10266 results about "Rapid detection" patented technology

The invention discloses a full-effect electric energy optimization rapid detection system and method, and particularly relates to the technical field of electric energy detection. The method comprises the following steps: acquiring voltage, current, frequency, harmonic component and three-phase unbalance degree of a power grid in real time, calculating a power grid impedance value and a load dynamic characteristic ratio, drawing an impedance frequency characteristic curve, analyzing a harmonic amplification abnormal degree and a load dynamic response characteristic, and evaluating the accuracy of a detection instruction generated by the full-effect electric energy optimization device. The error correction is carried out on the inaccuracy detection instruction, the duty ratio is dynamically adjusted by utilizing the PWM technology, the accurate matching of the amplitude and the phase of the output current is realized, the error correction effect is evaluated by comparing the real-time detection value with the theoretical calculation value, and the detection model and the compensation strategy are automatically optimized. The problems of detection precision reduction and error compensation caused by complex power grid conditions in the prior art are solved, harmonic amplification, reactive power over-compensation and three-phase balance damage are avoided, and efficient and accurate electric energy optimization and power grid stability improvement are achieved.

The invention discloses a data line surface defect rapid nondestructive detection method based on intelligent image recognition, relates to the technical field of image data processing, and aims to solve the technical problems of difficult defect feature separation and low detection accuracy under complex weaving texture noise interference, and the method comprises the following steps: S1, collecting a data line image and carrying out graying processing; s2, constructing a multi-scale image pyramid, accurately segmenting the image by adopting a self-adaptive threshold algorithm, and realizing rapid detection of surface defects of the data line in combination with Hough transform; s3, frequency domain separation of weaving texture and background noise is realized through Fourier transform, interference is filtered out in combination with a band-pass filtering technology, and then the defect contrast is enhanced through histogram equalization; according to the method, the three-layer Gaussian pyramid is constructed for multi-scale decomposition, and the band-pass filtering technology is combined, so that the weaving texture and the defect signal are effectively separated, the detection accuracy is greatly improved, and the problems of missing detection and misjudgment caused by frequency characteristic confusion are thoroughly solved.

The invention discloses a near infrared spectrum rapid detection system and a real-time analysis method, and the system comprises an environmental parameter and spectrum information collection module, a data processing module, an intelligent evaluation model construction and optimization module, a real-time analysis and evaluation module, and a result feedback and optimization module. The intelligent evaluation model construction and optimization module is used for constructing an intelligent evaluation model by fusing a neural network and a support vector machine, taking the characteristic vector information which is obtained after processing and represents environmental parameters and is associated with infrared spectrum information as input, and taking an expected evaluation result which is associated with the infrared spectrum information of a sample as output; training the intelligent evaluation model; and the real-time analysis and evaluation module is used for inputting the environment parameter information and the infrared spectrum information which are acquired in real time into the trained intelligent evaluation model to obtain an evaluation result of the infrared spectrum information. The system and the method have the advantages of high detection speed, high accuracy and the like.

The invention discloses an intelligent monitoring system for a municipal drainage pipe network, and particularly relates to the technical field of drainage pipe network monitoring. The node operation mode identification module carries out real-time classification and confidence evaluation on the operation state of the pipe network, constructs a multi-attribute pipe network weighted graph based on pipe diameter difference, gradient and confluence density, and extracts multi-scale features through graph Fourier transform. A hybrid anomaly detection link is constructed in combination with an LSTM self-encoder, an isolated forest model and chemical oxygen demand and turbidity water quality verification, and the problems that traditional single-index monitoring is prone to false alarm and missing alarm and inaccurate in positioning are solved; sensor data compensation is realized through cooperation with digital twinning, a rapid detection mode is started during rainstorm early warning, key nodes are processed preferentially, and drainage scheduling is controlled in a closed-loop mode; and target nodes which are easy to accumulate grease are screened based on pipe network topology connectivity, accumulation risks are predicted by fusing multi-sensor data, and a preventive clearing instruction is triggered.

