479results about How to "Guaranteed detection effect" patented technology

The invention discloses a capture probe group for detecting pathogenic microorganisms and application of the capture probe group. The nucleic acid sequence of the capture probe group comprises a sequence as shown in SEQ ID NO. 1 to SEQ ID NO. 101. The capture probe group can cover and detect 119 types of bacteria, 32 types of fungi, 41 types of DNA viruses and 71 types of RNA viruses and covers common infectious disease pathogens, the kit containing the capture probe can cover and detect 88 types of drug-resistant and virulence genes, the detection performance of pathogenic microorganisms is greatly improved, and the sensitivity and specificity are improved.

The invention discloses a vehicle-mounted sign retroreflection brightness coefficient detection method and system, and the method comprises the steps: obtaining the image data and point cloud data of a to-be-detected road region shot by an observation module at the top of a detection vehicle, and carrying out the detection of the to-be-detected road region according to a pre-trained first deep learning detection model; obtaining a relative distance between the to-be-detected signboard in the to-be-detected road area and the detection vehicle; when the relative distance meets a preset shooting distance, triggering the detection module to continuously shoot the signboard to be detected with light source illumination and without light source illumination, and respectively inputting two continuous frame images obtained by continuous shooting into a deep learning classification recognition model to obtain a target signboard image, the difference between the average gray values of each target signboard image is calculated to obtain an interference-free gray value, the retroreflection brightness coefficient of the signboard to be detected is obtained according to a preset retroreflection conversion formula, the continuous frames of images are used, interference brought by the external environment during measurement of the retroreflection brightness coefficient of the signboard is eliminated, and the measurement accuracy is improved.

The invention discloses a power system false data injection attack detection method and system. Measurement data of a power distribution system are acquired; constructing an attack sequence, and injecting the constructed attack sequence into the obtained measurement data to simulate a false data attack; constructing a false data injection attack detection model based on the expansion causal convolution and the recurrent neural network to analyze the state change of the power distribution system; and judging an attack behavior according to the analyzed state of the power distribution system. According to the method, time sequence data can be processed, the long-term dependency relationship and the complex mode can be captured, and the response capability to the complex attack mode can be improved, so that the false data injection attack can be effectively detected and prevented, and the safety and reliability of the power distribution system can be maintained.

The invention discloses a vulnerability and threat scanning method and system based on artificial intelligence, and particularly relates to the technical field of artificial intelligence. The method comprises the following steps of: extracting known threat and potential zero-day attack data from a real environment to train an AI model, performing threat detection test on the AI model in diversified scenes by combining with generated simulation attack samples of high-complexity low-anomaly and low-complexity high-anomaly, analyzing the response capability of the AI model to high-complexity attacks, and determining the risk of high-complexity attacks by recording and counting false alarm and missing alarm conditions. Comprehensively evaluating the accuracy of identifying unknown novel threat features by the model; when it is detected that recognition of the model on unknown novel threat features is not accurate, by predicting the abnormal degree in a fixed time period, generating an early warning signal and optimizing the model, the detection capacity of the AI model on complex camouflage attacks can be remarkably improved, a blind area of the model in an actual environment can be found and repaired in time, and the recognition accuracy of the model on the unknown novel threat features is improved. And breakthrough of zero-day vulnerabilities and advanced persistent threats to the system is effectively prevented.

The invention discloses a pedestrian and vehicle detection method based on an improved YOLOv10n. The pedestrian and vehicle detection method comprises the following steps: on the basis of an original model YOLOv10n, replacing a first layer and a third layer in Backbone and a seventeenth layer Conv in Neck with a space-to-depth convolution SPDConv; a focused spatial pyramid pooling module ASPPF is constructed, and semantic fusion of cross-level features is optimized; designing a global grouping coordinate attention module GGCA, fusing the GGCA into a Neck layer, generating attention maps by using global information of the feature maps in the spatial dimension, and weighting the input feature maps through the attention maps to enhance the feature expression ability; a WIou loss function is introduced, the bounding box loss of the network is optimized, and the convergence speed and regression precision of the model are improved. And training a model by using a data set which is made by autonomous labeling to obtain a training result, and completing a pedestrian and vehicle detection task by using an optimal training weight. According to the method, a good detection effect is achieved with a small number of model parameters, and the pedestrian and vehicle recognition accuracy is well improved.

