324 results about "Network camera" patented technology

The invention provides a network camera monitoring identification method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a monitoring data stream which is outputted by a network camera and comprises a video frame sequence and a corresponding time sequence metadata sequence, and then carrying out the spatial-temporal context coding processing of the monitoring data stream; the method comprises the following steps: generating a context feature cube containing spatial position information and time evolution information, then executing a normal behavior mode learning operation based on the context feature cube, and generating a reference feature library containing typical scene feature templates and feature evolution rule description; and dynamically matching and comparing the context feature cube of the current time period with the reference feature library, calculating a feature matching deviation value and generating an abnormal confidence score, and finally generating a monitoring early warning instruction containing abnormal occurrence time, a space coordinate range and a confidence level identifier according to the abnormal confidence score and corresponding space-time position information. And the accuracy and the early warning effect of monitoring and identification of the network camera are effectively improved.

The invention discloses a video stream adaptive low-delay real-time transmission method and system based on edge calculation. The method comprises the following steps: receiving a real-time video stream from a network camera, creating a pipeline queue, adding timestamp information for each video frame, and setting a queue protection mechanism; the coded video frames are taken out from the input queue, and the frames in the video are processed through hardware acceleration decoding; the resource use condition of the system is monitored in real time; executing a self-adaptive frame skipping decision according to a performance monitoring result; timestamp generation: dynamically calculating a timestamp interval according to an actual processing frame rate; receiving the decoded original video frame and the corresponding timestamp information, accelerating decoding by using hardware, and executing a video coding operation; and packaging and transmitting the coded video data, and providing a standard protocol interface to be connected with a client for playing. According to the scheme, stable low delay and relatively low resource occupation can be kept, and meanwhile, the video quality is remarkably improved.

The invention provides an intelligent identification and real-time alarm system for video monitoring. The intelligent identification and real-time alarm system comprises a video sensor, a real-time preprocessing module, a deep learning module, a risk assessment module and an early warning module, the video sensor collects video information in an environment and sends the video information to the real-time preprocessing module, and the video sensor adopts one of a network camera IPC, an analog camera AVC and an NVR video processor; the real-time preprocessing module preprocesses the video data, the preprocessing comprises clipping, size adjustment, enhancement and denoising, standard video image formation, resolution and frame rate unification and video noise reduction, and the standard video image obtained after preprocessing is sent to the deep learning module; and the deep learning module performs target identification, target detection and abnormal behavior detection on the standard video image through a deep learning model, and analyzes the video according to the output detection target and the detection abnormal result.

The invention relates to the technical field of computer leakage prevention, in particular to a computer defense system based on the Internet of Things, which comprises an Internet of Things equipment feature acquisition module for acquiring operation data of a computer, a printer and a network camera associated with the computer, and converging instruction time sequence, memory access and peripheral interaction information. According to the method, the dynamic reference model covering the full behavior dimension of the equipment is constructed by collecting multi-source operation data of a computer, a printer and a network camera and extracting quantitative indexes such as instruction time sequence fluctuation, memory access fluctuation and peripheral interaction frequency, and a traditional static protection mode of single encryption or access control is broken through. Spatial mapping and boundary inspection are performed on real-time operation indexes based on a dynamic reference, and automatic closed loop from abnormal signal detection to risk grade evaluation is realized in combination with a data access risk scene matching mechanism, so that the misjudgment rate and delay of manual research and judgment are effectively reduced.

The invention discloses a space-based telemetering transmission system for a carrier rocket, and relates to the technical field of rocket measurement and control communication, and the system comprises a rocket-borne network camera which is used for collecting video image data of a preset monitoring point position of the rocket, compressing the video image data, and outputting the compressed video image data as telemetering data; the telemetering transmission equipment is respectively connected with the rocket-borne network camera and the ground equipment, performs telemetering framing on the telemetering data, outputs first telemetering data and outputs second telemetering data to the ground equipment; the space-based power amplifier unit equipment is connected with the telemetering transmission equipment, receives the first telemetering data, amplifies the first telemetering data and then outputs the first telemetering data through radio frequency; the space-based telemetering antenna is connected with the space-based power amplifier unit equipment, receives the first telemetering data and then transmits the first telemetering data to a relay satellite, so that the satellite sends the first telemetering data to a ground measurement and control station; and the ground equipment is used for receiving the second telemetry data and then analyzing and uploading configuration parameter information. The problem that the coverage blind area of a telemetering system mainly based on ground-based measurement and control is large in the prior art is solved.