The invention relates to a concrete working performance measurement method and system based on a multi-modal visual large model, and solves the problem that rapid detection of concrete working performance parameters is troublesome, and the method comprises the steps: based on the spatial semantic understanding capability of the multi-modal visual large model, combining a multi-view stereoscopic vision and structured light scanning technology, and calculating the working performance of concrete; reconstructing a three-dimensional geometric structure of the concrete slurry, extracting morphological characteristic parameters, and forming characteristic vectors; inputting the feature vectors into a pre-trained multi-task neural network, fusing the spatial-temporal features and combining a rheological algorithm to identify various working performance parameters; integrating identification results for at least three times by adopting integrated learning, and verifying parameters based on a fluid dynamics basic equation through a fluid simulation platform; and based on the verification result, generating a mix proportion optimization suggestion containing the material components. The method has the advantages that non-contact rapid measurement of concrete working performance parameters is achieved, precision and efficiency are improved, and mix proportion optimization suggestions are provided.

The invention, which relates to the technical field of data security monitoring, discloses a network data risk assessment system for a computer, comprising a data monitoring platform. The data monitoring platform is in communication connection with a data acquisition module, a data preprocessing module, an anomaly detection module, a threat evaluation module, an automatic alarm module, a response strategy generation module and a report generation module, and all the modules are in electric signal connection. According to the network data risk assessment system for the computer, the functions of anomaly detection, threat assessment, response strategy generation and automatic alarm are integrated, rapid detection and response to network threats are achieved, abnormal mode recognition is conducted through the machine learning technology, potential security threats are found, the recognition accuracy of abnormal behaviors is improved, and in addition, the network data risk assessment system for the computer has the advantages of being high in practicability and easy to popularize. And preset protection measures are automatically triggered, so that quick response is realized when high-risk threats are detected, and the damage of security incidents to the system is reduced.

The invention discloses a power supply fault rapid detection system and method based on an intelligent diagnosis engine, and belongs to the technical field of power electronic equipment fault diagnosis, and the system comprises a function region division module which is mainly used for dividing a plurality of function regions according to a power supply topological structure; the real-time data acquisition module is mainly used for acquiring parameter data of each functional area in a power supply operation stage; the current model building module is mainly used for building a current time sequence prediction model; the trend prediction module is mainly used for generating a future current change trend; the abnormity judgment module is mainly used for identifying an abnormal condition by performing similarity matching with real-time data; the fault positioning module is mainly used for matching with a fault mode based on an abnormal current waveform and constructing a directed graph to position a fault position; the method has the advantages that early warning and accurate positioning of the power supply fault are realized through multi-modal data fusion and a deep learning model, and the operation and maintenance efficiency and reliability of the power electronic equipment are remarkably improved.

The present disclosure relates to a system and method for vulnerability localization based on deep learning, which comprises, at a minimum, a processor configured to: analyze a code file under detection to obtain a first abstract syntax tree devoid of semantic information; build upon the first abstract syntax tree by incorporating data-flow edges and / or control-flow edges, thereby forming a second abstract syntax tree with semantic-flow enhancement; split the second abstract syntax tree to obtain a plurality of second abstract syntax sub-trees; and input these second abstract syntax sub-trees into a pre-established vulnerability detection and localization model. Compared with existing code vulnerability detection methods, the present disclosure employs a semantically-enhanced abstract syntax tree and finely-grained segmentation thereof, enabling both the efficient detection and accurate localization of code vulnerabilities, characterized by swift detection rates, low false positive rates, and commendable interpretability of the detection results.

The invention relates to a non-standard special-shaped part high-precision measurement and verification method based on three-dimensional vision, and belongs to the field of three-dimensional reconstruction and computer vision. Point cloud data of a non-standard special-shaped part are obtained through multi-angle scanning by means of multi-camera-projector collaborative layout in combination with a structured light coding technology and a phase shift code technology; the method comprises the following steps of: acquiring point cloud data, ensuring comprehensiveness and accuracy of the data, realizing high-precision alignment of the point cloud data through matching an underivative optimization algorithm with image feature points, and performing fusion, denoising and registration processing on the acquired point cloud data to ensure that point clouds acquired from a plurality of angles can be effectively fused so as to reconstruct a high-precision three-dimensional model. In the measurement and verification stage, the deviation between the point cloud data and the CAD model is measured through a standard coordinate system, multi-stage registration and a high-precision algorithm, rapid detection and size verification of the geometric deviation of the industrial workpiece are achieved, and reliable technical support is provided for quality control.

