574 results about "Thermography" patented technology

The invention provides an all-terrain autonomous navigation and task planning method and system for a photovoltaic inspection robot, and relates to the technical field of photovoltaic inspection, and the method comprises the steps: constructing a three-dimensional grid map and generating an initial inspection path, dividing hexagonal sub-regions and calculating a passing cost matrix, and optimizing a global inspection path based on the cost matrix. The motion state is predicted in real time, the motion track is corrected, thermal imaging and visible light data are collected to generate an inspection report, autonomous navigation and efficient inspection under complex terrains can be achieved, the inspection efficiency and accuracy are improved, and the labor cost is reduced.

The invention provides an intelligent asphalt pavement segregation detection method based on multi-source data analysis, and relates to the technical field of road engineering detection.The method comprises the steps that a to-be-detected road section is determined, and multi-source data of the to-be-detected road section is obtained; preprocessing the multi-source data; constructing a double-branch neural network detection model which comprises a first branch detection model and a second branch detection model, and inputting the preprocessed optical data and laser point cloud data into the pre-trained first branch detection model to obtain a grading segregation distribution result; inputting the preprocessed thermal imaging data into a pre-trained second branch detection model to obtain a temperature segregation distribution result; performing conjoint analysis on the grading segregation distribution result and the temperature segregation distribution result to generate a comprehensive segregation index; and outputting a detection result according to the comprehensive segregation index. According to the invention, comprehensive, accurate and automatic detection of the segregation degree of the asphalt pavement can be realized, and a scientific basis is provided for pavement quality evaluation and maintenance.

The invention relates to the technical field of image processing, and discloses a multispectral fusion night low-illumination image enhancement and shielding compensation method, which comprises the following steps of: cooperatively acquiring multi-modal data through a visible light camera, an infrared sensor, a thermal imaging sensor and a millimeter wave radar; comprising a low-illumination basic image, dark light texture details, target temperature distribution and contour and motion information of an object behind the shelter; fusing visible light and infrared textures by adopting a multi-scale transformation algorithm to generate a transition fusion image, and performing illumination compensation based on temperature distribution; predicting texture details of the occlusion area through a u-Het deep learning network in combination with detection data of the millimeter wave radar; and filling the missing texture according to the shielding contour, and generating a final target image through edge optimization and illumination smoothing technologies. According to the invention, the definition of a night low-illumination image can be improved.

The invention relates to the technical field of image recognition, in particular to a lightning arrester fault thermal imaging picture recognition method and system combined with gaze prediction, and the method comprises the steps: carrying out the down-sampling of an original thermal imaging image to a fixed size, sequentially passing through a multi-layer convolution and a Spatial Softmax layer, and outputting a predicted gaze point track sequence, extracting a key area and a non-key area according to the intensity of the fixation point track sequence; reconstructing the key area to obtain a compressed and recombined target area; inputting the non-key region into a variational auto-encoder to obtain a low-dimensional potential feature vector; and inputting the image-structure joint feature representation into an EffiCroprViT model, and finally obtaining a lightning arrester fault classification result. Local and global features are efficiently fused, the calculation complexity is reduced, background noise interference is effectively suppressed, real-time and accurate identification and early warning of the fault state of power equipment are realized, and the fault classification method has the advantages of high efficiency, high reliability and high reliability. Therefore, the safety and stability of power grid operation are ensured.

The invention relates to the technical field of power grid operation and maintenance, in particular to a power distribution network operation and maintenance inspection device and method based on artificial intelligence, and the device comprises a sound data acquisition module, a thermal imaging monitoring module, a data fusion diagnosis module and a path optimization and scheduling module. According to the invention, by integrating the acoustic sensor and the thermal imaging camera, the comprehensive state monitoring of the power grid equipment is realized, the accuracy of fault diagnosis and the foresight of operation are improved, and the potential fault point can be quickly positioned by integrating sound wave and temperature data, so that the fault early warning is more accurate, and the working efficiency is improved. The power grid safety risk caused by improper fault processing is effectively reduced, the stability and reliability of the power grid are improved, through real-time data analysis, continuous health assessment can be carried out on the power grid, a maintenance plan and resource allocation are optimized, the patrol is more efficient through automatic path optimization and a scheduling module, and the safety of the power grid is improved. The key area is ensured to be checked preferentially and thoroughly, and the overall operation efficiency of the power grid is further improved.