The invention discloses an unmanned aerial vehicle real-time identification and tracking system based on a neural network, and the system carries out the identification and tracking of a target unmanned aerial vehicle through the fusion of a millimeter-wave radar and a network camera, i.e., obtains a three-dimensional target coordinate through the millimeter-wave radar, achieves the all-weather monitoring of the target unmanned aerial vehicle, and achieves the real-time identification and tracking of the target unmanned aerial vehicle through the combination of an image recognition module of the network camera. The method achieves the precise recognition and confirmation of the type and position of a target, and improves the robustness and recognition accuracy in a complex environment. A closed-loop multi-strategy holder control algorithm is adopted, a tracking control module of the server is fused with three control strategies of proportion, fuzzy and integral differential adjustment, a holder control instruction is calculated in real time according to the target state, accurate turning and continuous tracking of a camera are achieved, the stability of the target unmanned aerial vehicle kept in the center of a picture is improved, and the target unmanned aerial vehicle is more stable. The requirements of continuous, stable and closed-loop tracking of the unmanned aerial vehicle in a complex dynamic environment are met, and the system has the technical advantages of being high in adaptive capacity, high in response speed and high in recognition precision.

The invention discloses a processing method for capturing an object image in real time based on a computer vision technology, and relates to the technical field of computer vision, and the method comprises the following steps: 1, capturing an object image in real time through a USB camera and a network camera, and transmitting image data to a server side in real time through TCP / IP for processing; step 2, removing noise by using a Gaussian filter, enhancing contrast by histogram equalization, extracting image features through a hybrid architecture based on Inception V3 and ResNet-50, optimizing feature extraction through a multi-scale attention mechanism by the hybrid architecture, performing pixel-level segmentation by using a full convolutional network, dividing the image into different object areas, and performing image segmentation on the object areas; post-processing is carried out on the segmentation result; according to the method, the Gaussian filter and the histogram equalization technology are adopted, image noise is effectively removed, the contrast ratio is enhanced, image features are extracted through the Inception network, pixel-level segmentation is carried out in combination with the full convolutional network, and high-precision and high-efficiency object segmentation is achieved.

The invention discloses a wall surface self-adaptive wheel type magnetic adsorption pipeline wall-climbing robot, which relates to the technical field of intelligent machinery and comprises a vehicle body, a passive self-adaptive device, a driving device, a permanent magnet adsorption magnetic wheel group and a sensing device, through mutual cooperation of the passive self-adaptive device, the driving device, the permanent magnet adsorption magnetic wheel set and the sensing device, the problem that when the wheel type magnetic adsorption wall-climbing robot walks on an uneven working face, the rotating speeds of all wheels are not synchronous can be solved; the wheel type magnetic adsorption wall-climbing robot smoothly walks on the working face and crosses the external corner of the pipeline, the use reliability of the robot is improved, then the working efficiency of the robot is improved, the front and back network cameras can transmit image information in the pipeline in real time, and an operator can conveniently monitor and adjust the working state of the robot.

The invention discloses an early forest fire smoke automatic detection system and method, and belongs to the field of forest fire monitoring and early warning. The system comprises a YOLOv7-Smokey small-range smoke detection network, a PyTorch framework, a TensorRT technology and an NVIDIA Jetson Nano embedded platform, and is characterized in that the YOLOv7-Smokey small-range smoke detection network is connected with the TensorRT technology through the NVIDIA Jetson Nano embedded platform. The method comprises the following steps: S1, carrying out image data collection and data enhancement on early forest fire smoke through a network and public data; s2, making an early forest fire smoke detection data set; s3, a YOLOv7-Smokey backbone network is designed, and training is carried out; s4, deploying the YOLOv7-Smokey network model to an NVIDIA (Noise Visual Identity Architecture) Jetson Nano embedded platform; s5, performing reasoning acceleration processing on the YOLOv7-Smokey network by using a TensorRT technology; s6, performing real-time early forest fire smoke detection on an NVIDIA Jetson Nano platform by using a network camera; and S7, transmitting a reasoning result to a server in a video stream pushing mode. The method is used for deploying an early forest fire smoke detection system on an NVIDIA Jetson Nano embedded platform, the real-time performance of detection is ensured while the detection precision is ensured, the operation consumption is reduced, and marginalized intelligent detection is realized.

