714 results about "Thermographic camera" patented technology

The invention discloses a multi-modal visual fusion complex scene small target detection tracking method and system, and relates to the technical field of unmanned aerial vehicle target tracking, and the method comprises the steps: employing a visible light camera, an infrared thermal imager and a laser radar sensor which are carried on an unmanned aerial vehicle platform, and synchronously collecting RGB images, thermal infrared images and point cloud data; the consistency of the multi-modal data is ensured through data preprocessing and space-time alignment; constructing a lightweight double-branch network to extract multi-scale features, generating a fusion feature map by adopting adaptive weighted fusion, and generating depth information by utilizing point cloud to assist in scale estimation; a small target detection head is designed based on the fusion feature map, and precise detection is realized in combination with a feature pyramid network, adaptive scale prediction and a context awareness suppression mechanism; furthermore, through multi-mode cooperative tracking, including target association, spatio-temporal context modeling, trajectory prediction and a re-detection mechanism, tracking continuity is ensured.

The invention relates to the technical field of image detection, in particular to a visible light and infrared image combined photovoltaic defect detection method based on an unmanned aerial vehicle, which comprises the following steps of: determining a photovoltaic power station detection area, carrying out synchronous aerial photography by using a visible light camera carried by the unmanned aerial vehicle and an infrared thermal imager, and carrying out local division to extract texture and temperature characteristics; the screening area performs fitting affine parameter generation on an extraction center point, corrects an infrared image to extract contour lines and texture change features, and screens a defect area to extract a positioning coordinate set; according to the method, through synchronously screening generated visible light and infrared image pairs, the resolution of an abnormal region is enhanced, a complex background and an abnormal target are accurately separated, fine-grained adaptive registration is realized through central point extraction and affine parameter derivation, and defect region characteristics are verified bidirectionally through a temperature contour closing proportion and a texture density variable quantity; thermal features and texture features are fused in the defect screening process, the recognition capability of weak anomalies is enhanced, and the defect positioning accuracy is improved.

The invention provides an intelligent inspection method and system for fan blades based on an unmanned aerial vehicle. The method comprises the steps that a visible light camera, an infrared thermal imager and a laser radar are integrated on the unmanned aerial vehicle; generating a flight route covering the whole surface based on the fan blade three-dimensional model; when the unmanned aerial vehicle is controlled to fly along the flight route, visible light images, infrared temperature data and laser point clouds are synchronously collected; the visible light image, the infrared temperature data and the laser point cloud are input into a defect analysis model, the defect type and grade are identified through conjoint analysis, a temperature abnormal area is positioned, and geometric deformation is quantified; a health assessment report is generated, the health assessment report comprises a defect positioning map and a graded maintenance decision matched with defect grades, multi-source data are collected through integration of a visible light camera, an infrared thermal imager and a laser radar, and then fusion analysis and recognition are carried out, so that compared with a manual inspection mode, the intelligent level of fan blade inspection is improved, and the inspection efficiency is improved. And the inspection efficiency of the fan blade is also improved.

The invention discloses a ship plane segmented welding multi-robot path planning system based on visual inspection, and relates to the technical field of ship manufacturing, the ship plane segmented welding multi-robot path planning system comprises a multispectral visual inspection module which is mounted on a portal frame and comprises a laser scanning sensor and an infrared thermal imager, the system is used for collecting three-dimensional point cloud and temperature distribution data of ship plane segments in real time. According to the ship plane segmented welding multi-robot path planning system based on visual inspection, the problem of path planning misalignment caused by assembly errors, thermal deformation and complex working conditions in large ship plane segmented welding is fundamentally solved through a multispectral visual real-time perception and material self-adaption dynamic correction mechanism. A composite correction vector and space-time grid collaborative pre-judgment technology is adopted, the welding seam track precision is effectively improved, it is ensured that the weld leg size strictly reaches the standard in the high-difficulty processes such as wrap angle welding and vertical position welding of the maximum-size workpiece, the defects such as air holes and cracks are eliminated, and the rework rate and the quality risk are greatly reduced.