The invention discloses a terahertz-based curved surface thermal barrier coating nondestructive thickness measurement system and method, and the method comprises the steps: firstly carrying out the coordinate calibration and reverse reconstruction of a sample piece and a mechanical arm through an optical scanning and trinocular vision measurement system, and building a virtual space; then matching the virtual space with the actual space, extracting a normal vector of the curved surface of the sample piece, performing path planning on the mechanical arms to generate a control script, and controlling the double mechanical arms to carry a terahertz measuring head to scan the sample piece in a manner of being vertical to the surface of the sample piece to obtain a terahertz signal; and finally, constructing a terahertz signal analytical model, optimizing terahertz simulation signal parameters through a self-adaptive teaching and learning optimization algorithm, and fitting a detection signal until an optimal solution is obtained, namely the thickness of the thermal barrier coating. According to the invention, through cooperation of the two mechanical arms and path planning, rapid detection of a complex curved surface structure is adapted; by establishing an analytical model and combining with an optimization algorithm, the detection precision is improved, and high-precision nondestructive detection suitable for a complex curved surface structure is realized.

The invention relates to the technical field of monitoring or controlling a charging station, in particular to a charging pile safety monitoring method and system, and the method comprises the following steps: step 1, monitoring: step 1.1, electric leakage monitoring: installing a high-sensitivity electric leakage sensor in a charging pile and a charging gun line, continuously monitoring the electric leakage current in real time, and monitoring the electric leakage current in real time; the monitoring frequency is that data are collected once every 10 milliseconds; step 1.2, overload and short circuit monitoring: real-time current change of a charging circuit is monitored by using a current transformer, and the acquisition frequency is once every 5 milliseconds; step 1.3, monitoring the temperature; according to the scheme, through monitoring of electric leakage, overload and short circuit, temperature, smog and flame, charging gun safety, vision and the like in combination with intelligent analysis, the operation state and the surrounding environment condition of the charging pile can be comprehensively, timely and accurately mastered, electrical faults, temperature anomalies and potential safety hazards can be rapidly detected and judged, and the safety and reliability of the charging pile are improved. And all-around real-time monitoring of the potential safety hazard of the charging pile is realized.

The invention discloses a production line aluminum alloy ingot surface defect detection method, device and system and a storage medium. The method comprises the steps that an aluminum alloy ingot surface defect image data set is collected; selecting YOLOv5 as a detection model, and optimizing the initial anchor frame for an application scene by using a K-means + + algorithm; carrying out accelerated reasoning improvement on the YOLOv5 detection model, and meanwhile, fusing an attention mechanism to dynamically detect a head Dyhead; a Mixup and Mosaic data enhancement algorithm is adopted to enhance the data set, an improved YOLOv5 detection model is trained, and a Focal loss function and non-maximum suppression are introduced during training; and detecting the surface defect image of the aluminum alloy ingot by using the trained model to obtain the type confidence coefficient and position information of the defect in the image. With the adoption of the technical scheme, the types and positions of the defects can be quickly detected, so that the requirement of quick and accurate detection of an aluminum alloy ingot production line is met.

The invention provides a full-layout defect rapid detection system based on pattern matching and classification, which relates to the technical field of semiconductor manufacturing and comprises a data acquisition and multi-source fusion module, a self-adaptive preprocessing module, a dynamic pattern matching module, a multi-source feature fusion module and a closed-loop optimization and output module, the dynamic mode matching module dynamically generates a defect template from real-time data through a clustering algorithm (such as DBSCAN) and a generative adversarial network (GAN), the speed and accuracy of defect detection are remarkably improved in combination with reinforcement learning filtering and region focusing technologies of the self-adaptive preprocessing module, a detection strategy can be adaptively adjusted according to the real-time data, and the defect detection accuracy is improved. According to the method, a high-risk area is preferentially matched, and GPU parallel computing acceleration processing is performed, so that the detection time is shortened to 50% or below of that of a traditional method, deformation or fuzzy defects can be effectively identified, the false alarm rate is reduced by about 30%, and high-quality input is provided for subsequent matching and classification.