The invention discloses an unmanned aerial vehicle mechanical arm control system and method based on machine vision, and relates to the technical field of mechanical arm control. The system comprises an image acquisition module, a feature point extraction and matching module, a pose analysis module, a control parameter calculation and adjustment module and a motion control and execution module. The image data acquisition module acquires target area image data; the feature point extraction and matching module is used for extracting feature points of a target object from a reference image, constructing a feature set, extracting and matching the feature points of real-time image data, and screening effective matching points; the pose estimation module analyzes the actual pose of the unmanned aerial vehicle mechanical arm according to the matching points and calculates the actual pose and the expected pose through an optimization algorithm; the control parameter calculation and adjustment module is used for calculating the deviation between the expected pose and the actual pose, setting a control gain and updating a motion control parameter; and the motion control and execution module updates the motion track of the unmanned aerial vehicle mechanical arm in real time based on the adjusted control parameters.

The invention provides a hydropower station floating object detection method based on global relation modeling, and aims to improve the detection precision and efficiency of hydropower station water surface floating objects, and the method comprises the steps: collecting hydropower station water surface scene data through a multi-view device; extracting shallow multi-scale features through a lightweight convolutional network; modeling a global relationship between the floaters through a multi-head self-attention mechanism; carrying out the classification and positioning of floating objects through a target detection head; network training is performed through a multi-task loss function to optimize the model; and reasoning by using the trained floating object detection network, inputting a to-be-detected water surface image into the network, outputting the category and the position of the floating object, and finally generating a detection result. According to the method, the global relation modeling method is introduced, the detection precision of the hydropower station water surface floating objects is effectively improved, detection errors caused by insufficient local features in a traditional method are reduced, and the method has low calculation complexity and is suitable for real-time detection in practical application.

The invention discloses an ice surface defect detection and quantification system and method based on deep learning. The system comprises a data acquisition module, a data enhancement module, a neural network training module, a binocular stereoscopic vision-based detection module and an information fusion and analysis module. The method comprises the following steps: firstly, acquiring image data of ice surface defects by using various camera devices through a data acquisition module, and covering different illumination conditions and defect types; and then, the data enhancement module performs rotation, overturning, contrast adjustment and noise addition on the acquired data so as to improve data diversity and generalization ability of the neural network. And then, a multi-scale feature extraction module (MSTG-Net) and a detail enhancement convolution module (MSDSC) are introduced into a neural network training module, and the segmentation feature extraction of the ice surface defect is realized by training the improved YOLOv8 neural network. The detection module based on binocular stereo vision acquires an ice surface image through a binocular camera, and obtains defect mark information and a depth map in combination with the trained neural network model. And finally, further determining the three-dimensional position, the surface area size and the spatial morphological characteristics of the defect by combining the information fusion and analysis module with a binocular stereoscopic vision distance measurement result. According to the method, high-precision identification and positioning of ice surface defects are realized, reliable data support is provided for subsequent ice surface repair planning, and the method has the advantages of high precision, strong robustness, wide applicability and the like.