The invention relates to the technical field of substation equipment fault diagnosis, in particular to a substation equipment thermal fault diagnosis method and system based on an infrared image. The invention discloses a substation equipment thermal fault diagnosis method based on an infrared image. The method comprises the following steps: acquiring an equipment temperature distribution image through an infrared thermal imager; carrying out denoising, contrast enhancement and normalization preprocessing on the image; extracting features such as temperature anomaly, temperature gradient and hot spot areas; a YOLOv13 model is adopted to identify the equipment type; inputting the features into a deep learning model for fault classification; and implementing multi-level alarm according to the classification result confidence. According to the method, temperature gradient analysis and a regional dynamic contrast enhancement technology are creatively fused, so that the fault detection precision and the early warning capability are remarkably improved, and intelligent diagnosis and graded early warning of the thermal fault of the substation equipment are realized.

The invention relates to a shipbuilding safety monitoring system and a monitoring method thereof, and belongs to the technical field of shipbuilding monitoring, and the system comprises a multi-modal data collection module, an edge computing node, a digital twin platform, a digital twin platform, an analysis module, a block chain traceability module, and a feedback control module. The multi-modal data acquisition module comprises a visual sensor, an acoustic sensor, a thermal imager, a strain sensor, a laser displacement sensor and an environment sensor. The visual sensor, the acoustic sensor, the thermal imager, the strain sensor and the environment sensor are installed in a shipbuilding workshop. According to the invention, by fusing visual, acoustics, thermodynamics and other multi-sensor data and combining edge calculation real-time processing and digital twinning dynamic simulation, process quality prediction, defect detection and carbon emission tracking are realized, and the problems of insufficient real-time performance, difficulty in cross-system collaboration and lack of green manufacturing monitoring in the prior art are solved.

The invention relates to the technical field of intelligent warehousing monitoring, and discloses an intelligent warehousing monitoring system based on digital twinning, which comprises a data acquisition layer, a digital twinning modeling layer, an intelligent analysis layer and an execution control layer, and is characterized in that three-dimensional coordinates and temperature distribution data of goods are acquired through a laser radar array and an infrared thermal imager; constructing a dynamic point cloud data set in combination with RFID positioning; an improved YOLOv7 model is adopted to be embedded into a CBAM attention mechanism to recognize the cargo form risk, and an LSTM time sequence model is fused to predict a temperature anomaly trajectory; an AGV obstacle avoidance path is optimized based on a genetic-ant colony hybrid algorithm, and a temperature control system and a security device are driven to perform linkage execution through an OPC UA protocol. And finally, millisecond-level synchronous mapping of the physical warehouse and the digital twinborn body, real-time deviation correction regulation and control of an equipment running track and cross-system collaborative response of an emergency strategy are realized, and the safety early warning accuracy and dynamic protection robustness of the storage environment are improved.

The invention relates to the technical field of new energy aluminum alloy precision casting multi-modal quality monitoring, in particular to a new energy aluminum alloy precision casting multi-modal quality monitoring device and method. According to the technical scheme, the new energy aluminum alloy precision casting multi-mode quality monitoring device comprises a quality monitoring device body, an infrared thermal imager probe, a germanium single crystal protection lens, an integrated spectrometer probe debugging port, an acoustic detection microphone array and a honeycomb acoustic shielding cover. An infrared thermal imager probe is arranged on one side of the quality monitoring device main body, and a germanium single crystal protection lens is arranged on the outer side of the infrared thermal imager probe; synchronous acquisition and fusion analysis of multi-physical field data are realized by integrating six modes of spectrum, infrared thermal imaging, vibration, machine vision, acoustics and pressure deformation, and meanwhile, when the shrinkage risk is predicted by the infrared thermal imaging, a die-casting process compensation mechanism is automatically triggered to kill defects in the germination stage, so that the defect rate is greatly reduced.