The invention discloses an intrinsic safety type intelligent AI network camera and an image analysis system thereof, and relates to the technical field of image analysis, and the scheme is characterized by comprising an image feature analysis module, a rock stratum monitoring module, a risk assessment module and a behavior safety module. According to the method, the accuracy of crack identification and behavior analysis is improved through refined image data processing logic, the width, depth and distribution mean value of a crack pixel region are meticulously calculated, and a crack distribution table is constructed, so that the system can accurately analyze the crack expansion trend and determine the expansion direction and speed, the risk identification accuracy is improved, and the risk identification efficiency is improved. The prediction reliability of future fracture behaviors, the capability of evaluating the rock stratum stability in real time and the capability of predicting the collapse risk are enhanced, so that the system can warn potential risks in advance, provide a basis for safety management, analyze miner behaviors in real time, effectively identify potential abnormities, reduce potential safety hazards and enhance the safety monitoring and management capability of an under-mine environment.

The invention relates to the technical field of agricultural pest detection, in particular to an improved YOLOv8n-based pest detection method, which comprises the following steps of: performing data acquisition by adopting an automatic pest acquisition device consisting of trap lamp equipment, a solar panel and a network camera; constructing a pest data set; a YOLOv8n model is improved, and an iMLCA framework, a CSPPC module and a FASFFHead detection head are fused in the YOLOv8n model. According to the method, efficient detection of the pests is achieved by providing a detection model based on YOLOv8n improvement, namely a Pest-YOLOv8n algorithm, the algorithm combines an iMLCA architecture, a CSPPC module and a FASFHead detection head, higher feature extraction and detection capacity is shown, and the detection precision of the model on the pests in the complex and changeable environment is effectively improved.

The invention relates to an intelligent security management system for a sewage pumping station, which relates to the technical field of intelligent security management and comprises a front-end sensing layer, a transmission layer, a platform layer and an application layer, the front-end sensing layer is composed of a high-definition network camera deployed on the enclosure wall of the pump station and the top of a process area, a face recognition terminal installed on the side wall of a doorframe of an entrance and exit and a license plate recognition unit, the camera is powered by a POE, and the access controller is connected with a biological recognition card reader through an RS485 bus. Omnibearing monitoring and accurate identity verification of a pump station area are achieved through a high-definition network camera of the front-end sensing layer and the biological recognition technology, illegal invasion is effectively prevented, the transmission layer is combined with the wireless Mesh network through the industrial Ethernet, the stability and the anti-interference capacity of data transmission are ensured, and the service life of the transmission layer is prolonged. And the smoothness of a monitoring picture can be kept in a complex electromagnetic environment, and rapid fusion of multi-source data and efficient processing of service logic are realized.

The invention discloses an old network camera networking system, method and equipment based on distributed control, and particularly relates to the field of distributed control. An equipment registration and discovery module scans old network cameras in a local area network through an ONVIF protocol, obtains information of the old network cameras and generates a pull flow mapping table; the proxy device is in butt joint with an open source gap distributed device registration interface, registers to a distributed system as a distributed network camera gateway identity, and submits subordinate media acquisition terminal capability based on a pull stream mapping table; the open source gap terminal discovers proxy equipment through a soft bus, sends a media data request instruction and attaches a terminal equipment certificate of the open source gap terminal; the proxy device verifies the terminal device certificate; the RTSP stream management module creates a pull thread for each old web camera and starts to pull a video stream, and the stream video processing module is in butt joint with an open-source gap distributed equipment registration interface and then returns the video stream to an open-source gap terminal through a distributed soft bus; and the open-source terminal receives the video stream to complete networking.

A secure transmission method includes an Internet Protocol (IP) camera (IPC) that encrypts a to-be-transmitted video stream based on a pre-obtained first stream key to obtain an encrypted video stream. The IPC encrypts the first stream key based on a pre-obtained first wrapping key to obtain a first encrypted stream key. The IPC sends a data stream to a cloud platform, where the data stream includes the encrypted video stream and the first encrypted stream key. The cloud platform stores the data stream. A user equipment receives the to-be-processed data stream from the cloud platform. The user equipment decrypts the first encrypted stream key based on a pre-obtained first wrapping key to obtain the first stream key. The user equipment decrypts the encrypted video stream based on the first stream key to obtain a to-be-played video stream.

