7864 results about "Rapid detection" patented technology

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 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 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 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 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.

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 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 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.

The invention provides a gear geometric parameter rapid detection method and system based on wireless transmission, and relates to the technical field of gear detection, and the method comprises the steps: for a to-be-detected gear, extracting gear features based on a deep learning network, and determining a position combination of optimal measurement points in combination with a gear precision grade requirement; collecting geometric parameter data of the gear based on the position combination; and processing the geometric parameter data by adopting a cascade neural network to obtain actual geometric parameters, sending the actual geometric parameters to a remote monitoring terminal through a wireless transmission module with a self-adaptive channel selection function, and generating a detection result report.

The invention relates to the technical field of energy monitoring, in particular to an on-line energy monitoring system and method, a multi-source data acquisition module realizes omnibearing and multi-level energy network monitoring by integrating various sensors, and the comprehensiveness and accuracy of data are ensured; the adaptive acquisition control module establishes a risk assessment model by using historical data, dynamically adjusts the acquisition frequency and parameter range of the sensor, and optimizes the data acquisition efficiency. The data preprocessing module improves the data quality through filtering, denoising, alignment and standardization processing, and provides a reliable basis for subsequent analysis; the energy analysis module supports system optimization scheduling and energy-saving decision, rapidly detects abnormal points, and reduces manual intervention cost. And the intelligent response module formulates a response strategy according to an analysis result, so that self-regulation and optimization of the system are realized, and the energy utilization efficiency and the equipment operation reliability are improved.

The invention discloses a road video event rapid detection method based on edge calculation, particularly relates to the field of computer vision and image processing, and is used for solving the problem of motion evidence distortion caused by frame-level sequential disorder and content gaps in a road monitoring video. The method comprises the following steps of: reading a video clip, constructing a time sequence correction graph according to inter-frame content similarity and boundary continuity, rearranging to generate a corrected sequence, outputting a frame consistency index, estimating a stable motion field and a continuity score on the corrected sequence, and writing back an image stabilization amplitude correction time anchor point; generating an event evidence sequence according to the stable motion field, combining track breakpoints according to a time sequence correction graph, initiating backtracking revision to return candidate event segments, and if consistency verification is executed on candidate events, performing cross-level revision on the time sequence correction graph and image stabilization amplitude to trigger the stable motion field to re-estimate and output a confirmation event; and issuing a trigger result and establishing a mapping index as a new fragment to initialize priori acceleration time sequence correction and judgment convergence.

The invention discloses a self-powered biosensor for double-signal detection of sugarcane pokkah boeng disease, the self-powered biosensor comprises an anode, a cathode and an electrolyte, the anode is a CuCo-MOF / AuNPs / GOD biological anode, the cathode is a CuCo-MOF / AuNPs / DNA chain biosensor, and the electrolyte comprises glucose, MB and a PBS buffer solution. According to the self-powered biosensor, analysis is carried out through two non-interfering modes of electrochemical detection and colorimetric detection, and whether a detection result is a false positive signal or not can be effectively verified. An external power supply is not needed, and real-time outdoor detection can be realized. The material source is wide, the assembly process is simple, and accurate and rapid detection can be realized. The method has the advantages of high sensitivity, good selectivity and real-time detection, and can be used for accurately detecting the sugarcane pokkah boeng rot.

The invention relates to the technical field of tunnel fire detection, and provides a rapid tunnel moving fire detection method, which comprises the following steps of: calculating signals received by sensors in parallel by an intelligent algorithm, extracting undistorted characteristic signals in time, and combining data from a plurality of sensors through a data fusion technology; false alarms possibly generated by a single sensor are reduced, so that the fire detection accuracy is improved, and the time from fire occurrence to alarm giving is shortened; by fusing data of a plurality of sensors, the system is more robust to faults or abnormities of a single sensor, and the stability and reliability of the system are improved; the intelligent algorithm can be combined with the time domain oscillogram of a plurality of detectors, space and time information can be extracted in time, the specific position of the fire can be positioned more accurately, and fire extinguishing measures can be taken quickly.

The invention discloses an environment-adaptive transformer oil sample Raman spectrum detection method and related equipment, and relates to the field of optical sensing systems. The method comprises the following steps: collecting oil sample Raman spectrums and environmental parameters in multiple operation scenes, and constructing a multi-scene spectrum characteristic model and a standard fingerprint database; pre-processing and denoising parameters are adaptively set based on the environmental perception vector, and baseline correction and joint denoising are carried out on the original spectrum; scene discrimination is carried out by fusing the characteristics of peak position, peak height, peak width, integral area and the like, a scene-related component standard spectrum dictionary is generated, and the concentration and confidence of each target component are obtained by adopting constrained spectral line unmixing and quantitative calibration; and driving the fingerprint database and the model to update in combination with quality control indexes such as spectral shape relevancy and residual errors and a drift detection result. The system is composed of a Raman spectrum acquisition module, an environment monitoring module and a data processing module, and can improve the robustness and quantitative precision of Raman detection of transformer oil in a complex environment.