The invention provides an intelligent detection method and system for the tensile property of a plastic cable tie. Gradient temperature rise control is executed through the carbon fiber heating tube array, microscopic deformation fluctuation characteristics are captured through the double-grating displacement sensor, and temperature field distribution is collected through the infrared thermal imager. Establishing dynamic correlation between deformation and a temperature field to generate a three-dimensional space-time atlas, identifying a migration path of a thermal aggregation area, recording a standard path, and calculating a thermal anomaly coefficient in combination with pressure data; analyzing temperature conduction hysteresis to generate a heat conduction parameter, and dynamically correcting a threshold value by cross-modal correlation with an abnormal coefficient; a structure degradation index is generated based on the migration path, mapping between the structure degradation index and a temperature field is established to generate a thermal damage compensation coefficient, an abnormal coefficient is calibrated accordingly, and grading early warning is triggered in combination with the path deviation degree. According to the technical scheme provided by the invention, multi-scale damage evaluation from thermodynamic associated data acquisition to molecular chain fracture inversion is realized, and the high-temperature tensile failure critical point is accurately pre-judged through a dynamic calibration early warning mechanism.

The invention relates to the technical field of massage, in particular to an intelligent abdominal acupoint massage system and method based on multi-modal sensing, and the system comprises a multi-modal sensing module which comprises a 3D depth camera, an infrared thermal imager, a myoelectricity sensor and a millimeter wave radar; the intelligent decision-making module is used for updating a massage strategy in real time by applying a Poisson reconstruction algorithm, a multi-modal fusion positioning algorithm and a dynamic coordinate mapping method; the execution device comprises an end effector and is composed of a bionic massage head for executing massage, a six-dimensional force sensor for feeding back a pressure value in real time and an infrared temperature control module; the three-degree-of-freedom mechanical arm is composed of xyz translation compensation and a rotating joint and is used for controlling the end effector to move; the power module is used for supplying power to the system. The device has the advantages that accurate acupoint tracking and positioning can be provided for a user, safe, controllable, autonomously adjustable massage force is provided, and different massage schemes are set according to individuation of the physique of the user.

The invention relates to the technical field of health care, and discloses a bath chair self-adaptive adjusting system based on multi-modal sensing. According to the system, a pressure sensor array, an infrared thermal imager and an inertial measurement unit are used for collecting multi-dimensional real-time data, and the multi-modal sensing fusion module is used for processing the multi-dimensional real-time data to generate a structured physiological feature vector. The user intention recognition module predicts user behavior intention and gives a comfort level score on the basis, and the posture self-adaptive adjustment module optimizes seat supporting curved surface parameters according to the comfort level score to achieve real-time adjustment. In addition, the safety monitoring module evaluates the slip risk, the closed-loop control module guarantees safety, the online learning module optimizes the adjustment strategy, and the abnormal response module deals with the emergency situation. According to the system, the comfort and safety of the bath chair are improved, personalized adjustment is achieved, and the system has high practical value.

The invention provides a three-dimensional vision and infrared thermal imaging combined calibration method and system and a medium, and the method comprises the steps: carrying out the processing of a calibration plate based on a special infrared response material or an electric heating insert, and obtaining a visible light angular point pattern and a calibration plate of a thermal response structure region; shooting an image of the calibration plate based on a three-dimensional vision camera, and extracting image coordinates; acquiring an infrared image of the calibration plate to obtain a thermal image, and analyzing distribution information of thermal characteristic elements on the calibration plate; establishing a nonlinear mapping relationship between the image coordinates and the distribution information of the thermal characteristic elements; analyzing a projection error based on the nonlinear mapping relation, and performing parameter calibration on the three-dimensional vision camera and the infrared thermal imager based on the projection error; the thermal imager and the three-dimensional vision camera are responded at a time through the bimodal calibration plate, a nonlinear injection relation is established, parameter calibration is accurately carried out on the three-dimensional vision camera and the infrared thermal imager according to projection errors, the calibration precision is improved, and the method is suitable for complex environments such as industry and medical treatment.