The invention belongs to the technical field of underwater vehicle testing, and particularly relates to a testing device for simulating an actual ocean service environment and a using method of the testing device. The experimental device comprises a box body, a bottom plate detachably installed on the box body, a plurality of simulation areas arranged on the bottom plate or the side wall of the box body, a wave making mechanism and a water depth pressure simulation mechanism installed on the side wall of the box body, a sea wind simulation mechanism installed on the box body, a first illumination module and a plurality of first visual capture modules. The wave making mechanism, the water depth pressure simulation mechanism and the sea wind simulation mechanism are arranged to form an ocean service environment, a plurality of simulation areas are arranged on the bottom plate or the side wall of the box body, and the height of the multiple three-dimensional lifting columns relative to the polygonal sinking surface is adjusted through the corresponding telescopic driving mechanisms to simulate the ocean seabed landform. Different working environments are provided for a test object, and the problems that in the prior art, due to the fact that the difference between the simulated marine environment and the actual environment is large, appearance optimization design cannot be carried out, and service performance cannot be comprehensively evaluated are solved.

The invention relates to a drivable area detection method and device and a storage medium, and the method comprises the steps: building an initial grid map at an initial moment, and enabling the grid map to comprise a drivable area and a non-drivable area; and at each moment after the initial moment, dynamically updating the grid map by using the vehicle environment image or using the obstacle distance information to obtain the grid map at the current moment. If the grid map is dynamically updated by using the vehicle environment image at the previous moment, dynamically updating the grid map at the previous moment by using the obstacle distance information at the current moment; and if the grid map is dynamically updated by using the obstacle distance information at the previous moment, dynamically updating the grid map at the previous moment by using the vehicle environment image at the current moment. According to the invention, the method can effectively reduce the shielding problem, also has the advantage of low cost, can obtain the accurate boundary information of the driving region, and can carry out the modeling of a dynamic obstacle.

The invention discloses a multi-level multi-attention target detection method based on a radar and a camera, and the method comprises the steps: firstly obtaining a radar point cloud and an image of a public data set, and carrying out the preprocessing; secondly, constructing an SC-DLA34 feature extraction network based on a DLA34 network to obtain an associated point cloud, and performing multi-level fusion on the associated point cloud and the image to obtain a preliminary fusion feature; and then splicing the associated point cloud features and the preliminary fusion features at a channel level, and inputting the spliced features into a PEA pyramid attention model to obtain deep fusion features. Secondly, the deep fusion features are sent to a 3D detection head to be decoded, the depth, speed, rotation and attribute information of a detection target is estimated, and a target 3D detection frame is generated; according to the invention, cross-modal fusion of radar information and image information is realized, the expression capability of point cloud is improved, and the detection capability of the model on small targets is ensured.

The invention discloses a grinding workpiece edge shape detection method and system based on machine learning, and the method comprises the steps: obtaining an original image of a grinding workpiece, and carrying out the preliminary image preprocessing; denoising the image and enhancing edge information by combining image filtering and mathematical morphology image processing; based on a second-order differential operator, edge detection is carried out in combination with the morphological gradient, and a workpiece edge contour is obtained; performing feature extraction on the image by using a convolutional neural network, automatically learning workpiece edge features, and constructing and training an edge detection model; acquiring a workpiece image in real time, inputting the workpiece image into the edge detection model for detection, and dynamically correcting the model; and S6, outputting a final edge detection result. According to the invention, by combining image preprocessing, composite image filtering, second-order differential operator and morphological gradient edge detection and feature extraction and model construction of the convolutional neural network, high-precision and robust detection of the edge of the grinding workpiece is realized.

The invention relates to the technical field of computer vision and deep learning, and relates to a deep forgery detection method based on mask supervision. According to the technical scheme, an image is input to a preprocessing module for preprocessing; the processed image is respectively input into a mask supervision module for extracting spatial domain features and a frequency domain feature extraction module for extracting frequency domain features; and the spatial domain features and the frequency domain features are connected and input to a classification module to obtain a final prediction result. The method has the beneficial effects that the attention of the model to the irrelevant region is supervised and corrected through the mask, and a pixel-level label is used for guiding the model to face the synthesis region of the face, so that more accurate extraction of spatial features is ensured; the depth forgery detection method based on mask supervision has remarkable advantages in the aspects of detection accuracy, robustness, generalization ability, calculation efficiency, interpretability, overall performance optimization and the like, and a new solution is provided for the field of depth forgery detection.