The invention relates to the technical field of building steel plate processing, in particular to a laser cutting method and a cutting production line structure for spliced building steel plates, which comprises the following steps of: S1, preliminary rapid detection, S2, uneven area length measurement, S3, three-dimensional data acquisition and S4, cutting link. S2, triggering execution of the step S2 according to a detection result of the step S1 and providing an initial scanning area, providing a precise scanning area for the step S3 according to the unsmooth area range determined in the step S2, and providing a parameter adjustment basis for the step S4 according to the three-dimensional form data obtained in the step S3 so as to realize precise control of the whole process from detection to cutting. The invention aims to solve the potential safety hazard problem that when spliced building steel plates are cut in the prior art, particularly under the condition that joints are uneven, the cutting precision is obviously reduced, and the strength and stability of a building structure are possibly reduced.

The invention relates to the technical field of underground casing monitoring, and discloses an injection well immovable string casing damage rapid detection device based on efficient testing and adjusting, the injection well immovable string casing damage rapid detection device comprises a testing vehicle, a top sheave and a casing, one side in the testing vehicle is provided with a ground control system, and the other side in the testing vehicle is provided with a double-roller winch; a cable is wound on the outer side of the double-roller winch, an electromagnetic flaw detection testing instrument is installed at one end of the cable, an oil pipe is arranged in the casing pipe in a sleeved mode, a plurality of packers are sequentially installed between the outer side of the oil pipe and the inner wall of the casing pipe from top to bottom, and a plurality of water distributors are sequentially distributed in the oil pipe from top to bottom. A water injection well efficient testing and adjusting technology and an electromagnetic flaw detection technology are combined, an electromagnetic flaw detection testing instrument is tripped in through a testing vehicle, cable power supply and communication are tested, a ground control system carries out signal acquisition and processing on the electromagnetic flaw detection instrument, and rapid detection of casing damage of a water injection well immovable string is achieved.

The invention relates to the technical field of surface measurement, in particular to a road surface flatness rapid detection method based on a laser radar, which comprises the following steps: collecting vehicle attitude data to form a three-dimensional vector, mapping laser data to a space coordinate system, identifying a projection difference adjustment track, detecting a disturbance generation distribution area, and transversely distributing a translation direction line. Uniformly projecting to construct a reference line, extracting an echo and time index recombination time sequence segment, analyzing a point number trend to screen a stable segment, and extracting symmetric points to compare an included angle to generate a closed zone. According to the method, the attitude vectors of the vehicle in rolling, pitching and yawing states are obtained, the laser echo distance measurement data are mapped to the absolute space coordinate system, and the included angle difference between the distance measurement direction and the ground direction is accurately calculated, so that the space matching precision of a measurement path is remarkably improved, and a longitudinal section analysis result is closer to a real road state; and the space integrity, the track smoothness and the section structure rationality of the whole measurement process are comprehensively optimized.

A ginkgo canopy chlorophyll content estimation method based on an airborne hyperspectral image comprises the following steps: firstly, acquiring a ginkgo leaf canopy hyperspectral image and a chlorophyll content data set; and establishing a rapid prediction model for predicting the average content of chlorophyll in the canopy by using the hyperspectral image of the ginkgo leaf canopy, and predicting the corresponding chlorophyll content by using the model and taking the hyperspectral image of the ginkgo leaf canopy as input. The model establishment process comprises the following steps of: firstly, preprocessing an image by using multiple scatter correction (MSC); secondly, carrying out characteristic wave band selection on the preprocessed spectral data by adopting a competitive self-adaptive reweighted sampling algorithm CARS; and finally, substituting the screened characteristic wave band spectral data and the actually measured chlorophyll value into an XGBoost-RF machine learning model, and establishing a hyperspectral chlorophyll content estimation model. The method provided by the invention is a nondestructive rapid detection technology, and can monitor the plant health condition in real time.