The invention relates to the technical field of infrared thermal imaging, and particularly discloses a fire personnel intelligent search and rescue method and device based on infrared thermal imaging, and the method comprises the steps: synchronously collecting fire scene data through an infrared thermal imager and a laser radar, so as to obtain infrared thermal imaging data and fire scene three-dimensional point cloud data; further introducing a data processing technology based on deep learning to carry out feature extraction on the infrared thermal imaging data and the three-dimensional point cloud data of the fire scene so as to capture infrared thermal radiation distribution features and three-dimensional space structure features of the fire scene, and then carrying out fine-grained feature alignment joint perception on the infrared thermal radiation distribution features and the three-dimensional space structure features; therefore, the deep correlation characteristics of the fire scene heat distribution and the three-dimensional space can be captured, and the intelligent detection of the human body target can be realized on the basis. In this way, the problem that heat radiation information and space structure information are separated in a traditional method can be effectively solved, the information utilization rate is increased, and the accuracy and efficiency of human body target recognition are enhanced.

The invention relates to the field of virtual reality (VR) and medical image processing, in particular to an acupuncture copper human visual frame selection system combined with 3D digital human body modeling. The main components of the scheme comprise 3D modeling software, a multi-level 3D acupuncture copper human model (including skin, meridians and collaterals, acupuncture points and skeleton layers), a J2000 coordinate system, an interactive interface, an infrared thermal imager, a six-axis force sensor, an AI clustering algorithm, a force steering algorithm, a decision tree algorithm, an ICP algorithm, a v-mode l technology, a BERT model and the like, and the components work cooperatively to construct a high-precision 3D model and calibrate coordinates. A frame selection instruction is received through multi-modal interaction, temperature and force data are collected to generate a dynamic matrix, label arrangement and rule generation are carried out, multi-source data are integrated and exported, real-time binding and intelligent recommendation of acupuncture points and models are achieved, a complete acupuncture point visual frame selection system is formed, and the acupuncture point positioning accuracy and the operation intelligent level are improved.

The invention discloses a method and device for testing the fatigue crack growth rate of a metal material based on temperature gradient change, and relates to the technical field of metal material detection. Mounting a constant low-temperature component and a constant high-temperature component close to two ends of the sample respectively; pasting a plurality of thermocouples on the middle surface of the sample; a magnetic powder dynamic moving assembly is installed on the surface of the sample, and a constant-temperature state is given to magnetic powder; loading a bidirectional tension-compression fatigue testing machine along the through hole of the sample; a thermal imager is arranged far away from the parallel sample, and a thermal imaging image is shot; according to the data, the fatigue crack growth rate of the sample at the corresponding temperature is obtained; according to the method, the fatigue crack growth rate of the metal material can be rapidly tested, meanwhile, the temperature gradient is formed in the sample, the influence of the temperature on the crack growth rate can be obtained in different temperature areas, the device required by the whole test is simple in structure, low in cost and convenient to load and disassemble, and the whole efficiency of the test is effectively improved.

The invention discloses a cable defect detection method of a binocular intelligent inspection robot, and relates to the field of image processing, and the method comprises the steps: firstly obtaining the state information of a cable, then carrying out the preprocessing and stereo matching of the image information of the cable, and then constructing a target detection and segmentation model; the target detection and segmentation model is adopted to detect and segment cable defects, and model sample data is updated in real time in an incremental learning mode; fusing the cable state information acquired by the infrared camera, the thermal imager and the wireless sensor network by using a multi-sensor data information fusion model; the cable is maintained and managed based on a detection result; according to the invention, the detection precision and accuracy can be improved, cables of different types and specifications can be handled, the adaptability and stability are ensured, and the real-time performance and accuracy of detection can be improved.

The invention relates to an earthquake intensity field disaster situation acquisition system, and discloses an earthquake intensity field disaster situation acquisition system, which comprises the following modules: an unmanned aerial vehicle hardware platform, a disaster situation data acquisition module, an intelligent disaster situation analysis module, a disaster situation prediction and decision support module and a disaster situation return and dynamic response module. The unmanned aerial vehicle carries a high-definition camera, a thermal imager, a laser radar sensor and a meteorological sensor, collects image data, thermal imaging data, three-dimensional point cloud data and meteorological data of a disaster area in real time, and carries out disaster identification and evaluation through a deep convolutional neural network. In combination with multi-sensor data, the system can automatically identify disaster information such as building damage degree, fire areas, casualties and the like, intelligently optimizes resource allocation according to disaster assessment results, and ensures that rescue resources can be timely and accurately allocated to most required places of disaster areas.

