827results about How to "Guaranteed detection accuracy" patented technology

The invention provides a large yellow croaker medium-low density SNP liquid phase chip and application thereof, the chip is prepared from a probe for detecting 15,292 SNP molecular marker combinations of a large yellow croaker whole genome, and the position and base information of each SNP site on the chip are shown in a table 1. The invention further provides a preparation method and application of the chip, such as application in large yellow croaker population genetic diversity analysis, breeding population genotype identification, character correlation analysis or genome selective breeding. The chip designed by the invention comprises a series of core sites obviously related to growth, flow resistance and quality characters, and also comprises a large number of background sites, so that the detection accuracy of the designed chip is guaranteed, and the chip has the obvious advantage of low detection cost, the detection cost is reduced by more than 30%, and the detection efficiency is improved. The scientific and technological innovation of the large yellow croaker dark blue breeding industry can be further accelerated.

The invention discloses a lightweight tunnel crack segmentation identification method based on improved YOLOv8, and the method specifically comprises the following steps: 1, obtaining an image data set of a tunnel crack, and carrying out the preprocessing of the data set; step 2, carrying out expansion and enhancement on the preprocessed data set; step 3, labeling the data set processed in the step 2; 4, the YOLOv8 model is improved; 5, training the model improved in the step 4; and step 6, carrying out training test verification on the model trained in the step 5. According to the invention, the problems of low segmentation efficiency and low crack detection precision of the existing crack segmentation method are solved.

The invention provides a wind turbine generator blade defect determination method and device, and the method comprises the steps: carrying out the real-time monitoring of a target wind turbine generator blade, and obtaining a monitored panoramic image and a multi-channel sound signal; processing the multi-channel sound signal, and determining a beam set composed of multiple beams of beams in multiple space directions; performing spatial registration and fusion on the sound signals of the beam set and the panoramic image to determine a panoramic sound image; by using the relevance between the blade position in the panoramic image and the sound image extreme point, locking the wave beam of the blade head of the target wind turbine generator from the wave beam set, and determining a target wave beam; and according to the target wave beam and an anomaly classifier generated by convolutional neural network training, determining an anomaly monitoring result of the target wind turbine generator blade. According to the scheme, the interference problem and the maintenance cost problem of existing monitoring can be solved, and compared with the prior art, the reliability and the accuracy of a detection result can be further improved.

The invention discloses a machine vision multifunctional intelligent detection device which comprises a mounting frame, two vertical plates are symmetrically and fixedly connected to the upper end of the mounting frame, two transmission rollers are rotatably connected between the two vertical plates, and an annular conveying belt is jointly wound on the two transmission rollers; the detection mechanism comprises a U-shaped frame fixedly connected to the upper ends of the two vertical plates, a visual detection assembly is installed on the inner top of the U-shaped frame, and an annular lamp matched with the visual detection assembly is installed on the inner top of the U-shaped frame; and the driving mechanism is used for driving one of the transmission rollers to rotate, and the driving mechanism comprises a connecting frame installed on the front side of the front side vertical plate. In the using process of the detection device, dust and dust on the surface of a detected plate can be effectively removed, so that the overall detection quality is improved, in addition, the removed dust and dust can be treated in a centralized mode, and the situation of secondary adhesion is avoided.

The invention relates to the technical field of network security, in particular to a method and device for detecting illegal external connection of a terminal and a storage medium, and the method comprises the steps: obtaining all terminal equipment connected with a local area network, distinguishing authorized terminal equipment and unauthorized terminal equipment through authorization detection, and carrying out the risk grading of the authorized terminal equipment; the method comprises the following steps: generating an external connection behavior data set by combining client monitoring data, network layer monitoring data and intranet detection data, extracting key information such as an external connection target, access time, a protocol type and a port number, and dividing into pure intranet connection equipment, normal external connection equipment and unknown external connection equipment according to external connection permissions; inputting the log data of the unknown external connection equipment into the log anomaly detection model based on the Transform architecture, identifying an abnormal event, and calculating a log risk score; and if the log risk score exceeds a preset threshold, determining that the device is an illegal external connection device. According to the method, accurate detection of illegal external connection behaviors is realized, and the safety performance of a monitoring system is remarkably improved.