The invention relates to the technical field of surface detection, and particularly discloses multi-angle detection equipment for a chip tray, the multi-angle detection equipment for the chip tray achieves the purpose of cleaning and detecting the surface of the tray, an equipment base and an equipment support integrally support the equipment, and a fixing device fixes the tray, so that the detection efficiency is improved, and the detection cost is reduced. The tray is vertically inserted into the top of the fixing device, the fixing device fixes and rotates the tray and cooperates with the detection device for illumination and multi-angle shooting through light reflection, the detection efficiency is improved compared with a traditional plane shooting visual detection scheme, the influence on the surface of the tray is reduced by fixing the two sides of the tray, and the detection accuracy is improved. Therefore, secondary damage caused by surface damage in the detection process is avoided, and the protection device drives the light source of the detection device to adjust the distance, so that optical detection on different areas of the surface of the tray is realized, and the overall flatness of the surface of the tray can be quickly detected.

The invention provides a food heavy metal pollution detection system based on plasma emission spectrum analysis, and aims to realize rapid and accurate detection of heavy metal elements in a food sample. The system comprises a sample processing module, an excitation module, a spectrum detection module, a signal processing module, a data analysis module and an output module. The sample processing module converts a food sample into an aerosol or steam state. The spectrum detection module is used for extracting spectrum characteristic information of heavy metal elements. And the signal processing module adopts an AI-driven spectrum denoising algorithm to remove background noise and interference signals. And the data analysis module calculates the concentration of each element based on the characteristic peak intensity and generates heavy metal pollution data. And the output module is used for visually displaying the detection result. According to the method, complex sample pretreatment steps are reduced, the detection efficiency and sensitivity are improved, and the method is suitable for food safety detection scenes and particularly has remarkable advantages in field rapid detection application of agricultural products, drinking water and processed food.

The invention relates to the technical field of analysis of physical and chemical properties of soil, in particular to a dry farming soil fertility rapid detection and fertility improvement method, which comprises the following steps: sorting a colloidal phase and a dissolved phase of a humic acid complex based on density difference and dielectric property difference through a gradient centrifugation and dielectrophoresis enriched physical field synergistic separation technology; a two-channel detection time sequence is optimized by adopting a genetic algorithm, a non-overlapping time sequence signal flow is generated by taking a signal interference suppression ratio as a fitness function, and time domain cross interference is suppressed. A programmable resistance-capacitance compensation model is constructed, parallel capacitor array parameters are dynamically adjusted to correct electrochemical phase angle offset, and a three-dimensional fertility distribution curved surface and a residual map are generated in combination with a Kriging interpolation model. On the basis of residual spectrum spatial distribution characteristics, a cross-linked slow-release system is deployed to improve the organic matter binding capacity of a humic acid deficiency area, a mesoporous zeolite-based passivator is applied to regulate and control an ion migration path, a closed-loop feedback mechanism is formed through dynamic phase response monitoring, and accurate detection and directional remediation of soil fertility are achieved.

The invention belongs to the technical field of pathogenic microorganism detection, and particularly relates to a sequence combination for detecting cochliobolus miyabeanus based on RPA-CRISPR / Cas12b and application of the sequence combination. The CRISPR / Cas system contained in the composition comprises Cas12b and sgRNA, and the CRISPR / Cas system can be cooperatively used with a specific reaction system. The rapid detection method comprises the following steps: sample pretreatment: treating a sample to extract nucleic acid; performing amplification reaction: performing RPA amplification reaction on the extracted nucleic acid by using the composition, and applying a nucleic acid amplification product to CRISPR / Cas system detection; result interpretation: reading and judging a detection result through a quality control line and a detection line. The composition and the kit provided by the invention are specially used for detecting the cochliobolus miyabeanus. The method is simple and convenient to operate, rapid in detection, short in time consumption, low in cost and suitable for on-site rapid detection.

The invention discloses a terminal crimping quality detection method and system, and relates to the technical field of terminal crimping image detection. Comprising the steps that space coordinates of a standard datum point in a crimping image are determined, and the standard datum point is used for representing the space coordinate position, meeting the standard terminal crimping inspection standard, between a corresponding wire and a terminal notch. According to the method, the crimping samples which do not meet the standard terminal crimping inspection standard are rapidly removed in a spatial positioning mode, and then the crimping sample thermal imaging images which do not meet the standard terminal crimping inspection standard are obtained on the crimping surfaces of the crimping samples by means of trigger logic. According to the method, the crimping samples are detected, the invisible faults of the crimping samples are judged based on the set standard terminal crimping inspection standard, and finally different commands are input to the crimping equipment according to the types and severity levels of the invisible faults, so that the rapid detection of the crimping samples and the dynamic adjustment of the crimping equipment are realized, and the stability of the monitoring dimension of the crimping quality is ensured.