The invention discloses a defect detection sensor and a defect detection method based on a quantum technology, and relates to the technical field of defect detection.The defect detection sensor comprises a central controller, an orthogonal differential detection unit, a quantum magnetometer and a magnetic shielding cylinder; the orthogonal differential detection unit is used for collecting a first magnetic field change signal on the surface of a preset area of the preset part, transmitting the first magnetic field change signal to the quantum magnetometer and applying the first magnetic field change signal to alkali metal atoms of the quantum magnetometer; the quantum magnetometer is used for obtaining a second magnetic field change signal and sending the second magnetic field change signal to the central controller; and the central controller is used for analyzing the second magnetic field change signal and determining whether the preset area of the preset part has defects or not. According to the invention, the sensitivity and accuracy of defect detection can be improved.

The invention discloses a self-adaptive abnormal behavior detection response method for an industrial control network, and particularly relates to the technical field of industrial control network security, and the method comprises the following steps: collecting state change information of the industrial control network to obtain a first information set, and generating a state change monitoring index based on the first information set; acquiring network attack information of the industrial control network to obtain an information set II, and generating a network attack threat index based on the information set II; judging and dynamically selecting different detection algorithms based on the state change monitoring index and the network attack threat index to realize real-time detection and response of abnormal behaviors; when the type of the detection algorithm is selected, the application set of the detection algorithm of the type is adaptively optimized and applied at the same time; according to the method, the detection resources can be reasonably allocated under different network states and security threat conditions, and the industrial control network is more flexible to deal with sudden security threats by dynamically adjusting the detection strategy, so that the security and the operation continuity of the industrial control network can be ensured.

The invention discloses a turnout three-dimensional visualization method, device and equipment and a medium, and the method comprises the steps: collecting and obtaining video data of a plurality of visual angles of turnout parts, and carrying out the preprocessing, and obtaining a plurality of pieces of image data; calculating the multiple pieces of image data to obtain initialized three-dimensional point cloud data; training the three-dimensional point cloud data by using a three-dimensional Gaussian algorithm to generate a three-dimensional Gaussian model; and carrying out real-time visual rendering on the three-dimensional Gaussian model, and checking rendering images of the turnout parts at multiple different visual angles. According to the invention, the shot turnout part is represented by using the three-dimensional Gaussian model, and the images of the turnout part at multiple angles are rendered in real time, so that a user can detect the turnout part without on-site exploration; the detection efficiency is improved, manpower and material resources are saved, and the detection cost is reduced; and the detection precision and the detection timeliness of the turnout parts are further ensured, and the method is more suitable for application requirements in dynamic scenes.

The invention discloses a GIS switch foreign matter detection method based on multispectral image fusion and related equipment, and belongs to the technical field of electrical equipment foreign matter detection, and the method comprises the steps: obtaining a multispectral image of a to-be-detected GIS switch; and inputting the multispectral image into a pre-trained multispectral fusion model to obtain a multispectral feature map, and inputting the multispectral feature map into a pre-trained multispectral image foreign matter detection model to obtain a foreign matter detection result of the to-be-detected GIS switch. The development and automatic identification of the tiny foreign matter material under different light wavelengths are realized by researching the development technology of the tiny foreign matter material under different light wavelengths and combining multispectral fusion and a multispectral image foreign matter detection algorithm, and the accuracy and robustness of detection are improved.