The invention discloses a multi-level track disease recognition system based on vehicle body vibration data, and particularly relates to the field of blasting auxiliary devices. Comprising the steps that a data acquisition module acquires heterogeneity data of a track environment; the environment adaptive module analyzes the correlation between the vehicle body vibration data and the heterogeneity data, constructs a dual-view similarity matrix, and realizes adaptive classification of the track environment through a spectral clustering method; the label enhancement module generates high-quality pseudo labels and forms a completely labeled sample set; the disease recognition module adopts an attention-guided multi-task cascade CNN architecture, and outputs a multi-level recognition result containing disease positions and severity. By the adoption of the technical scheme, the problem that an existing track state evaluation method cannot evaluate the real state of track diseases in a complex environment is solved, and good performance is achieved in the aspect of geometric and structural disease recognition.

The invention discloses a remote sensing image change area detection method and device based on multi-modal prompt guidance, and the method comprises the steps: constructing a multi-modal remote sensing image change detection data set corresponding to a change detection field, the multi-modal remote sensing image change detection data set comprises remote sensing image data, change label range data and semantic category data of a change area; an initial remote sensing image change detection network based on multi-mode prompt guidance is established, and the initial remote sensing image change detection network is a change detection network established based on a detector-guider architecture; training the initial remote sensing image change detection network through the multi-modal remote sensing image change detection data set, and determining the remote sensing image change detection network based on the obtained model parameters; and inputting the multi-mode prompt into a remote sensing image change detection network, and obtaining a change detection result of the interested class of the remote sensing image.

The invention relates to the technical field of glass bottle defect detection, and particularly discloses glass wine bottle body defect optical detection equipment and detection method.The optical detection equipment comprises a conveying belt, and a clamping mechanism, a lifting rotating mechanism, a detection mechanism and a sorting mechanism are sequentially arranged on the conveying belt from the feeding end to the discharging end. The detection end of the detection mechanism is attached to the bottle body of the glass wine bottle for optical detection, the receiving end of the detection mechanism receives the light in the glass wine bottle, the light directly reaches the receiving end in the bottle after passing through a bottle body material, and a signal path is relatively fixed, so that the interference of ambient light or reflection of surrounding objects on an optical signal can be effectively reduced. The bottle body of the glass bottle has a certain thickness, deviation of light scattering and refraction is easy to occur when light passes through the thickness of the glass in traditional external detection, the error can be reduced by matching of an external detection end and an internal receiving end, fine defects such as bubbles, cracks or tiny scratches are helped to be captured, and the detection stability is improved.

The invention provides a lightweight PCB defect detection method and device based on improved YOLOv8n and a storage medium, and the method comprises the steps: obtaining a PCB surface defect data set, carrying out the preprocessing of the data set, and converting the preprocessed data set into a YOLO format; the YOLOv8n model is improved, and the improved YOLOv8n model is obtained; a YOLO format data set is used for training the improved YOLOv8n model, and a trained YOLOv8n model is obtained; performing channel pruning on the trained YOLOv8n model to obtain a model after channel pruning; and performing defect detection and classification on a to-be-detected image by using the model after channel pruning. According to the invention, the PCB defect detection precision is improved, the model calculation amount is effectively reduced, and the reasoning speed is improved.