The invention discloses an active and passive combined video monitoring system fault positioning method, which comprises the following steps of: S1, acquiring a real-time monitoring video stream, and acquiring video frame data, a video frame statistical index and video stream session information from the real-time monitoring video stream; s2, analyzing the video frame data to obtain a video fault index; s3, detecting a video server and a network path condition to obtain a network degradation condition of the network node; and S4, carrying out association analysis by taking the video fault index, the network path and the network degradation condition as data sources, and positioning the fault. According to the invention, from user perception, accurate positioning of video faults is realized, network camera faults, network transmission node faults and server faults can be positioned, an accurate monitoring means is provided for rapid repair of the video faults, and the video fault inspection and repair cost is greatly reduced.

The invention discloses a crown block safety monitoring system based on YOLO V5 and Wincc, belongs to the field of monitoring systems, and aims to solve the problems that a crown block monitoring system mainly depends on manual judgment and is easily influenced by experience and judgment of an operator, and the operation risk is increased. The system comprises a data acquisition module, an image vision module, a hoisting state judgment module and an abnormity alarm module, the data acquisition module is used for acquiring variable point locations of the crown block PLC in real time; the method comprises the following steps: reading a variable point location in a PLC (Programmable Logic Controller) through KepServerEX software; the image vision module is used for recording a steel ladle trunnion position working image in real time from the front angle through n high-definition network cameras mounted on the two sides of a crown block working area and transmitting the steel ladle trunnion position working image to the hoisting state judgment module; the hoisting state judgment module is used for receiving the steel ladle trunnion position working image transmitted from the image vision module; and performing visual image identification on the steel ladle trunnion position working image based on a YOLO V5 algorithm, and identifying whether a lifting lug of the steel ladle and a lifting hook of the crown block are in safe positions or not.

The invention provides a cross-platform brain-computer interface normal form system suitable for natural state monitoring, and the system comprises a cross-platform development module which achieves the operation of a set of codes on a plurality of platforms through an owner-drawing engine; the task integration and expansion module is used for integrating various brain-computer interface normal form tasks; establishing a local material library management mechanism, and performing association configuration on task types and stimulation materials through a data configuration file; according to the multimedia precise synchronous control module, an asynchronous programming mechanism and a timer function of a Flutter are adopted, when audio and video materials are played or ended, a hardware circuit or streaming data of an electroencephalogram collecting device, a physiological multi-guide instrument or a network camera is labeled, alignment of multi-device and multi-modal data is achieved, and the multi-device and multi-modal data are integrated. Generating a task log containing a timestamp mark; and the natural stimulation task module is used for incorporating a task for simulating a natural scene into a brain-computer interface normal form. And a brain-computer interface system which gives consideration to cross-platform consistency and flexible configuration of natural scene tasks is provided.

The invention relates to the technical field of under-mine network image definition enhancement, in particular to a network camera image definition enhancement method and system based on deep learning. The method comprises the following steps: obtaining a network camera image of a mine underground operation area, carrying out quality label classification processing on the image, and constructing a training sample containing multiple types of image features; analyzing the difference characteristics of the training samples, and determining the degradation characteristics of the under-mine image; the method comprises the following steps: analyzing the structural loss damage degree of a key area of an image under a repaired mine based on quality reduction characteristics, generating the structural definition of the image, carrying out deep learning model training and parameter optimization based on the structural definition, determining image enhancement parameter configuration, and embedding the image enhancement parameter configuration into an image processing module of a mine network camera system. Real-time enhancement processing on the acquired image is realized; according to the invention, the definition of the network camera image is enhanced, so that the equipment under the mine runs more efficiently.

The invention discloses a video processing method and device based on OpenHarmony, electronic equipment and a storage medium, and relates to a video security encryption technology, the method comprises the following steps: networking a network camera according to a star flash technology, determining a management node and a terminal node, the network camera carrying a SwanLink OS developed based on OpenHarmony; the management node is used for determining a time mapping relation according to the time information, and the time mapping relation represents a mapping relation between the management node or the terminal node and daily time; sending the generated initial vector to a terminal node; the terminal node is used for generating an encrypted ciphertext according to the initial vector and local information; in response to the shooting instruction, determining a target node according to the triggering time of the shooting instruction and the time mapping relationship; and the target node encrypts a shot image plaintext based on a symmetric encryption algorithm according to the encrypted ciphertext and the verification information to obtain an image ciphertext. The security and encryption efficiency of the network camera can be improved.