The invention discloses a rotor punching and stacking welding defect identification system based on edge calculation, and relates to the technical field of rotor welding identification, and the rotor punching and stacking welding defect identification system comprises a data acquisition module which is used for carrying out real-time acquisition on smoke dust data generated in the welding process, and the smoke dust data comprises smoke concentration, suspended particulate matter concentration and smoke infrared thermal imaging temperature; the model matching module is used for comparing the smoke concentration, the suspended particulate matter concentration and the smoke infrared thermal imaging temperature with corresponding preset reference models respectively to obtain abnormal data with an abnormal trend; and the judgment module is used for performing time sequence analysis on the abnormal data, calculating a standard deviation and a mean deviation rate based on a sliding window, and generating a corresponding early warning growth value. According to the method, a dynamic alarm value generation and attenuation mechanism is constructed, so that false alarm caused by continuous influence of historical data is avoided, and the real-time response accuracy of the integrated value is improved. And through a pre-judgment mechanism, it is ensured that alarm value attenuation is executed only on the premise that data are stable or no early warning growth trend exists.

The invention relates to the field of data processing, in particular to a multi-parameter collaborative optimization method and system based on a data center cooling system, and the method comprises the steps: obtaining a cooling subsystem of a computing equipment integrated cabinet, and carrying out the short-time temperature analysis of the computing equipment integrated cabinet based on infrared monitoring data and temperature sensing data, if it is confirmed that the limiting temperature of the component is not higher than the corresponding preset temperature threshold value according to the current temperature distribution, and it is confirmed that the continuous heating temperature is lower than the preset temperature change threshold value based on the temperature change trend, the temperature change trend and the current temperature distribution are obtained; if yes, returning to the step of executing multi-modal data acquisition on the computing equipment integration cabinet and the cooling subsystem, and otherwise, synthesizing the predicted temperature data, obtaining a cooling subsystem configuration strategy and setting the cooling subsystem. According to the invention, the cooling monitoring and adjustment of the computing equipment of the data center are realized by integrating the data collected by the temperature sensor and the thermal imaging probe which are reasonably arranged.

The invention discloses an unmanned aerial vehicle-mounted GNSSINS visual combination fire detection method, system and device and a medium, belongs to the technical field of unmanned aerial vehicles, and aims to solve the technical problem of how to improve the positioning precision and fire recognition capability of an unmanned aerial vehicle in a complex environment. Setting a dynamic temperature threshold value, and calibrating the threshold value in real time in combination with the environment temperature; identifying a fire source diffusion trend by adopting a temperature gradient analysis method, and excluding isolated high-temperature points; an image is collected through an RGB camera and converted into an HSV color space, flame features are extracted, and the flame shape is detected in real time through a YOLOv5 model; detecting a smoke area based on a U-Net segmentation network, outputting a binary mask, carrying out space-time alignment on the binary mask and the wind speed and wind direction of the meteorological data, and predicting the fire spreading direction; thermal imaging, visible light and smoke detection results are fused through the D-S evidence theory, and when the confidence coefficients of the thermal imaging, visible light and smoke detection results are all larger than 80%, it is judged that a fire exists.

Thermal imaging and navigation systems and related techniques are provided to improve the operation of manned or unmanned mobile platforms, including passenger vehicles. A system includes a thermal imaging device configured to be mounted on a vehicle. The thermal imaging device is configured to, when mounted on the vehicle, capture a first image of a scene encompassing a portion of the vehicle and capture a second image associated with a reflection of the scene from the portion of the vehicle. The system further includes a logic device configured to communicate with the thermal imaging device and determine a disparity map based on the first image and the second image.

Monitoring and control system and method for additive manufacturing an in-situ macro-field thermography that uses an IR camera or near-IR camera to prioritize the data captured from an entire print layer, processes data streams layer-by-layer, and provides thermal history of previously printed layers as feedback to the control system. The system leverages one or more different mathematical thermophysical-based (MTB) models to compute a thermally related specification in order to optimize the printing sequence of islands or stripes layer-by-layer. The thermal feedback from the thermography system calibrates or updates the thermal model layer-by-layer. The system and / or method may provide real-time optimization of scanning topology in chessboard and stripe printing strategies. The method may be implemented by one or more computer programs controlling one or more computer controllers of an additive manufacturing system, such as a 3D printer implementing a metal powder bed fusion additive manufacturing process.

