4162results about How to "Quick check" patented technology

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

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 relates to the technical field of user behavior analysis, and discloses a method for analyzing abnormal user behaviors based on logs and flow data, and the method comprises the steps: collecting user behavior data, including historical user behavior data and real-time user behavior data; based on historical user behavior data, constructing a group behavior baseline and an individual behavior baseline, and obtaining key behavior characteristics of the user; the deviation degree DEV of the key behavior characteristics of the user deviating from the group behavior baseline and the matching degree SimScore of the real-time behavior sequence of the user and the personal behavior baseline are calculated; according to the deviation degree DEV and the matching degree SimScore, whether the user behavior is abnormal or not is judged, and the risk level of the abnormal behavior is determined; triggering corresponding abnormal alarm and response measures according to the risk level; according to the method, the abnormal behavior of the user can be identified more accurately by fusing the log and the flow data and combining group and individual behavior baselines, and the accuracy and efficiency of anomaly detection are improved.

The invention relates to the technical field of concrete pouring, in particular to a concrete pouring homogeneity detection system and method based on sensing data, and aims at achieving efficient and accurate concrete pouring homogeneity evaluation and defect detection. The system comprises a multi-sensor sensing module, a multi-sensor data conversion and transmission module and a concrete homogeneity evaluation module, the multi-sensor sensing module comprises a temperature sensor, an ultrasonic sensor and a vibration sensor; the multi-sensing data conversion and transmission module is used for acquiring multi-sensing data and performing data type conversion and transmission on the multi-sensing data; and the concrete homogeneity evaluation module is used for constructing a concrete pouring homogeneity evaluation model to detect and evaluate the homogeneity and defects in the concrete pouring process. According to the invention, detection and evaluation of the concrete pouring homogeneity are realized through combination of multi-sensing data, and the accuracy of detection of the concrete pouring homogeneity is improved.

The invention discloses primers, a kit and a method for identifying whether a sugarcane material contains a saccharum arundinaceum chromosome 7 or not. According to the method, whether the sugarcane material contains the saccharum arundinaceum chromosome 7 or not can be stably detected by utilizing the saccharum arundinaceum specific sequence PCR primer TaChr07-F / R (the sequence is shown as SEQ ID NO.1-2) through a conventional common PCR reaction program, and the method has the advantages of high efficiency, good stability, low cost, simplicity and convenience in operation and good generalization performance, and can be applied to improvement of sugarcane germplasm resources or sugarcane breeding.

The invention relates to the technical field of intelligent detection of building materials, in particular to a real-time detection method for grain composition characteristics of muck. The method comprises the following steps: S1, acquiring original point cloud data; s2, particle point cloud clustering; s3, determining the particle size of the sieved particles; s4, particle morphological feature extraction; s5, correcting the mass of the particles; and S6, grading feature output. The method has the characteristics of high precision, high efficiency, low cost, wide applicability and the like in particle grading characteristic detection, is particularly suitable for grading characteristic detection of residual mud particles, can also meet the real-time detection requirements of related particulate matters in other fields, such as mineral particle screening, building gravel detection, agricultural granular fertilizer grading and the like, and has a wide application prospect. Rapid and accurate detection of grain composition characteristics in different industries can be realized, and the requirements of various industrial production and engineering application are met.

The invention discloses a bad traffic monitoring method and system based on an LSTM and a clustering algorithm, belongs to the technical field of network security, and efficiently converges network traffic by using a BPF technology. The method comprises the following steps: accurately identifying fragmented traffic through an LSTM network, and capturing a complex dependency relationship by using a special memory unit structure of the LSTM network; performing session recombination by adopting an FCM clustering algorithm, restoring a complete network session, and quickly identifying malicious traffic through feature matching; and finally, timely treatment measures are taken for the confirmed malicious traffic. According to the method, rapid and accurate detection and identification of bad website traffic fragmentation behaviors are realized, and the technical problem that a traditional method is difficult to deal with is solved; through efficient caching and recombination mechanisms, the efficiency and accuracy of malicious traffic processing are improved; intelligent disposal measures are adopted, and it is ensured that malicious traffic is effectively restrained in time; the overall protection capability of network security is improved, and enterprise network resources and user data security are protected.