The invention provides a rice seed vigor rapid detection method based on near infrared spectrum, and belongs to the technical field of spectral analysis. The method comprises the following steps: performing different degrees of aging treatment on rice seeds in a high-temperature and high-humidity environment, taking unaged rice seeds as a control group, collecting spectral data of the rice seeds subjected to different degrees of aging treatment, and preprocessing the data; taking out the seeds of each treatment group, and measuring physiological indexes including germination rate, germination potential and the like; based on the preprocessed spectral data, a data enhancement method is adopted to fully embody internal characteristics and category differences of sample data; a Conv1D-GRU framework is applied, the model can capture spatial features and time sequence information in spectral data at the same time, a full-connection layer predicts extracted comprehensive features by means of vitality, a classification result is output through a softmax layer, a hybrid optimization population search algorithm is used for further optimizing the performance of the model, and the performance of the model is improved. Therefore, the activity of the rice seeds can be rapidly detected according to different aging degrees of the rice seeds.

The invention relates to the technical field of wood detection, and particularly discloses a wood furniture material rapid detection system, which utilizes a portable spectrum scanner to collect spectrum scanning images of a wood furniture material to be detected from a plurality of positions, and introduces a spectrum image processing technology based on deep learning. Spectral feature extraction, correlation analysis of spectral features of all the positions and complementary information enhanced polymerization processing are performed on the spectral scanning images of the multiple positions, so that global spectral feature description of the to-be-detected wooden furniture material is constructed; based on matching analysis between the spectral characteristics of the wooden furniture material to be detected and the spectral characteristics of the known wood type, rapid and accurate detection of the wooden furniture material is realized. In this way, the limitation of a traditional wood identification method and the defects of an existing spectrum detection method can be effectively overcome, and the accuracy and efficiency of wood furniture material detection are improved.

The invention discloses a method for analyzing mineral trace elements by utilizing a micro-Raman spectrum, which comprises the following steps of: fixing a mineral sample on a rotary sample table of a micro-Raman spectrometer, selecting an area to be detected by combining a micro-imaging technology, collecting a Raman background signal under the pre-illumination of low-power laser, and analyzing the trace elements in the area to be detected according to the Raman background signal. The laser focal length, the spot size and the incident angle are adjusted accordingly, the laser is focused on a target area, under the calibrated laser condition, through a multi-angle excitation micro-area, after an algorithm is applied to optimize signals, reduce impurity interference and integrate multi-angle Raman signals, the characteristic spectrum information of the microelements can be extracted, and the microelements can be accurately detected. High-resolution analysis of sample components is realized, and a three-dimensional distribution model of microelements in a microcell is constructed in combination with microscopic imaging data. The method has the characteristics of non-destructiveness, high resolution and rapid detection, and is suitable for mineral component analysis in the fields of geology, material science and the like.

The invention relates to the technical field of foamed silicone rubber surface detection, and discloses a foamed silicone rubber surface detection method based on image visual identification, which comprises the following steps: acquiring a color image of a foamed silicone rubber surface, carrying out graying and normalization processing, and generating a diffuse reflection effective detection area by using a polarization filter difference technology; color abnormity is detected based on an image gridding and gray statistical method, meanwhile, pore and microbubble defects are detected in combination with minimum value seed point extraction and an eight-neighborhood region growing algorithm, finally, various masks are fused, the defect area and number are counted, and a quality inspection report is generated; the method can effectively inhibit highlight interference and accurately identify uneven colors and pore microbubbles, the detection process does not need external training, calculation is efficient, and the method is suitable for rapid detection and quality control of surface defects of foamed silicone rubber.