The invention provides an interactive recognition and positioning method and system for people falling into water on the water surface. The method comprises the steps that a video acquisition module is started, and people falling into water on the water surface are searched for through a people-falling-into-water recognition module; a drowning person target positioning module is used to calculate camera coordinates of drowning persons, and a high-precision RTK positioning module is combined to convert geodetic coordinates of drowning person targets and send the geodetic coordinates to the unmanned ship; the unmanned ship decelerates after approaching a preset range of a drowning person target, a video acquisition module at a horizontal visual angle is started to acquire information of the drowning person, the drowning person target identification module is used for identifying the drowning person, and a thermal imaging camera and laser radar ranging information are fused to further identify and position a water surface target; and fusing the positioning information of the drowning personnel provided by the unmanned aerial vehicle and the unmanned ship by using a Kalman filter to obtain the accurate geodetic coordinates of the drowning personnel. The method has good practicability.

The invention provides an intelligent target identification method and system based on an unmanned intelligent turntable. According to the method, an unmanned intelligent rotary table is used for collecting a thermal imaging video at night, a thermal radiation map is generated through compensation, and a background heat conduction coefficient is extracted. After the high-temperature heat source is positioned, calculating a centroid trajectory, and combining a background coefficient and an environment gradient to establish a dynamic reference temperature field; the heat source is divided into a contact sub-region and a non-contact sub-region according to the heat exchange rate difference, and the boundary mutation of the contact sub-region is marked. And analyzing the collaborative response of the temperature gradient change rate and the boundary deformation rule of the contact sub-region to judge an abnormal behavior. If abnormal, tracking parameters are generated based on the mass center acceleration to adjust the posture of the rotary table to keep the target in the middle, and non-contact subarea temperature diffusion is utilized to verify continuity so as to optimize tracking. According to the method, intelligent discrimination and adaptive closed-loop tracking of night abnormal behaviors are realized by analyzing the dynamic thermodynamic characteristics of the heat source contact area and the deformation rule cooperative response.

The invention relates to the technical field of performance detection, in particular to a heat insulation performance detection process for an energy-saving and environment-friendly curtain wall material. Comprising the following steps: S1, cutting a sample; step S2, performing pretreatment; s3, performance testing is carried out; s4, calculating the actual thermal conductivity of the to-be-detected sample according to the actual temperature difference and the heat source condition. According to the invention, the sample area is divided into two types and temperature distribution analysis is carried out to effectively judge the accuracy of the thermal imaging image, and corresponding regulation and control are carried out according to the analysis result, so that the reliability and accuracy of the test are improved, the sample is ensured to be uniformly heated in the test process, local overheating or supercooling is avoided, and the test efficiency is improved. By monitoring a real-time displacement field and environmental parameters, whether jitter or heat flow disturbance exists in the test process can be identified, and the reliability of test data is further ensured; and comparing the calculated actual heat conductivity with a related standard to judge whether the heat insulation performance of the sample meets the requirement or not.

The invention provides a railroad sleeper crack detection method and system based on three-dimensional feature construction, and the method comprises the steps: obtaining sleeper data: obtaining a sleeper image and a thermal image; a crack sleeper screening step: screening the images of the sleepers with cracks, and calling the thermal images of the sleepers; a three-dimensional feature construction step: extracting a plane crack area, calculating the crack depth according to the thermal image through an inversion strategy, and adding the crack depth to the depth of the plane crack area to form a three-dimensional crack; a crack safety evaluation step: calibrating a main crack and a branch crack diffused with the main crack in the three-dimensional crack, analyzing a crack safety coefficient according to the size data of the main crack and the size data of the branch crack through an evaluation strategy, and judging whether the sleeper needs to be replaced or not according to the crack safety coefficient; the method has the advantages that a quantitative safety coefficient evaluation model is established through three-dimensional size analysis of the main crack and the branch cracks, and the structural stability of the sleeper can be accurately judged.

The invention relates to a mining intrinsic safety type thermal imaging video camera, and relates to the technical field of video cameras, the mining intrinsic safety type thermal imaging video camera comprises a video camera body and an adjusting assembly, the adjusting assembly comprises an adjusting part, and the adjusting part comprises an adjusting shell, an adjusting ball, an adjusting wheel and an adjusting rod; the adjusting ball is located on the opening side of the adjusting shell and fixedly connected with the camera body, and the center of the adjusting ball coincides with the center of the adjusting shell. The four adjusting wheels are all located on the side, close to the adjusting shell, of the adjusting ball, the four adjusting wheels are evenly distributed in the diameter direction of the adjusting ball, the adjusting wheels are rotationally connected to the inner wall of the adjusting shell, motors are installed at the ends of the adjusting wheels, and the rotating axes of the four adjusting wheels are all parallel to the opening side of the adjusting shell; one end of the adjusting rod is fixedly connected to the adjusting ball, the other end of the adjusting rod is fixedly connected with an adjusting sliding plate, the adjusting sliding plate is arranged in an adjusting sliding groove formed in the adjusting shell in a sliding mode, and the adjusting sliding plate pulls the adjusting rod under limitation of the adjusting sliding groove so that the adjusting ball can abut against the adjusting wheel. The method has the effect of expanding the monitoring range of the camera.