The invention belongs to the field of electric power and the technical field of artificial intelligence, and particularly relates to an electric power field operation safety detection method and system based on a hybrid expert model. According to the method, the visual language model and the YOLO target detection model are combined, and efficient and accurate electric power field operation safety detection is realized under the condition of few samples through a hybrid expert model architecture. In the training stage, through multi-model cooperative training, an expert model set for different scenes and targets is constructed. In the detection stage, a two-stage detection mechanism is adopted, YOLO is firstly used for rapid preliminary detection, then VLM is used for rechecking a low-confidence target, and finally detection results are integrated to ensure the accuracy of detection. The method aims to effectively cope with complex environments and potential safety hazards in electric power field operation, safety detection efficiency and accuracy are remarkably improved, and compared with a single model architecture, the method shows wider application scenes and higher detection quality in an electric power field operation scene.

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 provides an online car-hailing passenger position rapid positioning method based on voice interaction and visual perspective, and relates to the technical field of intelligent traffic, and the method comprises the following steps: arranging a sensor, a camera, a metal reflective curtain and a projector on a car; voice information of a driver is collected, and an intelligent voice wake-up model is designed; constructing a scenarized speech recognition model; detecting a pedestrian target for the panoramic image by using a sound-image multi-level cross-modal detection model, and obtaining appearance and posture feature detection information of the pedestrian target; a static feature matching method and a dynamic feature recognition model are designed, and passenger feature information matching is achieved; based on a matching result, determining position information of the passenger; acquiring a passenger direction taking the vehicle as a reference origin through position estimation in the panoramic image; according to the direction estimation result, a projector in the vehicle is controlled, the passenger information is projected on a curtain in the corresponding direction in the vehicle, and meanwhile voice information is used for giving a prompt.

The invention provides a security risk detection method and device for text graph large model generation content, and the method comprises the steps: selecting a test category according to a test parameter based on a preset test database, and matching a corresponding data set from the test database according to the test category; inputting the keywords in the data set into the tested large model in batches according to the format of forward cue words; performing content security detection on a corresponding picture or video image generated by the detected large model according to the input cue word; performing watermark identification detection on a corresponding picture or video image generated by the detected large model according to the input cue word; and analyzing to obtain a detection report according to the content security detection result and the watermark identification detection result. According to the test database, the risk of the large model system is externally assessed based on the characteristics of the input prompt word, the content annotation and the output video content, and the accuracy and effectiveness of the detection and assessment method are improved.

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 self-adaptive network security early warning system and method based on deep learning, and aims to continuously update a threat recognition model through an advanced autonomous learning mechanism to adapt to a rapidly changing network environment. The system establishes a dynamic behavior baseline by integrating multi-source data including network traffic, system logs, user behaviors and the like, and realizes real-time monitoring of network behaviors and accurate identification of abnormal behaviors; once potential threats are detected, the system can automatically trigger preset response measures, such as suspicious traffic isolation and access request blocking, so that actions can be taken quickly before damage caused by the threats; according to the design of the system, the dependence on manual intervention is reduced, the real-time performance and effectiveness of early warning are improved, and the recognition accuracy of unknown attacks is remarkably improved; through the automatic and intelligent early warning mechanism, more reliable and intelligent protection is provided for network space security, and high efficiency and accuracy of network security management are ensured.

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 discloses an industrial internet security situation assessment system based on deep learning, and relates to the technical field of industrial internet security, which comprises a data acquisition module, a data preprocessing module, a feature extraction module, a deep learning model training module and a data storage module, the system has the advantages that the deep learning model training module adopts a combined architecture and an optimization algorithm to train an accurate model, the real-time monitoring module monitors real-time data in the industrial internet according to received data, abnormal conditions are found in time, and the real-time monitoring module monitors the real-time data in the industrial internet according to the real-time data. The method comprises the following steps of: acquiring the security situation value data of the current industrial internet, transmitting the security situation value data of the current industrial internet to a situation awareness module, and performing deep quantitative evaluation and trend prediction by the situation awareness module by using a fuzzy logic and evidence theory fusion algorithm and integrating historical and real-time data to accurately calculate a comprehensive security situation value so as to calculate a more accurate comprehensive security situation value. And accurate judgment and timely and effective response to the security situation are ensured.