The invention belongs to the technical field of biological detection, and particularly relates to a monoclonal antibody 4F6 for HPV16 type E7 protein detection and application. The heavy chain variable region CDR sequences and the light chain variable region CDR sequences of the antibody are SEQ ID NO.1-3 and SEQ ID NO.4-6 respectively, and the antibody can be specifically combined with HPV16 type E7 protein without cross reaction. A double-antibody sandwich ELISA detection system constructed based on the antibody is combined with a biotin-avidin amplification technology, the sensitivity reaches 100 pg / mL, and the antibody is suitable for rapid detection of the HPV16 type E7 protein in a cervical exfoliated cell sample. The invention further provides the HPV16 type E7 recombinant protein which is obtained through prokaryotic expression and has immunocompetence, and the HPV16 type E7 recombinant protein is applied to antibody screening and detection. Compared with nucleic acid detection, the method has a lower false positive rate, can be used for early screening of cervical cancer and precancerous lesions, and has a good application prospect.

The invention discloses a method and a system for efficiently and rapidly detecting protein content and moisture content in a plant-based product based on near infrared spectrum and deep learning, and the method comprises the following steps: obtaining spectral data of a plant-based sample, and carrying out experimental determination on the protein content and the moisture content; in combination with multi-step data preprocessing and adaptive dimensionality reduction, feature signals highly related to target detection are extracted, and a deep learning model based on a convolutional neural network and a self-attention mechanism is utilized to construct a high-precision detection model; a final stable detection model is obtained through multiple times of verification and optimization of the model, and hardware integration is carried out on the final stable detection model and a portable spectrometer to form a detection system. The detection system comprises a 3D printing shell, a spectrum sensor, a conversion line and a data processing unit, the protein content and the water content of a sample can be rapidly and nondestructively detected through one-key operation, and the whole process does not need chemical reagent intervention. The method has the advantages of being high in detection speed, high in portability, easy and convenient to operate, wide in application scene and the like, and is particularly suitable for application occasions needing efficient and on-site real-time detection.

The invention discloses an intelligent road disease detection method and equipment based on dynamic multi-scale convolution. The method comprises the following steps: performing target category labeling on a training sample set to form a corresponding relationship between sample data and labels; establishing a detection framework including a backbone network, a neck network and a detection network, the backbone network extracting features through convolution layer and cross-stage local layer stacking, introducing spatial pyramid pooling to enhance multi-scale expression, and enhancing fine-grained disease area attention by enhancing spatial attention; the neck network is integrated with dynamic multi-scale convolution and is matched with an upper sampling layer and a cross-stage local layer to fuse high and low layer features; the detection network is provided with a plurality of parallel detection heads, and disease detection is carried out corresponding to different scale feature maps. According to the method, rapid detection and accurate positioning of disease targets of various sizes can be realized, and the detection capability of sparse and small-scale disease targets is remarkably improved through collaborative optimization of a dynamic multi-scale convolution kernel and an enhanced space attention mechanism.

The present invention relates to the technical field of image detection, and in particular to a method for rapid detection of a fracture region in a precision-stamped part of a new energy vehicle. The method comprises: acquiring a complete stamped part grayscale image; acquiring each sub-region in the complete stamped part grayscale image; for each sub-region, taking a fitting curve of all edge pixels of the sub-region as an edge curve of the sub-region, and constructing a curvature evaluation factor between each pixel and the adjacent previous pixel on the edge curve; constructing the degree of curvature change of the edge curve; constructing a curvature sequence and a distance sequence of the edge curve; acquiring each subsequence of the curvature sequence of the edge curve; constructing the fracture edge matching degree, a local stress discontinuity index and a fracture region confidence level of the sub-region; and on the basis of the fracture region confidence levels of all sub-regions, using a clustering algorithm to cluster and delineate a fracture region. According to the present invention, the accuracy of detection of the fracture region in the precision-stamped part of the new energy vehicle can be improved.

The invention relates to the technical field of livestock breeding, in particular to a cow manure automatic scoring system and method.The cow manure automatic scoring system comprises an image acquisition unit, a physical property sensor, a chemical component detection module and a data processing unit.The image acquisition unit is used for capturing the form, color and fiber residue of manure; the physical property sensor is used for measuring the water content of excrement and evaluating the viscosity and hardness of the excrement; the chemical component detection module comprises a near infrared spectrum analysis unit and an electrochemical sensor unit; and the data processing unit is used for integrating an AI algorithm, realizing multi-source data fusion and scoring, and uploading the data to the cloud management platform through the communication module. Therefore, the problems that in the prior art, manual scoring is subjective, low in efficiency and difficult to quantify, diseases are difficult to find early due to the lack of rapid component detection means, and accurate nutrition regulation and control are difficult to achieve due to the fact that scoring data are difficult to integrate into a breeding system are solved.