The utility model discloses a biscuit packaging quality detection device which comprises a detection table and further comprises a detection assembly arranged on the detection table and used for detecting the sealing state of biscuit packaging, and the detection assembly is provided with a control assembly for pressure control in the detection process. According to the biscuit package quality detection device, in the process of detecting the sealing quality of the biscuit package, the biscuit package to be detected is extruded and subjected to pressure maintaining treatment through the detection assembly and the control assembly, whether the sealing quality of the biscuit package is qualified or not is judged, and in the pressurizing process, the biscuit package is subjected to pressure maintaining treatment; when the internal pressure of the detection cylinder rises to a certain degree, the internal pressure does not continue to rise, so that pressure control in the detection process is realized, and the pressurization pressure in the detection process is ensured and the detection effect is ensured while the to-be-detected biscuit package is prevented from being extruded and damaged due to overlarge pressurization pressure.

The present invention discloses a disc motor rotor dynamic balancing optimization device, which relates to the technical field of motor rotor dynamic balancing test. It comprises a base plate and a rotor body, wherein a test assembly is arranged on the base plate, and a test belt is arranged on the test assembly; a bracket plate is fixedly mounted on the base plate, and an optimization assembly is arranged inside the bracket plate, and the data of the rotor body test is made more accurate by the optimization assembly; a detection frame is fixedly mounted on the bracket plate, and a detection instrument is arranged on the detection frame, and the test assembly comprises two side plates fixedly mounted on the base plate, and a drive motor is fixedly mounted on the side plates. The invention utilizes the coordinated use of the test assembly and the optimization assembly, and by improving the stability and adjustability during the test process, it can optimize the test effect when the disc motor rotor is subjected to a dynamic balancing test, and effectively improve the efficiency and accuracy of the disc motor rotor dynamic balancing test.

The invention discloses an LED screen detection device and method, and belongs to the field of LED screen detection. An LED screen detection device comprises a transportation frame body, a mounting frame is fixedly connected to the transportation frame body, a conveying belt of the transportation frame body penetrates through the mounting frame, a detection box is arranged in the mounting frame, and a refrigeration and heating circulator is fixedly connected to the bottom of the mounting frame. The detection assembly is arranged in the detection box; according to the device, the detection disc reciprocates above the LED screen along with the detection plate, the surface of the LED screen is cleaned firstly, the detection effect is ensured, and the detection precision is improved; the LED screen wear resistance detection device has the advantages that the LED screen wear resistance detection device can detect the wear resistance of an LED screen, simulates the color and appearance conditions of the LED screen in repeated touch sliding, realizes multifunctional detection, and improves the detection efficiency by arranging a driving rack and a driven gear, so that the detection disc can drive a silica gel brush to move and rotate at the same time, and dust and impurities can be better removed. The LED screen wear resistance detection device can detect the wear resistance of the LED screen, and simulates the color and appearance conditions of the LED screen in repeated touch sliding.

The invention discloses a lossless visual detection device for a liquid crystal display screen, and relates to the technical field of liquid crystal display screen detection. According to the lossless visual detection device for the liquid crystal display screen, through the arrangement of limiting columns and a rectangular cleaning scraper, when a conveyed liquid crystal display screen body is in contact with the rectangular cleaning scraper, the rectangular cleaning scraper and a positioning ring can be driven to rotate along the corresponding limiting columns, and at the moment, an air inlet channel is communicated; according to the liquid crystal display screen body detection device, the outer surface of a liquid crystal display screen body can be cleaned, impurities can be extracted and collected, and compared with a traditional detection mode, on one hand, the detection convenience and detection efficiency of the device can be improved, and the device can be suitable for detecting liquid crystal display screen bodies of different sizes; the application range of the equipment is expanded, and meanwhile, the detection effect on the liquid crystal display screen body can be ensured.

The invention discloses an automatic detection method and system for small-size pests, and relates to the technical field of automatic detection of pests, environmental condition data in a pest monitoring area are collected, and pest data are collected by a sensor network; identifying insect types and position information in the insect pest image by using the trained insect pest target detection model; the insect types are verified according to the insect pest characteristics of the small-size insects, the insect intensity in the area is calculated according to the position information of the target insect pests and the time nodes, when the insect intensity exceeds the expectation, the trained insect pest outbreak prediction model is used for predicting the number of the insect pests, and if the prediction result exceeds the expectation, an alarm value is constructed according to the alarm data. And when the warning value exceeds a warning threshold value, outputting an insect pest emergency treatment scheme by the small-size insect pest treatment knowledge graph. On the basis of mutual cooperation and verification of a plurality of different characteristics, the detection reliability is improved when small-size insects are detected and identified.