The invention provides a gear keyway width measuring method applying machine vision, and relates to the technical field of gear detection. The method comprises the steps of image acquisition, preprocessing, boundary extraction, contour separation, central point calculation, pixel equivalent calculation, symmetric line slope calculation and keyway width calculation. The method specifically comprises the steps of extracting a boundary through a Sobel operator, separating a contour based on a connected domain area, calculating a central point through an arithmetic average method, determining a pixel equivalent in combination with the actual size of an inner hole, achieving symmetric line slope calculation and point classification through iterative fitting and Hough conversion, and finally completing width measurement. According to the invention, a non-contact machine vision technology is adopted to replace traditional contact measurement, so that the problems of large manual operation error, low automation level and insufficient online detection capability are solved, high-precision and automatic measurement of the width of the gear keyway is realized, the measurement efficiency and the result reliability are improved, and the production cost is reduced. The method is suitable for a rapid detection scene in gear batch production.

The invention provides an infrared small target detection method and system based on depth-guided low-rank sparse decomposition, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining all original infrared images shot by remote sensing equipment, and sequentially stacking the original infrared images according to an obtaining time sequence, and obtaining an infrared original tensor; performing low-rank background and sparse target decomposition processing based on the infrared original tensor to obtain a low-rank sparse tensor decomposition model; a low-rank background tensor containing nonlinear transformation is obtained through processing of a constructed hierarchical nonlinear tensor ring background module; processing through a sparse target module fused with an attention mechanism to obtain a sparse feature tensor of the infrared small target area; and reconstructing a low-rank sparse tensor decomposition model guided by the deep neural network, and carrying out solving processing to obtain a final infrared small target detection result. According to the invention, accurate, robust and rapid detection can be carried out on a small target under a complex background.

The invention relates to the technical field of biological detection, in particular to a monoclonal antibody combination for HPV18 type E6 protein detection and application. The provided combination is composed of 5G3 and 2C7, and the amino acid sequences of complementary determining regions of variable regions of a heavy chain and a light chain of the combination are clear and are respectively shown as SEQ ID NO.1-12. The antibody combination has high specificity and sensitivity, the lowest detection limit can reach 100 pg / ml, and cross reaction with other HPV subtypes is avoided. According to a double-antibody sandwich ELISA and biotin-avidin amplification detection system constructed based on the combination, the signal intensity and the detection accuracy are remarkably improved, and the combination is suitable for rapid detection of the HPV18 type E6 protein in a cervical exfoliated cell sample and has application value in early diagnosis of cervical cancer, risk stratification, vaccine research and development and curative effect evaluation.

The present invention relates to the technical field of signal processing and fault diagnosis. The method of the present invention comprises: collecting a flame chemiluminescence signal, and performing adaptive filtering noise reduction processing; using an improved proper orthogonal decomposition method and a high-order mode to obtain comprehensive combustion feature information; developing a real-time synchrosqueezing transform algorithm to perform time-frequency analysis on data, and updating energy distribution in real time; taking into account sensor data to perform multi-parameter joint analysis to obtain a combustion state evaluation, using a plurality of high-speed cameras to acquire flame images from different angles, reconstructing three-dimensional flame morphology by means of a computer vision technology, and providing fault detection information; and constructing a fault prediction model to give an early warning of a combustion fault. The present method implements nondestructive and rapid detection of oscillating combustion faults, keeps rich information of flame images, reflects overall flame pulsation characteristics, increases the speed and accuracy of detection, and compensates for the defect of low frequency resolution of conventional time-frequency analysis methods.

The invention provides a near-infrared spectrum rapid detection method and system for effective components of a herbicide, which are applied to the technical field of agricultural detection.The near-infrared spectrum rapid detection method comprises the steps of obtaining near-infrared spectrum data of a to-be-detected soil area and moisture content and compactness numerical values reflecting soil texture differences, and calculating the effective components of the herbicide on the basis of a preset interval to which the moisture content numerical value belongs; according to the method, the corresponding preset scattering correction parameter is selected, the actual scattering correction parameter is obtained through compactness numerical calculation, fine adjustment of the scattering correction parameter is achieved, and the quality and reliability of spectral data are improved through spectral correction; and the corrected spectrum is analyzed, so that the real condition of the herbicide in the soil can be reflected more accurately. Therefore, the interference of complex soil texture in the field on near infrared spectrum herbicide detection is effectively overcome, and the beneficial effect of rapidly and accurately detecting the effective components of the herbicide under the complex soil condition is achieved.