The invention provides a submerged-arc welding device for a special-shaped part, and relates to the technical field of submerged-arc welding equipment, the submerged-arc welding device comprises a submerged-arc welding gun part, and a pressing control part is mounted on the submerged-arc welding gun part; the downward pressing control piece is used for adjusting downward pressing force; a welding path rolling part is mounted on the downward pressing control part; the welding path rolling part is used for rolling a welding path; a bottoming fitting piece is mounted on the submerged-arc welding gun part; the bottoming fitting piece is positioned below the pressing control piece; a bottoming detection piece is mounted on the bottoming fitting piece; the bottoming detection piece is used for detecting the bottoming width; the device can be used for detecting the initial backing uniformity of carbon dioxide arc welding, the advancing speed of a welding wire can be conveniently controlled for compensation, the uniformity of a welding path is improved, and the problems that an existing special-shaped part submerged-arc welding device cannot conveniently conduct leveling adjustment work on special-shaped welding seams and cannot automatically test backing parameters of carbon dioxide arc welding are solved.

The invention provides an alkane gas leakage real-time identification and positioning method and system based on deep learning and a medium. The method comprises the following steps: acquiring an infrared image and a visible light image; performing rapid registration based on the imaging area and view field offset correction to obtain a registered visible light image; performing gas target detection according to the infrared image; performing pseudo-color conversion according to the detected gas target and gas concentration; and fusing the pseudo color to the registered visible light image. Gas detected in an infrared view field is subjected to pseudo-color fusion in a visible light view field, and finally plug flow output is performed. According to the invention, the deep learning detection algorithm and the dual-light rapid registration algorithm are combined to solve the alkane gas leakage detection task, and compared with the existing artificial observation alkane gas leakage detection method, the detection error caused by limited artificial observation is reduced, the gas leakage detection speed is greatly accelerated, and the safety and reliability are better.

The invention provides a battery shell air tightness detection device which comprises an air exhaust part, a detection mechanism, a feeding mechanism and a mounting table, the feeding mechanism and the detection mechanism are arranged on the mounting table, the feeding mechanism is used for feeding the detection mechanism, and the detection mechanism is used for mounting a battery shell and performing air tightness detection on the battery shell; the detection mechanism is provided with a circulation channel, and the circulation channel is communicated with the interior of the battery shell to form a sealed pressure measurement space; the air extracting piece is used for extracting air in the pressure measuring space so that negative pressure can be formed in the pressure measuring space. The battery shell is installed on the detection mechanism, meanwhile, the detection mechanism is provided with a channel communicated with the interior of the battery shell and the air exhaust part, the air exhaust part exhausts air in the battery shell so that negative pressure can be formed in the battery shell, and a detector is arranged in the air exhaust part and can detect the size of the negative pressure in the battery shell. Whether the battery shell leaks air can be judged, manual plugging is not needed, the detection precision is high, the operation is simple, and the working efficiency is high.

The invention belongs to the technical field of air filter air permeability detection, and particularly relates to an air filter air permeability detection device which comprises a conveyor used for conveying an air filter to be detected; a first support is arranged on one side of the conveyor, a first hydraulic cylinder is installed on the side face of the first support, an inflator pump is arranged at the end of the output end of the first hydraulic cylinder, the two ends of the inflator pump are ventilated, and a fan is installed in the inflator pump. According to the air filter air permeability detection device, the air receiving cylinder and the inflator pump are arranged, during detection, the conveyor drives the air filter to be detected to a detection position, then the inflator pump and the air receiving cylinder are in butt joint with the air inlet and the air outlet of the air filter respectively, and the fan and the wind resistance detector are matched for detection; after detection is completed, the conveyor conveys the next air filter to a detection position, and circulating automatic detection is achieved; the device is high in detection efficiency, and is suitable for monitoring large-batch air filters.