The utility model relates to an ultrasonic crack detection device for a mining conveyor belt. The ultrasonic crack detection device comprises a supporting frame, a transmitting detection wheel assembly and a receiving detection wheel assembly, wherein the transmitting detection wheel assembly and the receiving detection wheel assembly are mounted on the supporting frame; the receiving detection wheel assemblies are arranged on the two sides of the transmitting detection wheel assembly at intervals in the length direction of the bearing frame. The transmitting detection wheel assembly is provided with a first roller used for making contact with a mining conveyor belt and an ultrasonic generator used for transmitting ultrasonic waves to the mining conveyor belt. The receiving detection wheel assembly is provided with a second roller used for making contact with a mining conveyor belt and an ultrasonic receiver used for receiving ultrasonic waves transmitted by the mining conveyor belt. According to the mining conveyor belt, small damage on the mining conveyor belt can be found in time, detection early warning can be sent out in time, so that the belt with safety risks can be repaired or replaced in time, economic losses caused by equipment faults can be greatly reduced, and the safety risks of personnel are effectively reduced.

The invention belongs to the technical field of gas pipeline detection, and discloses gas pipeline compressive strength detection equipment which comprises a main body box and a detection box fixedly arranged on the inner wall of the bottom of the main body box, and a strength detection assembly and a hydraulic control assembly are arranged in the main body box. The strength detection assembly comprises a fixing frame fixedly arranged on the bottom wall of the main body box, a two-way screw with the middle rotationally arranged in the fixing frame and a guide rod with the middle horizontally and fixedly arranged in the fixing frame, and the change of air pressure in an air pressure piston cylinder is controlled by activating a servo motor in the forward direction and the reverse direction; the pressure-resistant and tear-resistant performance test is carried out on the gas pipeline by utilizing the force generated by the gas pressure gradient, in the process, the internal volume of the hydraulic piston cylinder is correspondingly changed due to the compression and release of the gas pressure, and the compressive strength and tear-resistant strength indexes of the gas pipeline are calculated by accurately monitoring the increase and decrease of the volume of the liquid in the observation cylinder.

The invention discloses a building exterior wall water seepage detection device, and belongs to the technical field of building wall detection.The building exterior wall water seepage detection device comprises a to-be-detected wall, support mechanisms are placed on the two sides of the front end face of the to-be-detected wall, and an auxiliary supporting mechanism is installed between the two support mechanisms; a movable adjusting mechanism is installed on the auxiliary supporting mechanism, a detection mechanism is installed between the auxiliary supporting mechanism and the movable adjusting mechanism, and a detection driving mechanism is installed on the two support mechanisms. By designing the support mechanism, the auxiliary supporting mechanism, the movement adjusting mechanism and the detection driving mechanism, the height of the splicing device is determined according to the range during use, detection can be conducted according to walls of different areas, the application range of the device is widened, and the whole device can complete rapid splicing and is extremely convenient to transport after being disassembled.

The invention relates to the technical field of copper pipe drawing, and discloses a copper pipe drawing device which comprises a workbench, a first motor is fixedly installed outside the workbench, the output end of the first motor is connected with a conveying belt wheel through a rotating shaft, a fixing frame is fixedly installed on the left side of the workbench, and a drawing plate is fixedly installed on the fixing frame through screws. And two side edge plates are fixedly mounted on the rear side of the workbench. According to the copper pipe drawing device, stable movement of the moving plate can be guaranteed through the conveying belt wheel, the positioning wheel and the walking track, copper pipe drawing is achieved, when the moving plate moves, linkage can be achieved to enable the material guiding plate to rotate, the material guiding plate is prevented from affecting normal advancing of the moving plate, the drawn copper pipe can be supported and guided through the material guiding plate, and the copper pipe drawing efficiency is improved. The copper pipe falls into the placing box to be detected, the surface of the copper pipe can be fully detected by driving the copper pipe to rotate, and the visual detection device is linked to move left and right while the copper pipe rotates, so that the detection effect of the copper pipe can be ensured.