The invention provides a defect detection method and system for a prepreg laying layer and a computer storage medium, and the method comprises the steps: carrying out the frequency conversion thermal excitation of a to-be-detected prepreg laying layer, collecting an infrared phase-locked diagram of the to-be-detected prepreg laying layer, and converting the infrared phase-locked diagram into an infrared phase-locked gray-scale map; carrying out boundary identification on the infrared phase-locked grey-scale map, and then carrying out image segmentation on the infrared phase-locked grey-scale map by taking a grey-scale threshold value as a constraint condition to obtain an infrared phase-locked grey-scale map with divided grey-scale regions; and performing shape recognition on the gray region greater than the gray threshold to obtain a region containing a singular point and / or an edge curve, and further obtaining the defect type of the to-be-detected prepreg paving layer and the total area of the defect region. According to the method, the linearly increased variable-frequency infrared excitation source is adopted to excite the prepreg paving layer, so that defect information of different depths can be excited, infrared signals radiated by near-surface and deeper defects can be detected by an external detector, and the problem that deep defects are difficult to detect by an infrared phase locking method in the prior art is solved.

The present application relates to the field of crane pipe leak testing, and specifically discloses a crane pipe leak testing system for liquid chlorine unloading. The crane pipe includes a fixed pipe and a movable pipe, and a heat insulation structure is arranged on the fixed pipe and the movable pipe; the heat insulation structure is used to insulate the inside of the fixed pipe and the movable pipe; a temperature detection component for obtaining the temperature of the heat insulation structure is arranged at the heat insulation structure; a socket matching the filling port of the tank truck is formed on the fixed pipe; the movable pipe is inserted into the socket so that the fixed pipe and the movable pipe form a closed loop; a gas thermal imaging component is arranged on the fixed pipe, and the gas thermal imaging component forms a gas detection area, and the gas detection area covers the movable pipe. The present application has the effect of ensuring that the liquid chlorine will not be vaporized when unloading the truck, and better protecting the crane pipe, while using the temperature detection component and the gas thermal imaging component to detect the temperature outside the heat insulation structure, and when the crane pipe leaks, it will cause the temperature outside the heat insulation structure to change significantly, thereby using liquid chlorine to directly perform leak testing.

The methods described herein provide improvements in composite infrared absorption imaging. The scanning approach described herein reduces erroneous measurements caused by thermal drift and optical interference that occurs between light scattering from the top surface of the sample and light scattering from the underlying substrate. Embodiments described herein relate to imaging and spectroscopy, and more particularly, to improvements in photo-thermal imaging and spectroscopy systems, and techniques for obtaining spectral information characterizing optical properties and / or materials or chemical compositions of a sample, such as information related to infrared (IR) absorption spectroscopy.

The invention relates to a multi-spectral target detection method and device based on feature fusion and electronic equipment. The method comprises the following steps: respectively extracting visible light features of an RGB image and thermal radiation features of a thermal imaging image; carrying out bimodal feature fusion on the visible light features and the thermal radiation features by adopting a double-cross attention mechanism to obtain a fusion feature map; and inputting the fused feature map into a detection network, and carrying out target classification and positioning to obtain a detection result. By comprehensively utilizing the advantages of each image type, the limitation of another mode is compensated by the strong point of one mode, so that the fuzzy degree of the color image under a severe weather condition or a changing illumination environment is effectively solved, abundant extra information is further provided, and the robustness and accuracy of target detection are enhanced.

The invention relates to the technical field of solar street lamps, and provides a solar street lamp sectional illumination control system with human body induction, which comprises a local module and a cloud service platform, and is characterized in that the local module comprises a sensor group unit, a power supply unit, an illumination unit, a data processing unit and a communication unit; the local module obtains a multi-source data packet through a sensor of the local module, the data processing unit judges the existence of a human body through the multi-source data packet, and the lighting unit is controlled through a judgment result to form a corresponding lighting mode. According to the invention, a graded awakening mechanism of the millimeter wave radar, the PIR sensor and the thermal imaging sensor is adopted, the PIR sensor detects a dynamic heat source in real time, the radar and thermal imaging are awakened to carry out accurate detection, and the radar and the thermal imaging sensor carry out detection alternately and periodically, so that missing detection can be avoided while the power consumption is reduced.