The invention relates to a duplex guide vane full-automatic positioning, clamping and detecting device which comprises an automatic positioning seat and an electrical circuit quick-plug pressure-maintaining tray, the lower end of the automatic positioning seat is installed on the electrical circuit quick-plug pressure-maintaining tray, and the upper end of the automatic positioning seat is provided with a shape-following cambered surface matched with the duplex guide vane in shape. A plurality of positioning mechanisms, a plurality of positioning detection mechanisms and a plurality of airtight detection points are arranged on the follow-up cambered surface, an airtight sensor is arranged in each airtight detection point, a pressing mechanism is arranged at the upper end of the follow-up cambered surface, a floating supporting mechanism is arranged at the lower end of the follow-up cambered surface, and the floating supporting mechanism is provided with a movable supporting plate. The pressing mechanism is provided with a liftable pressing block, the lower side of the pressing block is an inclined face, and an auxiliary pressing cylinder is arranged on the inclined face. Automatic positioning and detection of the blade are achieved through the positioning mechanism and the positioning detection mechanism on the follow-up cambered surface at the upper end of the automatic positioning seat, the blade positioning and clamping efficiency is improved, overall off-line transfer can be achieved, and automatic production line integration is facilitated.

The invention provides an abnormal street lamp detection system and method based on a YOLOv8 model, the system comprises an image acquisition module, a target detection module and an abnormal street lamp identification module, the image acquisition module is used for acquiring a monitoring image on a road in real time, the target detection module adopts the YOLOv8 model to carry out target detection on image data, and the abnormal street lamp identification module is used for identifying the abnormal street lamp. Identifying a street lamp area in the image, positioning a street lamp in the image and marking the position of the street lamp; and the abnormal street lamp identification module analyzes the detected street lamp state by using image features, performs multiple times of abnormal detection on multiple street lamp images collected in the same time period by adopting a multiple-time identification strategy, and judges a final result through a voting mechanism, thereby identifying the abnormal street lamp. According to the invention, automatic detection and state analysis are carried out on the street lamps by using the YOLOv8 model, so that real-time monitoring and identification of abnormal street lamps are realized, and the intelligent management level of an urban road illumination system is improved.

The invention relates to the technical field of railway line flaw detection, in particular to railway line automatic comprehensive flaw detection equipment which comprises two steel rails and a flaw detection car and further comprises a C-shaped mounting plate, a buffer assembly, a waist detection mechanism and a bottom detection mechanism. A first fixing rod is transversely arranged in the C-shaped mounting plate, fixing sleeves are slidably connected to the two ends of the first fixing rod correspondingly, and balance assemblies are arranged at the ends, away from each other, of the two fixing sleeves correspondingly. The device has the advantages that different detection assemblies are arranged for different detection areas, so that steel rail detection of different parts is more efficient and accurate, it is ensured that different types of defects can be detected, the detection period is shortened, the balance assembly and the buffer assembly cooperate with each other to enable the head detection assembly to keep a dynamic balance state, and the detection efficiency is improved. The probe is effectively protected from vibration damage, and the adaptability of the probe to the change of the steel rail and the detection stability of the probe are improved.

The utility model belongs to the technical field of ball valves, and particularly relates to a ball body of a ball valve, which comprises a ball body, a flow channel hole for fluid to pass through is arranged in the center of the ball body, one end of the flow channel hole is an inlet end, the other end of the flow channel hole is an outlet end, and a plurality of spiral rifling grooves are arranged on the inner wall of the flow channel hole in an annular array mode. A plurality of groups of defoaming channels are formed in the spiral rifling groove, one end, close to the direction of the inlet end, of each defoaming channel is an inlet, the other end of each defoaming channel is an outlet, the hole diameter of each outlet is smaller than that of each inlet, and the hole diameters of the inlets and the outlets in the groups of defoaming channels from the inlet end to the outlet end tend to be gradually reduced. The shape and the layout of the spiral rifling groove can improve the flowing stability of the fluid, and the defoaming channel shrinks and crushes large bubbles in the fluid step by step to enable the large bubbles to be dissolved in the liquid, so that the detection precision of the metering equipment on the fluid flow is improved, and the quality of subsequent chemical preparations or liquid blending is guaranteed.