The invention discloses a continuous zooming method for a double-camera network camera based on a three-dimensional reconstruction technology, and belongs to the technical field of cameras, and the method comprises the steps: collecting double-camera image data of the double-camera network camera, and carrying out the preprocessing of the data; performing feature extraction and matching on the double-shooting image data of the double-shooting network camera, and determining corresponding points in the double-shooting image data of the double-shooting network camera; parallax is calculated according to the corresponding points, depth information of each pixel in the scene is calculated according to the parallax, and a depth map is constructed; generating a scene three-dimensional model according to the depth map and internal and external parameters of the double-camera network camera; and simulating image effects under different focal lengths based on the scene three-dimensional model so as to realize continuous zooming. According to the invention, the problems that the image shooting effect is poor and the user experience feeling is reduced due to the fact that the existing double-camera network camera cannot be subjected to better continuous zooming are solved. According to the invention, continuous zooming can be carried out on the double-camera network camera, the image shooting effect of the double-camera network camera is improved, and the user experience can be improved.

The invention discloses a preset bit parameter setting method and device of a camera, equipment and a medium. The method comprises the following steps: setting a preset bit action, and receiving a first user image shot by a network camera; and detecting whether a preset bit action exists in the first user image, and if yes, extracting image features of the first user image. The first camera configuration is automatically scored according to the image features. And if the first configuration score is smaller than the score threshold, generating an adjustment instruction according to the image features, and sending the adjustment instruction to the network camera. And receiving a second user image shot by the network camera, and automatically scoring the configuration of the second camera according to the second user image. And if the second configuration score is greater than or equal to the score threshold, taking the adjustment amount of each parameter of the network camera as a preset bit parameter. According to the method, the first camera configuration and the second camera configuration are automatically scored, and the method has a unified scoring standard.

1. The name of the design product: the main body of the network camera. 2. The use of the design product: the design is used with the camera mounting bracket, which can be clamped on the display for face recognition, outdoor sports, video conference, and live camera device. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1. 5. Other circumstances that need to be explained: the design belongs to a partial design, the solid line part represents the protected design part, and the dashed line part represents the non-protected design part.

The utility model discloses a building security and protection monitoring system, which is used for security and protection monitoring in building floors, and is characterized by comprising a camera shooting module provided with a plurality of network cameras, and one network camera is arranged in each floor; the wireless transmission module is provided with a plurality of wireless gateways, one wireless gateway is arranged in each floor, a wireless network is connected with a network camera, the wireless gateway on the upper floor is sequentially cascaded with the wireless gateway on the lower floor, a router is arranged on the roof, an SIM (Subscriber Identity Module) card is arranged in the router, and the router is respectively connected with the wireless gateway on the uppermost floor and the monitoring center; the power supply module is provided with a plurality of energy storage battery modules, each floor is provided with one energy storage battery module, and the energy storage battery modules are respectively connected with the network cameras and the wireless gateways in the corresponding floors; according to the invention, wiring-free deployment of the monitoring system for the rear stair area of the old building is realized. And wireless signals are kept stable through short-distance multi-hop transmission.

The utility model discloses a middle frame assembly and a network camera, and the middle frame assembly comprises a middle frame support which comprises a bottom plate and a supporting assembly disposed on the top side of the bottom plate; the ball head assembly is rotationally arranged on the supporting assembly and does not make contact with the bottom plate; the first driving motor is detachably arranged on the bottom side of the bottom plate and located below the ball head assembly. According to the utility model, the middle frame support, the ball head assembly and the first driving motor are combined to form the installation main body of the network camera, and the middle frame support is used for supporting other parts, that is, the middle frame assembly serves as a main body for installing the other parts of the network camera; the other parts can be respectively installed at different positions of the middle frame assembly, the other parts cannot be influenced when each part is disassembled, and most parts which are respectively installed on the middle frame assembly can be independently disassembled, so that the network camera can be conveniently disassembled, overhauled and reassembled.

The invention discloses a centralized storage and unified management method, system, equipment and medium for a remote video monitoring system of a power generation enterprise, and belongs to the technical field of video monitoring. Identifying a monitoring blind area; replacing equipment according to an evaluation and classification result, and adding a high-definition network camera in a monitoring blind area; constructing a three-layer network architecture comprising an access layer, a convergence layer and a core layer, wherein the core layer adopts redundant connection; optimizing a hard disk video recorder deployment scheme based on the network topological relation; establishing a double-layer storage architecture, and adjusting a data uploading strategy according to a network state and a video recording type; unified access of devices of different manufacturers is realized through protocol conversion; and generating a monitoring interface according to the user role. Scientific classified updating of equipment is realized through a multi-dimensional equipment evaluation model, a transmission path is shortened through network hop count minimization and optimization deployment, bandwidth peak shifting utilization is realized through double-layer storage and intelligent scheduling, and unified and efficient management is realized.