The invention relates to the technical field of high-place operation safety equipment, in particular to an outer wall defect detection method based on infrared thermography, which comprises the following steps: acquiring a construction building drawing of each building, acquiring report records about wall body cracks in an intelligent property platform and forming a plurality of crack type sets and related crack type sets, combining the related crack type sets into a related crack set, determining a crack building drawing of each building in combination with report records, determining a plurality of crack clustering centers of the corresponding building according to the crack building drawing, and determining whether the crack clustering centers are inner side defect points or not according to the crack clustering centers and the target positions; determining an outer wall detection position of the infrared thermal image according to the inner side defect point, and controlling the detection unmanned aerial vehicle to move to the outer wall detection position to obtain a corresponding outer wall infrared thermal image; and determining whether a temperature abnormal area exists or not according to the outer wall infrared thermal image so as to determine wall defects. Through crack clustering and directional detection, the problems of blind detection, low efficiency and insufficient accuracy in outer wall defect detection are solved.

Flow characteristics of a three-dimensional fluid flow are quantified by bubble thermography velocimetry (BTV) in which large numbers of bubbles buoyant in the fluid and having a predetermined size and temperature are introduced into in the fluid flow while long wavelength infrared camera (LWIR) cameras record the positions over time of individual bubbles. In one application the fluid is air and the bubbles are soap bubbles, and the velocity, acceleration and direction of individual bubbles carried by wind through a target area of interest are derived from the position of each bubble at predetermined time intervals for environmental analyses such as weather and climate modeling, urban dispersion studies, building wind load analyses, and the like. BTV defines each bubble's path and velocity vectors in three dimensions that produce richer data than known flow analysis techniques by tracking the bubbles over larger scales at correspondingly higher spatial and velocity resolutions.

The invention discloses a gas leakage detection method. The method comprises the following steps: S10, carrying out denoising and image enhancement on an acquired visible light image and an acquired thermal infrared image; s20, carrying out image feature point matching; s30, fusing the visible light image and the thermal infrared image after registration; and S40, gas leakage detection based on the improved YOLOv8 framework. According to the invention, thermal imaging, multispectral and visible light imaging multi-source fusion technology is adopted, so that the problems of efficient detection and accurate identification of multiple types of gases in a broadband range are solved.

The invention provides a photovoltaic module fault image recognition system based on infrared thermal imaging analysis, and relates to the field of image recognition, comprising: an asynchronous image acquisition module calculates a response delay value based on component spatial distribution, a shielding state and a thermal capacity coefficient, and generates a dynamic scheduling path to control an infrared thermal imager to capture component images of different thermal response processes; the thermal anomaly trend analysis and recognition module constructs a time difference thermal response matrix, recognizes continuous multi-frame candidate temperature rise areas with the temperature rising continuously, and builds a temperature trend prediction model to judge thermal anomaly; the response behavior intervention and disturbance execution module calculates a disturbance influence factor, decides to apply disturbance to a target or an adjacent component and triggers image acquisition; and the routing inspection scheduling feedback updating module dynamically adjusts the priority and frequency of the acquisition path according to the thermal anomaly judgment result and the intervention data.

The invention provides a house external window detection method and device, a terminal and a storage medium, and the method comprises the steps: obtaining an external window thermal image generated by scanning an external window of a current house through thermal imaging equipment, and extracting corresponding temperature distribution information from the external window thermal image; acquiring an outer window image generated by shooting the outer window of the current house by a camera device, and identifying the outer window image to determine outer window structural features and outer window potential safety hazards; constructing an outer window three-dimensional model based on the outer window thermal image and the outer window image; and detecting and evaluating the outer window based on the outer window three-dimensional model, the temperature distribution information, the outer window structure characteristics and the outer window potential safety hazard to obtain a detection and evaluation result reflecting the outer window thermal performance and the outer window safety performance. Through combined detection of thermal imaging and machine vision, comprehensive evaluation of the thermal performance and the safety performance of the outer window is realized, automatic detection of the outer window of the house is realized, and the detection efficiency and the accuracy of a detection result are improved.