The invention discloses a coal gangue grading and sorting method and system based on LIBS (laser-induced breakdown spectroscopy) and image analysis, and relates to the technical field of coal gangue sorting, the method comprises the following steps: acquiring spectral data of each sample in a mixed coal flow comprising coal briquettes and coal gangue by using an LIBS technology; comparing the obtained spectral data with a pre-established coal briquette spectral database, preliminarily identifying coal briquettes and coal gangue according to a comparison result, and completing preliminary sorting; obtaining a visible light image of the separated mixed coal flow, preprocessing the image, inputting the preprocessed image into a coal gangue detection model based on an improved Mask RCNN, identifying and segmenting all coal gangues in the image, and obtaining the equivalent particle size of each coal gangue; and according to a preset particle size range, the coal gangue is divided into different grades, and the coal gangue in the mixed coal flow is classified and sorted. According to the invention, accurate identification of the coal gangue and grading sorting of the coal gangue can be realized.

The invention discloses an Internet of Vehicles anomaly detection lightweight method based on deep learning and knowledge distillation, and the method comprises the steps: carrying out the preprocessing of a time sequence message of each vehicle in a data set through employing a sliding window strategy by employing a public Internet of Vehicles data set VeReMi Extension, obtaining a time window, and dividing the time window into a training set and a test set; constructing a teacher network, and performing reconstruction-based unsupervised learning training by using a large number of normal time windows in a training set; constructing a lightweight student network, and guiding the student network to realize knowledge distillation on the normal time window in the training set by using the trained teacher network; and deploying the trained lightweight student network and the tested preset threshold value to a roadside unit of the Internet of Vehicles, thereby realizing rapid Internet of Vehicles anomaly detection. According to the invention, the knowledge learned by the teacher network with high precision and large capacity is migrated to the lightweight student network, and rapid vehicle networking anomaly detection is realized while the detection precision is ensured.

The invention relates to the technical field of ecological environment monitoring, in particular to a multi-rotor unmanned aerial vehicle for cooperatively monitoring greenhouse gas and atmospheric pollutants, which comprises a multi-rotor platform, a three-dimensional ultrasonic anemograph, a comprehensive analyzer, a gas inlet pipeline, a support rod, a main GNSS antenna, an auxiliary GNSS antenna, a temperature and humidity sensor and a damping part, the comprehensive analyzer is arranged below the multi-rotor platform; one end of the damping piece is connected with the multi-rotor platform, and the other end is connected with the comprehensive analyzer; the supporting rod is fixed on the center line of the top end of the comprehensive analyzer, and the supporting rod penetrates out of a gap between the multi-rotor platform and the comprehensive analyzer; the three-dimensional ultrasonic anemograph and the main GNSS antenna are correspondingly arranged on the first end head and the first end rod body of the supporting rod; the auxiliary GNSS antenna and the temperature and humidity sensor are correspondingly arranged on the second end head and the second end rod body of the supporting rod. The air inlet pipeline is fixed below the supporting rod; one end of the air inlet pipeline is arranged below the three-dimensional ultrasonic anemograph, and the other end of the air inlet pipeline is communicated with the comprehensive analyzer.

The invention discloses a lightweight water ship target detection method in a complex navigation environment, and the method comprises the following steps: obtaining a picture of a to-be-detected water ship, and forming a data set; dividing the data set into a training set and a test set; based on the improved YOLOv8 network, an enhanced and lightweight ship detection model is constructed; based on the training set data, training the enhanced and lightweight ship detection model to obtain a trained enhanced and lightweight ship detection model; according to the method, data of a test set is input into a trained enhanced and lightweight ship detection model, detection of a water ship target in a complex navigation environment is realized, a GELAN module is combined with an original PANet network, a more efficient and lightweight neck network is designed, and more efficient feature fusion is realized. In addition, an MPDIOU loss function is used to optimize a model gradient gain distribution strategy, so that the network can be converged more quickly.

The invention provides a detection method and a related device, and the method comprises the steps: obtaining first data; then, according to the first data, determining items to be detected, and / or, according to the first data, determining a detection sequence of each item to be detected; then, acquiring at least one physiological parameter corresponding to each item to be detected according to the detection sequence to determine target physiological data; and finally, determining a detection result according to the target physiological data. The disease detection process can be optimized, the detection time is greatly shortened while the detection accuracy is guaranteed, and the user experience is improved.