The invention relates to the technical field of spinning quality detection, and discloses a jute spinning product quality detection method and system based on an intelligent sensor. The method comprises the following steps: acquiring real-time process parameters of a jute spinning production line, and synchronously capturing yarn surface temperature field distribution in combination with an infrared thermal imager; performing space-time alignment processing on the yarn tension fluctuation data and the temperature field distribution to generate a fused dynamic process characteristic matrix; according to the tension gradient change rate and the temperature field abnormal region coordinates in the dynamic process characteristic matrix, dividing potential occurrence sections of yarn quality defects; a microstructure image of the yarn in the potential generation section is extracted, a fiber arrangement angle deviation value is measured through a polarized light interferometer, and fiber molecular bond vibration frequency data is obtained in combination with a Raman spectrometer; and inputting the fiber arrangement angle deviation value and the molecular bond vibration frequency data into a cascade quality analysis model, and outputting a quantitative index set of the internal structure defects of the yarns.

The invention provides a control method and device of a massage robot, electronic equipment and a readable storage medium, and the method comprises the steps: scanning the acupoint position of each human body acupoint in an initial to-be-massaged area of a target user through a depth camera; detecting a key massage area in the initial to-be-massaged area through an infrared thermal imager; determining a to-be-massaged target meridian of the target user based on the position of the key massage area on the target user; generating a massage path based on each human body acupoint on the target meridian and the acupoint position of each human body acupoint on the target meridian; and controlling a mechanical arm of the massage robot to execute massage operation on the target user according to the massage path. Through the method, the massage effectiveness of the massage robot is improved.

The invention discloses a lime kiln monitoring system based on multi-source data dynamic fusion, which is characterized by comprising a thermal imager, an infrared sensor, a pressure sensor, a gas analyzer and an edge computing node, the edge computing node is configured with a Raspberry Pi platform and an LSTM neural network accelerator and is used for executing multi-source data preprocessing and feature compression, and the Raspberry Pi platform and the LSTM neural network accelerator are connected with the thermal imager. The system also comprises a working condition analysis system, and the working condition analysis system comprises a ring formation prediction model based on ST-GCN, a combustion optimization model based on DDPG, and an anomaly detection model based on an isolated forest and automatic encoder hybrid model. The invention aims to solve the limitation of a traditional monitoring system, realize real-time monitoring and accurate control, improve the equipment health management and predictive maintenance capability, optimize waste heat recovery and energy utilization, and enhance man-machine interaction and operation convenience.

The invention discloses an infrared thermal image enhanced real-time rectal blood vessel blood flow detection method. The method comprises the following steps that S1, a thermal image of an operation area is collected through an infrared thermal imager to serve as a to-be-processed medical purpose image; s2, adopting a double-branch collaborative network architecture, adopting an improved U-Net structure for a deep denoising priori network DDPNet, introducing a detail sensing unit DAU, and separating noise and organizational structure features through residual learning; and S3, obtaining a supervision signal in the image, and processing each supervision model to obtain a processed image. The method can effectively improve the accuracy and comprehensiveness of information obtained after image processing in the operation and improve the effectiveness of information acquisition in the operation process.

The invention relates to the technical field of unmanned aerial vehicle radio detection, in particular to an unmanned aerial vehicle detection and judgment method based on cooperation of an air-ground system and radio detection, and the method comprises the steps: finding a suspected target through scanning of a multi-frequency-band radar, triggering an infrared thermal imager carried by an aerial unmanned aerial vehicle to approach an ultra-wide-frequency-band spectrum sensor for verification; accurate positioning is realized by combining ground photoelectric equipment multi-view triangulation positioning and a signal arrival time difference algorithm, a command and control center calculates threat levels through a multi-source data fusion engine, and navigation decoy of dynamically adjusting intensity is adopted for targets smaller than or equal to two levels. Hard killing of ground laser locking rotor center and air electromagnetic pulse synchronous coverage is executed on a target of more than or equal to three levels, the equipment state is synchronized in real time through an optical fiber link, high-energy-consumption equipment is intelligently closed after threat is relieved, a platform starting strategy is optimized, a detection closed loop of'three-dimensional perception-graded treatment-intelligent energy saving 'is constructed, and the intelligent energy saving effect is achieved. And an efficient and accurate low-altitude safety protection scheme is provided for a sensitive area.