The invention relates to the technical field of optical sensing, in particular to a copper-carbon quantum dot nano-enzyme and a preparation method and application thereof. Poria cocos and Cu < 2 + > are taken as raw materials, and the copper-carbon quantum dots with excellent peroxidase activity are prepared by adopting a one-pot hydrothermal method and can be used for sensitively, accurately and selectively detecting glyphosate. Cu < 2 + > is combined with oxygen-rich and nitrogen-rich components in poria cocos to generate an oxidation-reduction reaction, CuCDs with Cu < 2 + > / Cu < + > are obtained, Cu < 2 + > doping changes electron cloud distribution of the CDs, the electron transmission capability of the CuCDs is enhanced, and Cu < 2 + > and the CDs have a synergistic effect to generate three ROS (. OH,. O2 <-> and < 1 > O2), so that excellent peroxidase activity is exerted. The glyphosate is chelated with hydroxyl, amino, carbonyl, carboxyl and Cu on the surface of the CuCDs to occupy active sites, so that the active sites are poisoned, the peroxidase activity of the CuCDs is selectively inhibited, and a powerful choice is provided for simply, quickly, sensitively and selectively detecting the glyphosate.

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 a high-robustness target loss detection and rapid tracking method, and belongs to the technical field of calculation, reckoning or counting. By combining the YOLO algorithm and the MOSSE algorithm, the method can actively search the target and realize re-detection and rapid recovery tracking of the target after the space target is lost for a long time. And when the target moves out of the visual field, the MOSSE algorithm cannot continue tracking, and at the moment, the YOLO algorithm is automatically started to carry out target detection so as to confirm that the target appears again. Once a target is detected, the features of the target are immediately transmitted to the MOSSE algorithm so as to quickly switch back to the tracking mode, thereby ensuring the continuity and stability of tracking. The key point of the method is that a switching mechanism can quickly respond between target loss and reoccurrence, and time delay in the detection and tracking process is remarkably reduced.

The invention provides a soil nutrient detection method based on a gamma-ray energy spectrum and an XGboost algorithm, and the method comprises the steps: constructing a training sample for soil nutrient detection, the training sample comprises radioactive element data, and the label of the training sample is soil nutrient data with the same soil point location as the radioactive element data; the training sample is input into an XGboost model, the XGboost model extracts features of the training sample and predicts soil nutrients of the training sample, the soil nutrients serve as nutrient predicted values, a loss function is constructed according to the nutrient predicted values and labels of the training sample, the XGboost model is trained, and a soil nutrient detection model is obtained; acquiring soil radioactive elements of to-be-detected soil through a gamma-ray energy spectrometer, and inputting the soil radioactive elements into the soil nutrient detection model to obtain a nutrient detection result of the to-be-detected soil.

The invention relates to the technical field of data processing, in particular to an abnormal behavior detection method and device, equipment and a storage medium, and the method comprises the steps: obtaining historical operation data, and carrying out the preprocessing and division processing, and obtaining first and second preprocessed historical data; performing clustering analysis on the first preprocessed historical data by adopting a clustering algorithm, training based on a clustering analysis result to obtain a normal behavior baseline model, and inputting the second preprocessed historical data into the normal behavior baseline model to obtain normal behavior baseline information; training by adopting the normal behavior baseline information to obtain an abnormal behavior recognition model; real-time operation data is acquired and preprocessed, the preprocessed real-time operation data is input into the abnormal behavior recognition model to obtain an abnormal recognition result, and whether a response mechanism is triggered or not is judged according to the abnormal recognition result; according to the method disclosed by the invention, the abnormal behavior can be quickly identified, and the corresponding automatic response measure can be executed, so that the potential threats can be quickly detected and coped with.