The invention discloses a high-precision alternating-current voltage direct-current component detection circuit which comprises a capacitance-resistance voltage division circuit, a differential amplification circuit, a second-order RC filter circuit, a voltage following and lifting circuit and an AD voltage clamping circuit which are arranged in sequence. The invention aims to improve the detection precision of the direct-current voltage in the alternating-current output voltage of the energy storage converter and feed back the detected accurate direct-current voltage amplitude to the CPU to carry out closed-loop control on the system output voltage so as to achieve the purpose of reducing the direct-current component in the alternating-current output voltage of the energy storage converter.

The utility model provides a photovoltaic glass appearance defect detection workbench, and belongs to the technical field of glass appearance detection. The photovoltaic glass appearance defect detection workbench comprises a clamping assembly and a light supplementing assembly, the clamping assembly comprises a supporting plate, a supporting frame, an electric push rod, a first motor, an adjusting part and a clamp, the light supplementing assembly comprises a driving part, a mounting plate, a detection light source and a blower, the driving part is arranged on one side of the supporting frame, and the mounting plate is arranged on the other side of the supporting frame. By arranging the supporting plate, the supporting frame, the electric push rod, the first motor, the adjusting pieces, the clamps, the two adjusting pieces capable of adjusting the distance and the two clamps capable of adjusting the distance, photovoltaic glass of various sizes can be clamped and fixed, by arranging the driving piece, the mounting plate, the detection light source and the air blower, the movable detection light source can conduct light supplementing detection on the photovoltaic glass at all positions, and the detection accuracy is improved. And the synchronously moving blower can blow away dust of the windshield washer fluid, so that the dust is prevented from influencing detection judgment.

The invention discloses an energy-saving uniform-temperature open type sintering furnace applied to production of a DCB copper-clad ceramic substrate, and belongs to the technical field of sintering furnaces, the energy-saving uniform-temperature open type sintering furnace comprises a sintering furnace body, a feeding table and a discharging table are arranged on the two sides of the sintering furnace body respectively, and a sintering hearth is formed in the sintering furnace body. By arranging the holding port, the heat dissipation port and the four-channel heat exchanger, a copper-clad ceramic substrate just entering the sintering furnace body can be preheated, so that energy consumption caused by preheating is reduced, by arranging the heating pipe, high-temperature gas can be further used for driving the waste heat driving mechanism, and by arranging the preheating detection mechanism, the heat dissipation efficiency of the sintering furnace is improved. By arranging the waste heat driving mechanism, air pressure can be provided for the dust removal port, so that the dust removal port has a dust removal function on the copper-clad ceramic substrate, the copper-clad ceramic substrate can be positioned by the sliding plate and the connecting clamping plate on the reciprocating screw, and the copper-clad ceramic substrate is kept in the middle on the material conveying belt, so that the heating efficiency is improved.

The invention relates to the technical field of flow measurement, in particular to a particle energy type high-frequency on-line three-phase flow metering method, which comprises the following steps: acquiring three-phase flow information, microwave signal intensity curves and environmental information parameters of a plurality of fluid samples, determining interfered categories, fitting sample curves, determining interfered characteristic fluid samples, and calculating the particle energy type high-frequency on-line three-phase flow according to the interfered characteristic fluid samples. The method comprises the following steps: acquiring a microwave signal intensity curve of a to-be-measured fluid by utilizing microwave irradiation, preliminarily determining and judging whether three-phase flow information of the to-be-measured fluid is accurate or not, determining whether to calibrate and compensate the microwave irradiation or not, selecting output information based on a comparison condition of the three-phase flow information, or giving out an abnormal early warning to a metering environment, and further, outputting the output information. According to the invention, abnormal risk identification is rapidly carried out according to the specific environment condition of the mixed fluid, the measurement mode is adaptively optimized, and the detection precision of three-phase flow information is improved.