The invention discloses a livestock identity recognition method and system based on an unmanned aerial vehicle, and relates to the technical field of livestock identity recognition, and the method comprises the steps: constructing an identity information database which comprises the electronic ear tag IDs of all target livestock individuals in a pasture, a visible light feature image, an infrared thermal imaging contour template and a historical motion behavior sequence in a cloud server; initialization of an unmanned aerial vehicle system carrying a visible light camera, an infrared thermal imager, an RFID reader and an edge calculation module is completed, and communication connection between the unmanned aerial vehicle system and a ground control station and between the unmanned aerial vehicle system and a cloud server is established; controlling the unmanned aerial vehicle to enter an inspection area, monitoring a ground target through a visible light camera, synchronously acquiring a visible light image, an infrared thermal image and a radio frequency response signal of a target individual when the livestock is detected to enter an effective identification range, and performing timestamp alignment on the three types of data; a first visual feature vector is extracted from the visible light image and a visual confidence score is generated.

The invention discloses a multi-modal fusion AI visual detection method. The method comprises the following modules: a multi-modal data acquisition module, a data preprocessing and aligning module, a multi-modal feature fusion AI module, a real-time decision and feedback module, and a model iteration and data management module. The multi-modal data acquisition module comprises a high-resolution visible light camera, an infrared thermal imager, a high-precision line laser scanner and a synchronous trigger controller; the method further comprises the following steps: step 1, an early-stage preparation stage; 2, a detection execution stage; 3, a decision-making and feedback stage; 4, performing daily maintenance and iteration; the invention aims to provide a multi-modal fusion AI visual detection method, so as to solve the problems that the existing method depends on a single detection mode, only surface texture defects can be identified, and internal problems or three-dimensional form defects of materials cannot be considered; and a traditional detection mode often depends on manual judgment or a semi-automatic process, is low in efficiency and is greatly influenced by subjective factors.

The invention discloses a substation equipment data defect identification method and system, and relates to the technical field of substation part defect detection. The method comprises the steps of porcelain bushing state analysis: scanning a target porcelain bushing through a camera carried by an unmanned aerial vehicle to generate a three-dimensional point cloud model, and judging the state and flight environment of the porcelain bushing; a shooting angle coarse adjustment step: adjusting the shooting angle of the thermal imager according to the state of the porcelain bushing; a flight path planning step of presetting a shooting point position according to the three-dimensional point cloud model and planning a flying path; an image acquisition and splicing step: acquiring and splicing thermal imaging images; the method has the advantages that through strategies such as porcelain bushing state analysis, point position supplementation and obstacle avoidance in flight path planning and panorama complementation in image splicing, it is ensured that complete and effective thermal imaging information can still be obtained under the complex conditions that the porcelain bushing is upright or inclined, and flying obstacles exist; accurate judgment of the abnormal condition and the abnormal area of the porcelain bushing is realized.

The invention relates to the technical field of unmanned aerial vehicle autonomous reconnaissance, in particular to an unmanned aerial vehicle reconnaissance decision-making method, device and equipment based on edge calculation and a medium, and the method comprises the steps: obtaining the multi-dimensional data of an environment and a target through an optical camera, an infrared thermal imager and a radar carried by an unmanned aerial vehicle; target identification and strategy reasoning are carried out on the multi-dimensional data, a target type and attributes are identified from the multi-dimensional data through a lightweight convolutional neural network, and a battlefield situation is obtained in advance to generate a strike strategy; obtaining a threat level and a task priority, and dynamically adjusting the flight path of the unmanned aerial vehicle and the strike strategy according to the threat level and the task priority; and obtaining a strike result corresponding to the strike strategy, analyzing the strike result in real time, judging a target destroy state through an airborne sensor, and updating a subsequent task strategy. According to the invention, the autonomy of the unmanned aerial vehicle system is improved.