A thermal imaging camera (100) is disclosed, comprising a housing (110), a front mount (120), an infrared assembly (140) for detecting infrared radiation, a visual assembly (160) for receiving visual radiation, the infrared assembly (140) and the visual assembly (160) being arranged substantially within the housing (110), and at least one cooling element (180) for cooling at least the infrared assembly (140). It is proposed that the thermal imaging camera (100) has an arranging device (200) which is designed to arrange the visual assembly (160) with respect to the infrared assembly (140).

The invention discloses a PCB welding spot defect positioning detection method and system based on deep learning, and the method comprises the steps: S1, collecting visible light data and thermal imaging data of a PCB, carrying out the preprocessing, and obtaining the preprocessed visible light data and thermal infrared data of the PCB; s2, according to the pre-processed visible light data and thermal infrared data of the PCB, feature extraction and feature alignment are carried out respectively, and a textural feature map and a temperature feature map after alignment are obtained; s3, carrying out feature calibration, and generating a fusion feature map; s4, calculating a positioning feature map and an optimized enhanced feature map, and finally outputting a welding spot defect detection frame set; s5, calculating a sub-pixel feature map, and outputting a defect distribution probability map; and S6, converting the defect distribution probability graph into a binary defect mask, and outputting a final defect detection result in combination with morphological closed operation. The method can solve the problem that a traditional PCB welding spot defect positioning method is insufficient in small defect recognition accuracy.

The invention relates to the technical field of thermal imaging temperature measurement, in particular to electronic thermal imaging temperature measurement equipment which comprises a mounting base body, a rotary adjusting assembly, a thermal imaging temperature measurement assembly and a mounting assembly, the rotary adjusting assembly is mounted on the outer wall of one side of the mounting base body, the thermal imaging temperature measurement assembly is mounted on the outer wall of one side of the mounting base body, and the mounting assembly is mounted on the outer wall of the other side of the mounting base body. Mounting assemblies are mounted on the outer walls of the two sides of the mounting base body. The rotary adjusting assembly comprises a first guide rail, a motor, a second rotating shaft, a first gear, a toothed belt and a rotating rod; the rotary adjusting assembly is adopted, the position of the equipment can be adjusted, manual position adjustment of the equipment is avoided, the manual burden is reduced, and the adjusting effect is improved; by adopting the thermal imaging temperature measuring assembly, the height of the temperature measuring structure can be adjusted, the temperature of surrounding environments with different heights can be measured, more important data can be displayed, and the temperature measuring effect is improved.

The invention discloses a fire early warning cooperative system composed of a thermal imaging double-spectrum intelligent ball, an intelligent analysis platform and a fire extinguishing unmanned aerial vehicle, which is applied to the technical field of fire early warning, and comprises the thermal imaging double-spectrum intelligent ball, an integrated visible light sensor and a thermal imaging sensor, and a double-spectrum fusion monitoring technology is adopted to carry out omnibearing and all-weather monitoring; the intelligent analysis platform simulates a fire spreading path based on a fire spreading prediction model of a fluid dynamics and machine learning algorithm, generates a three-dimensional fire danger thermodynamic diagram, and completes a whole-process response from regional early warning to command center decision making to unmanned aerial vehicle cluster task distribution through a multi-level early warning mechanism; the fire extinguishing unmanned aerial vehicle adopts a multi-model collaborative operation system to meet the fire extinguishing requirements of different fire scenes. According to the invention, in a complex and changeable fire scene, the fire hazard can be quickly and accurately identified, and the efficient disposal of fire extinguishing operation is realized.

The utility model relates to the technical field of fire detection, and discloses a thermal imaging temperature-sensing fire detector, which comprises a heat dissipation mechanism and a detection mechanism, the detection mechanism is fixedly connected to the surface of the heat dissipation mechanism, the detection mechanism comprises a detection unit and a protection unit, the detection unit is connected to the inside of the heat dissipation mechanism, and the protection unit is connected to the inside of the heat dissipation mechanism. The protection unit is fixedly connected to the surface of the heat dissipation mechanism, and the heat dissipation mechanism comprises a mounting base, a mounting block, a first shell, a water outlet, an air inlet cover plate, a mounting frame, a waterproof filter screen and a fan. According to the thermal imaging temperature-sensing fire detector, the device can be effectively prevented from being damaged through waterproof, dustproof, heat dissipation and main body protection modes, the application stability and reliability of the device in severe environments such as humid environments and sand and dust environments are improved, and through the Internet-of-Things connection and alarm modes, the device can be effectively protected from being damaged. And surrounding personnel can be quickly reminded, and timely and accurate information support can be provided for fire rescue, so that the fire situation can be quickly and efficiently faced.