The application discloses a kind of network camera's video transmission method, system, device and storage medium, comprising: terminal obtains the equipment information of network camera by near field communication, according to equipment information, second instruction is sent to network camera, to make network camera send video acquisition request to client;Client receives video acquisition request, if video acquisition request meets the requirement, first identity check information is sent to terminal;Terminal receives first identity check information, and second identity check information is called back to client;Client receives second identity check information, carries out identity check according to first identity check information and second identity check information, if terminal identity check passes, first instruction is sent to network camera, to make network camera send video that meets video acquisition request to terminal.The embodiment of the application can safely and conveniently acquire the video of network camera, and can be widely applied in video processing technical field.

This invention relates to a domestically produced method for monitoring the unauthorized use of mobile phones, belonging to the field of information security protection technology. The system of this invention includes network cameras, a domestically produced intelligent server, access control, client terminals, a video wall, and a switch. The network cameras and access control are deployed in the monitoring location; the switch and the domestically produced intelligent server are deployed in the server room; the video wall is deployed in the monitoring room; and the client terminals are deployed in the office. The method of this invention includes: video acquisition, video analysis, display of analysis results, and access control linkage control. This invention uses domestically produced CPUs and domestically produced accelerator cards, making it more secure and eliminating the risk of sanctions or backdoors; it can automatically control access, promptly preventing unauthorized mobile phone users from escaping the scene, thus buying valuable time to prevent information leaks.

The utility model discloses a POE network camera module for visual binocular splicing of an outdoor courtyard robot, and relates to the technical field of camera modules. The main board is fixedly connected to one side of the mounting substrate; the two lenses are fixedly connected to one side of the main board; and the stabilizing module is located on the mounting base plate, the stabilizing module comprises two mounting frames, the two mounting frames are fixedly connected to one side of the mounting base plate, and fixing ring pieces are fixedly connected to the two mounting frames. According to the outdoor courtyard robot vision binocular splicing POE network camera module disclosed by the utility model, elastic potential energy pre-stored by the coil spring is converted into traction force of the winding rope, and separation detection triggering is combined, so that the clamping piece can be driven to keep the lens stable when the lens is separated, the lens displacement risk in an outdoor bumpy environment is effectively dealt with, and the service life of the lens is prolonged. And damage to a mainboard interface or a connector caused by pulling of a flat cable due to lens separation is avoided.

The application discloses an ADHD multi-feature extraction and fusion classification method based on original videos, and comprises the following steps: S1, a network camera is used to collect video records of a subject watching videos, the videos are preprocessed, and preprocessed image frames are obtained; S2, the preprocessed image frames are analyzed to obtain behavior modes including facial actions, eye movements and head movements; S3, feature components of the behavior modes are extracted and fused; and S4, a deep learning network is constructed to classify the fused features. The application further discloses an ADHD multi-feature extraction and fusion classification system based on original videos. The application classifies ADHD patients based on video sequences, avoids invasive influence, reduces cost and is easy to popularize. Through multi-modal feature fusion, the limitation of single modal data is reduced, better accuracy and effectiveness are achieved, and in addition, the application can also be used for classifying autism cases.

The invention discloses a hydraulic engineering slope safety monitoring device and monitoring method based on Internet of Things and image recognition, and relates to the technical field of hydraulic engineering safety monitoring. The device comprises a monitoring instrument bearing table, a side surface acquisition mechanism, a bottom mounting and adjusting mechanism and a movable sliding table integrated with a displacement and image sensor. Transverse self-adaptive movement of the monitoring platform is achieved by installing the adjusting mechanism at the bottom, the vertical height of the sensor is adjusted through the movable sliding table and the limiting sliding plate, and the problems that a traditional monitoring point is fixed in position and difficult to dynamically cover are solved. The device integrates various sensors of soil pressure, displacement, image, wind speed and wind direction and the like, and realizes energy self-sufficiency through a solar power supply system. The side collection mechanism has a pneumatic self-cleaning function and can automatically remove pollutants on the surface of the sensor. All data are processed by the control panel and uploaded to the cloud platform by the network camera through the Internet of Things for intelligent analysis and early warning. According to the invention, integration of multi-parameter synchronous acquisition, point location adaptive adjustment, self-cleaning maintenance and remote intelligent early warning is realized, and the automation level and early warning timeliness of slope safety monitoring are significantly improved.