The invention relates to the technical field of big data, in particular to a security and protection monitoring information storage system and method based on big data redundancy analysis, and the system comprises a video collection module which is used for collecting and transmitting security and protection monitoring video data in real time; the data transmission module is used for transmitting the collected video data to a storage server through a wired or wireless network; the data redundancy analysis module is used for performing redundancy analysis on the received video data and removing repeated and useless data; the storage server is used for storing the processed security and protection monitoring video data and providing an efficient read-write function; according to the invention, by combining the fluctuation analysis algorithm and the feature extraction algorithm based on machine learning, redundancy detection and data cleaning of video data are realized, data redundancy is effectively reduced, and the storage efficiency is improved; the calculation model of the fluctuation characteristic value and the machine learning characteristic value ensures accurate detection and removal of repeated data.

The invention relates to the technical field of concrete detection, in particular to a concrete water seepage prevention detection device which comprises a supporting shell and trundles. The controllable covering and spraying unit is connected with the supporting shell; the adsorption type supporting unit is connected with the supporting shell, and the adsorption type supporting unit is connected with the controllable covering spraying unit; wherein the controllable covering spraying unit comprises an adjustable spraying assembly, a synchronous variable recycling assembly, a driving type liquid conveying assembly and an induction type transmission control assembly, by arranging the controllable covering spraying unit, the spraying range can be accurately controlled, waste liquid can be synchronously recycled, the detection effect is guaranteed, and the detection efficiency is improved. The device is simple in structure and convenient to use, resource waste can be reduced, meanwhile, stable connection between the device and the outer wall can be guaranteed, the device is fixed, the stability of the device in use is greatly improved, and the reliability of a detection result is guaranteed.

The utility model discloses a multiphase extraction device for separating harmful substances in food detection, which comprises an operating table, a back plate is arranged on the rear side of the upper wall surface of the operating table, and a multiphase extraction structure is arranged at the upper end of the operating table; the multiphase extraction structure comprises a stand column, a soaking box, a filter screen, a switch valve, a funnel, a sliding rod, a sliding extraction assembly and a distillation assembly; the utility model relates to the technical field of food detection, adopts a multi-phase extraction structure, can realize the extraction operation of solid phase and liquid phase of food during use through the arrangement of the soaking box and the separating funnel, is higher in overall matching degree, can effectively reduce auxiliary instruments and ensure the food detection effect while ensuring the extraction efficiency, and is convenient to use. The separating funnel is mounted in a sliding manner, so that the angle and the position of the separating funnel can be converted, and the extracting efficiency of the extracting solution is ensured.

The invention discloses a redundancy-based hardware failure time division multiplexing detection circuit and method, and the method comprises the steps: enabling the counting of an input selection counter to be connected with a first input completion mark outputted by an upstream circuit, and enabling the counting of an output selection counter to be connected with a second input completion mark outputted by a downstream circuit; the output end of the input selection counter is connected with the enabling end of the input selector muxin, and the output end of the output selection counter is connected with the enabling end of the output selector muxout; the input end of the input selector muxin is connected with the output of the upstream circuit, the output end of the input selector muxin is connected with the input end of the redundant circuit, the input end of the output selector muxout is connected with the output of the hardware circuit, and the output end of the output selector muxout is connected with one input end of the comparator; the input end of the redundant circuit is connected with the output end of the input selector muxin, the output end of the redundant circuit is connected with the other input end of the comparator, and the output end of the comparator is connected with the control register.