The invention relates to the technical field of warehouse management, in particular to a material inspection method and system for an emergency warehouse. Comprising the following steps that multi-dimensional data of materials in an emergency warehouse are collected in real time through a multi-modal sensor array, and the multi-modal sensor array comprises an RFID reader-writer, an infrared thermal imager and a 3D visual sensor; dynamically grading the materials in the warehouse based on a material priority classification model, wherein the priority classification model considers the validity period, the use frequency and the emergency response level of the materials; constructing a material three-dimensional digital twinborn model, and comparing the collected multi-dimensional data with preset material standard parameters in real time; when the comparison difference exceeds a preset threshold value, abnormal early warning is triggered, and an inspection task is generated; planning an optimal inspection path for the automated guided vehicle based on an improved conflict avoidance algorithm; according to the invention, material inspection with dynamic standard adaptation and intelligent priority adjustment can be realized.

The invention provides a converter valve key component burning defect identification method based on a neural network, and belongs to the technical field of power electronic equipment fault diagnosis, and the method comprises the steps: collecting multi-modal data through an infrared thermal imager and a plurality of sensors, and inputting the pre-processed multi-modal data into a specially designed neural network architecture; the framework comprises an image feature extraction module, a time sequence feature extraction module, a feature fusion module and a classification positioning module. According to image processing, improved ResNet50 is combined with an attention mechanism, time sequence features are extracted through a bidirectional long-short-term memory network and a time convolution network, and effective feature combination is achieved through a dynamic weight fusion mechanism. Meanwhile, a double-phase heat conduction model is introduced to analyze temperature distribution, accurate identification of burning defects of key components of the converter valve in a complex environment is realized through large-scale data set training and a two-stage optimization strategy, and key technical support is provided for safe operation of a power system.

The invention discloses a mental health monitoring and nursing robot with body-multi-dimensional intelligent cooperation. The robot comprises a mobile robot platform equipped with a differential driving chassis, a laser SLAM navigation module and the like; a multi-mode sensing network integrating an RGB-D depth camera, a thermal imager and the like processes data in real time through an edge computing unit; an intelligent decision-making engine based on a micro-service architecture covers a patrol recognition module, a vital sign monitoring module, a psychological support Transform dialogue model, a multi-modal emotion recognition module and the like; and the man-machine interaction interface is connected through an end-to-end encrypted 5G network. A medicine compliance management module, an RFID medicine box and a privacy protection scheme of medicine taking action recognition and federal learning and differential privacy are further arranged. The method has the advantages that nursing efficiency is improved by more than 40%, adverse event occurrence rate is reduced by 35%, psychological intervention coverage rate is improved, medicine taking compliance is improved, sublingual Tibetan medicine detection rate is improved to more than 95%, data privacy is safe and controllable, and intelligent upgrading of mental health nursing is promoted.

The invention belongs to the technical field of fire early warning, and discloses an indoor fire early warning system based on multi-source sensing, which comprises a multi-source sensor module, a feature extraction module, a decision module, an early warning module and a user interaction module, the multi-source sensor module realizes multi-dimensional fire characteristic data acquisition from four aspects of smoke, temperature, gas and infrared thermal imaging. By arranging the multi-source sensor module, a smoke sensor, a temperature sensor, a gas sensor and an infrared thermal imager are combined, multi-source monitoring of a fire is achieved, meanwhile, feature extraction and decision fusion are conducted on smoke concentration, temperature, gas concentration and an infrared thermal imaging picture through mutual cooperation of the feature extraction module and the decision module, and the accuracy of the fire monitoring is improved. Compared with a traditional single sensor, the system has the advantages of being higher in detection precision and low in false alarm rate, and fire early warning can be achieved in a real sense.