The invention provides a vehicle equipment consumption prediction method and device, a model training method and device, a computer readable storage medium and electronic equipment, and relates to the technical field of vehicle equipment maintenance prediction, and the method comprises the steps: obtaining the vehicle equipment time sequence data of to-be-predicted vehicle equipment; according to the vehicle equipment time sequence data, through a Transform-KAN model, obtaining a prediction result of vehicle equipment consumption; wherein the Transform-KAN model is obtained by training an improved Transform model on the basis of a vehicle equipment time sequence data set, and the improved Transform model is obtained by replacing an MLP layer in the Transform model with a KAN model. According to the invention, the technical effect of rapidly and effectively detecting the vehicle equipment is realized.

The invention discloses a plant disease and insect pest detection device and method based on unmanned aerial vehicle high-altitude texture imaging, and belongs to the technical field of intelligent agriculture. The plant disease and insect pest detection device comprises an unmanned aerial vehicle body, a holder, a high-altitude texture camera and a small host; the high-altitude texture camera is arranged below the holder, the holder is arranged below the small host, the small host is arranged below the unmanned aerial vehicle main body, the unmanned aerial vehicle main body is provided with a front high-definition camera, a battery and an illumination sensor, and the high-altitude texture camera is wirelessly provided with a handle and a flight remote controller through Bluetooth; according to the plant disease and insect pest detection method, high-precision recognition of disease and insect pests is achieved on the basis of high-altitude texture feature fusion in combination with an environment perception driven data enhancement module, a self-adaptive multi-scale image convolution feature extraction and fusion module and a disease and insect pest area attention fusion module. The technology effectively supports precise detection of diseases and insect pests in agricultural production, and provides a scientific basis for agricultural management and pesticide application.

The invention relates to the technical field of computer vision, and discloses a litchi fruit quality detection method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining the variety of a to-be-detected litchi, and determining a specified excitation wavelength corresponding to the variety of the to-be-detected litchi; setting the excitation wavelength of fluorescence spectrum analysis equipment as the specified excitation wavelength; fluorescence spectrum data and multispectral image data of a plurality of different surface areas of the to-be-detected litchis are obtained respectively, the fluorescence spectrum data and the multispectral image data corresponding to the same surface areas are associated, and the sum of the different surface areas covers the whole surface of the to-be-detected litchis; inputting each group of associated fluorescence spectrum data and multispectral image data of the to-be-detected litchis into a pre-trained fruit quality prediction model, and outputting a quality degradation prediction result of the to-be-detected litchis; and rapid, accurate and lossless prediction of the quality condition of the litchi fruits is realized.

The invention discloses an SNP molecular marker related to an IMF character of a Ningxiang pig and application of the SNP molecular marker. The SNP molecular marker comprises at least one of an SNP1 site, an SNP2 site, an SNP3 site, an SNP4 site, an SNP5 site and an SNP6 site. According to the scheme, the SNP molecular marker related to the IMF character is provided, Ningxiang pig breeds with different IMF contents can be efficiently and accurately identified and screened out, and the SNP molecular marker has the advantages of being rapid, accurate and low in detection cost, can be used for detection and application of different Ningxiang pig breeds and has wide application prospects. And an indispensable important tool is provided for breeding Ningxiang pig breeds with high IMF content and excellent meat quality and improving the quality of Ningxiang pork.

The invention discloses a data verification method, device and equipment and a medium, and belongs to the technical field of digital verification, and the method comprises the steps: receiving to-be-verified data information; creating a target basis matrix, and performing short ring and trap set elimination optimization on the target basis matrix; encoding the data information to be verified through a fast encoding algorithm; and performing data verification on the encoded to-be-verified data information through the optimized target basis matrix. According to the basis matrix provided by the invention, the storage space of the check matrix can be reduced, the performance of the check matrix can be improved, and meanwhile, the digital channel can be quickly checked.