The invention relates to the technical field of data processing, in particular to a covariance inverse matrix recursive updating method for on-satellite hyperspectral anomaly detection, which comprises the following steps: acquiring a hyperspectral data stream; carrying out adaptive dimension reduction processing on the data stream and loading an initial background model; extracting local background statistics by adopting a sliding window mechanism, generating a background spectrum dictionary by utilizing online dictionary learning, calculating a reconstruction error between a current pixel and the dictionary and a Mahalanobis distance between the current pixel and a background model, and fusing to generate an abnormal score; performing abnormal confidence coefficient evaluation by combining the spatial context information and the spectral angle matching degree, and updating a spectral mean vector by using an exponential weighted moving average algorithm based on non-abnormal pixel data; and the updated model is injected into the next round of processing to form a recursive chain. According to the method, the high calculation complexity of direct inversion of a covariance matrix is avoided, real-time anomaly detection on a satellite is realized, the downloading amount of original data is remarkably reduced, and the congestion of a satellite-ground communication link is relieved.

The invention relates to the technical field of medical image intelligent analysis systems, in particular to a mammary gland molybdenum target image intelligent analysis system and method based on multi-scale deep learning, and the system comprises an image obtaining module which is used for obtaining a mammary gland molybdenum target image; the calcification detection module is connected with the image acquisition module, and is used for carrying out intelligent detection on calcification points in the mammary gland molybdenum target image based on the multi-dimensional feature matrix and the adaptive feature filter, and generating confidence scores and dynamic segmentation thresholds of the calcification points; the calcification segmentation module is connected with the calcification detection module and is used for segmenting the calcification point region based on the dynamic segmentation threshold value of the calcification detection module; the calcification quantification module is connected with the calcification segmentation module and is used for extracting quantitative features of the calcification points based on the segmentation result of the calcification segmentation module, and by introducing feature extraction and feature fusion of different scales, the sensitivity to tiny calcification points is remarkably improved; and meanwhile, the problem that a traditional single-scale model cannot accurately identify calcification characteristics of different scales is solved.

The invention discloses a damping mechanism, a sonar with the damping mechanism and a damping method of the sonar, and relates to the technical field of underwater detection.The sonar comprises a sonar body, a watertight compartment, a driving mechanism and an overturning cover, the driving mechanism is arranged at the top of the watertight compartment, and the overturning cover is arranged on the driving mechanism; the driven end of the turnover cover is rotatably installed in the watertight compartment and is drivingly connected to the driving mechanism, the sonar body is assembled at the assembling end of the turnover cover, and when the turnover cover is driven to turn over in the direction of covering the bottom opening of the watertight compartment, the turnover cover enables the sonar body to be contained in a compartment cavity of the watertight compartment, and the sonar body is driven by the turnover cover to be driven to rotate in the direction of covering the bottom opening of the watertight compartment. When the turnover cover is driven to turn over in the direction of opening the bottom opening of the watertight compartment, the turnover cover enables the sonar body to be suspended below the watertight compartment, in this way, the sonar can be in a recovery state and a detection state, when the sonar is in the recovery state, the sonar body is hidden and protected, and when the sonar is in the detection state, the sonar body is protected. When the sonar is in a detection state, the sonar is used for underwater detection.

The utility model relates to a carrier and PCR (Polymerase Chain Reaction) detection system, the carrier comprises a cavity wall, the cavity wall comprises a first wall, a second wall and a side wall, the first wall and the second wall are oppositely arranged, the side wall is arranged between the first wall and the second wall, the side wall is annular, a reaction cavity for accommodating reaction liquid is defined by the cavity wall, and at least part of the cavity wall can be heated by an electromagnetic field. The PCR detection system comprises an electromagnetic heating mechanism and a cooling mechanism, the electromagnetic heating mechanism is used for heating the carrier, and the cooling mechanism is used for cooling the carrier.