated with the outside is formed in the circumferential direction of the main cabin body, the air shower is correspondingly arranged on the outer side of the isolation cabin access port, and an outlet of the air shower is communicated with the isolation cabin access port; the air purification equipment is communicated with the interior of the main cabin body, the electric control box body is arranged on the inner peripheral wall of the main cabin body, and the illuminating lamp and the monitoring camera are arranged at the top of the inner peripheral wall of the main cabin body. The clean isolation cabin has the advantages that repeated investment can be reduced, large-range isolation space is avoided, manpower and material resource investment can be reduced, the construction period of nuclear facility engineering is shortened, and economic efficiency is improved.