The invention relates to the field of environment monitoring, and discloses a mobile VOCs rapid detection and positioning system, which comprises a data acquisition module, a data fusion module, a concentration gradient analysis module, a Bayesian filtering module and a particle filtering optimization module, the data acquisition module is used for acquiring VOCs concentration data and multi-source positioning data; the data fusion module outputs high-precision position information for the positioning data; the concentration gradient analysis module is used for calculating a concentration gradient based on the VOCs concentration data and meteorological information and simulating concentration field distribution; the Bayesian filtering module dynamically updates the probability distribution of the pollution source position in combination with the concentration field distribution and the fusion position information; and the particle filtering optimization module optimizes the Bayesian filtering result. The system is suitable for various complex environments, can realize rapid detection of VOCs and high-precision positioning of pollution sources, and has good real-time performance, precision and environmental adaptability.

The invention relates to the technical field of water quality pollution detection, in particular to a water quality pollution rapid detection method driven by a quantum sensing technology. A quantum sensor array is deployed in a water area to be detected, and quantum signal response data of the water area is collected; constructing a pollutant quantum recognition model, and inputting quantum signal response data into the model to obtain an initial predicted value of pollutant concentration; generating an initial detection parameter of the quantum detection instrument based on the initial predicted value, executing primary pollution detection by the instrument according to the parameter, and collecting primary deviation data of an actual detection value and an expected detection value; determining a detection error according to the deviation data, further correcting the initial detection parameter, and generating an optimized detection parameter; and finally, the instrument performs secondary pollution detection based on the optimized detection parameters. According to the method, complex water sample pretreatment is not needed, water quality pollution detection can be rapidly and accurately completed, detection requirements of different water areas are met, and the detection efficiency and accuracy are improved.

The embodiment of the invention discloses a novel fish acute septicemia detection system based on hyperspectral imaging. The system comprises a data acquisition module, a model detection module and a user interaction module, wherein the data acquisition module acquires hyperspectral image data on the surface of a target fish body; the model detection module carries out diagnosis by taking the hyperspectral image data as input based on a trained deep learning diagnosis model, and the deep learning diagnosis model obtains a pixel space structure and a pixel spectrum sequence of the fish body surface based on the hyperspectral image data; identifying spatial features representing related clinical symptoms based on a pixel spatial structure, and identifying spectral features representing an inositol metabolism condition based on a pixel spectral sequence; and the user interaction module outputs a diagnosis result of the target fish body based on a result output by the model. The characteristic that the aeromonas hydrophila utilizes inositol as a unique carbon source and the map change of typical infection symptoms on the surface of a fish body are combined, and accurate and rapid detection is achieved through hyperspectral imaging and a deep learning algorithm.

The invention discloses a pressure vessel welding seam nondestructive rapid detection device, which comprises a displacement mechanism and a to-be-detected mechanism, the to-be-detected mechanism comprises supporting frames, a pressure vessel main body is placed at the upper top ends of the two supporting frames, and accessories are welded and formed at the two ends of the pressure vessel main body. The lifting frame rotates to drive the two sets of knocking mechanisms to move up and down, the reset spring is compressed in the process, the two sets of knocking mechanisms move in a staggered mode, the welding position inside the pressure container body and the accessory is impacted, and the welding position is detected through the laser detection head. When the pressure vessel is impacted, whether the pressure vessel is damaged or not after being impacted is checked, if the pressure vessel is damaged, the marking pen can achieve fine adjustment of arc-shaped movement with the help of the marking mechanism so as to make contact with the inner wall of the pressure vessel body, and due to rotation of equipment, marking treatment is conducted on the damaged position, and follow-up treatment is facilitated for workers.