The invention relates to the technical field of carbon emission accounting, and particularly discloses a carbon emission calculation method and system for railway infrastructure construction, and the method comprises the steps: firstly, calling basic data of a railway infrastructure from a background database and corresponding carbon emission which is verified to be real to construct a reference sample set; and carrying out embedded coding and dynamic semantic search on the basic data of the to-be-verified railway infrastructure uploaded by the user and each reference sample by adopting a data processing technology based on deep learning, so as to query response information based on the semantic query between the to-be-verified basic data and each reference sample. And the reasonable carbon emission amount of the railway infrastructure to be verified is intelligently estimated, and whether the total carbon emission amount of the railway infrastructure to be verified uploaded by the user is true or not is judged based on the reasonable carbon emission amount of the railway infrastructure to be verified. According to the method, rapid and accurate calculation and verification of the carbon emission in the railway infrastructure construction process can be realized, false declaration is avoided, and the authenticity and reliability of the carbon emission data are improved.

The invention discloses SYBR Green I real-time fluorescent quantitative PCR (polymerase chain reaction) detection primers and a kit for large yellow croaker pyramidal worm, and belongs to the technical field of biotechnology and diagnostic detection. A pair of specific primers designed by the invention comprises an upstream primer as shown in SEQ ID NO. 1 and a downstream primer as shown in SEQ ID NO. 2. By adopting the kit disclosed by the invention, the rapid quantitative detection of the large yellow croaker pyramidal worm can be realized, the detection specificity is strong, and the sensitivity is high. At present, a research report of a real-time fluorescent quantitative PCR detection method for quantitatively detecting the Pseudosciaena crocea pyramidal based on SYBR Green I does not exist, a basis can be provided for diagnosis of Pseudosciaena crocea pyramidal infection, an important role is played in guarantee of development of Pseudosciaena crocea breeding industry, and the method has a good application prospect.

The invention discloses a method and a system for rapidly detecting the content of aluminum in bauxite based on a near infrared spectrum, and aims to meet the cross-field requirements of geoscience, resource exploration, artificial intelligence and the like. A traditional bauxite Al2O3 detection method is high in cost, expensive in used instrument and low in efficiency, and application of the traditional bauxite Al2O3 detection method in the fields of scientific research, industry and management is limited. In order to solve the problems, the method comprises the following steps: establishing a sample database by utilizing collected near infrared spectrum data of bauxite and Al2O3 standard values corresponding to samples; according to the method, a deep learning model based on DenseNet and Roformer is constructed and trained, so that the deep learning model is suitable for capturing a nonlinear relationship between a bauxite spectrum and the Al2O3 content of the bauxite spectrum, and rapid detection and analysis of Al2O3 of the bauxite can be simply and efficiently realized.

The invention discloses an image recognition method and system for contact corrosion, and the method comprises the following operation steps: obtaining a to-be-detected contact image, and carrying out the preprocessing of the to-be-detected contact image, and obtaining a contact gray image; acquiring a defect area of the contact gray level image to obtain a plurality of suspected areas; clustering is carried out according to the brightness values of the pixel points in the suspected areas, and a rusted area is obtained; calculating a surface fading index according to the brightness value of each pixel point in the suspected area, and calculating a corrosion comprehensive characteristic index of the corrosion area according to the surface fading index; and judging whether corrosion exists in the corrosion area or not through the corrosion comprehensive characteristic index, and obtaining a final recognition result of the to-be-detected contact image.

The invention relates to the technical field of food quality management, in particular to a rapid detection method and kit for sulfur dioxide in preserved fruits based on a colloidal gold immunochromatography technology. According to the principle that malachite green MG can be rapidly detected by a malachite green colloidal gold immunochromatography detection card, sulfur dioxide or sulfite can be subjected to addition reaction with MG, and a product cannot be detected by the malachite green colloidal gold immunochromatography detection card, a reverse relationship between the content of sulfur dioxide in a sample and the residual amount of malachite green is obtained; mG is added into a preserved fruit sample extracting solution, after reaction, the malachite green colloidal gold immunochromatography detection card is used for detecting residual MG in a to-be-detected solution, so that sulfur dioxide in a preserved fruit sample is rapidly detected by using an immunochromatography colloidal gold detection method, and the kit disclosed by the invention is obtained based on the immunochromatography colloidal gold detection method. The method solves the technical problems that a colorimetric method is easily influenced by the clarification and transparency degree of sample liquid and background color, the sample treatment speed is low and the like.