832results about How to "Improve classification accuracy" patented technology

The invention relates to the technical field of geological risk monitoring and early warning in tunnel construction, in particular to a karst tunnel risk intelligent early warning method, device and equipment based on deep learning, and the method comprises the steps: obtaining multi-source coupling data; performing high-dimensional fusion on the processed multi-source coupling data by using a generative adversarial network, and reconstructing a high-precision three-dimensional model of the detection area; performing geological risk grade classification on the detection area to generate a corresponding risk distribution map; generating a dynamic risk evaluation index, and triggering an early warning signal when the risk level exceeds a preset threshold value; the method comprises the steps of multi-source data acquisition, data preprocessing, high-precision three-dimensional model reconstruction, risk grade classification, dynamic risk monitoring and early warning and the like, and can solve the problems of single risk detection means, insufficient evaluation precision and poor real-time performance in existing karst tunnel construction.

The invention discloses an industrial product quality detection method and system based on machine vision, and relates to the technical field of quality detection.During operation of the system, through an industrial camera and an optimized light source layout, high-resolution image acquisition is performed on electronic components in real time by adopting multi-angle shooting, a series of optimization processing is performed on the acquired images, and the quality of the electronic components is detected. The method comprises the following steps: extracting shape, texture and color features according to geometric characteristics of an electronic component to form key parameters for judging defects, identifying potential defects of the electronic component by using a deep learning model algorithm, calculating to obtain a quality evaluation index Q, and calculating the quality evaluation index Q according to calculation results of feature extraction and defect detection. And comprehensively evaluating the quality of each component, comparing the quality evaluation index Q with a first preset threshold value M and a second preset threshold value N to obtain a quality evaluation grade, feeding unqualified detection information back to a production line, and removing unqualified components.

The invention provides a multi-modal large model construction and operation method and system for reservoir dam safety. The method comprises the following steps: constructing a training set, wherein the training set comprises a single sample; multi-modal data as model input: sensor data arranged on the dam, historical hidden danger records, engineering technology documents formed in various stages of planning, design, construction and operation of the dam, information resources related to dam safety, geometric and material information of the BIM model, timestamps and spatial position information; the probability value of occurrence of the hidden danger event serving as the training label is obtained; the training set is adopted to train a multi-mode pre-training model based on a Transform framework; performing real-time prediction and early warning on potential safety hazards of the dam; and calculating the actual influence degree, the comprehensive risk score, the dynamically set early warning threshold and the emergency measure suggestion of the hidden danger event according to the probability value of whether the hidden danger event occurs or not output by the model. The scientificity and effectiveness of dam safety monitoring are comprehensively improved.

The invention discloses a microblog emotion analysis method based on standard dictionaries and semantic rules. The microblog emotion analysis method comprises the following steps: collecting microblog data and manually labeling and marking the emotion value of each microblog; proposing corresponding standard micrblog emotion dictionaries, and establishing an emotion dictionary database; based on the standard emotion dictionaries, adding the semantic rules for assistance, and performing parameter adjustment and optimization on parameters of the semantic rules; based on a real dataset experiment, acquiring the final classification accuracy and precision. The technical scheme provided by the invention is adopted to well analyze the emotion tendency of each microblog user by introducing the standard emotion dictionaries, microblog expression dictionaries and the semantic rules, therefore, higher classification accuracy and precision are achieved.

According to the goaf detection method based on transient electromagnetic and seismic wave field joint inversion, data acquisition and joint inversion are carried out on the goaf through the combination of the transient electromagnetic detection system and the reflection seismic detection system, the complementary advantages of the two detection methods are played, the bottlenecks of a single method in the aspects of resolution and signal-to-noise ratio are broken through, and the goaf detection method based on transient electromagnetic and seismic wave field joint inversion is realized. Fine characterization of the structure, the physical property and the stability of the goaf is realized; a machine learning classification algorithm is introduced to analyze a goaf multi-attribute feature data set, and intelligent discrimination of the type, scale and stability of the goaf under complex geological conditions is realized; establishing a quantitative response relationship between goaf physical property parameters and goaf electrical and elastic difference characteristic parameters, constructing a goaf transient electromagnetic and seismic wave field joint forward modeling numerical model, and constructing an electromagnetic-seismic joint inversion objective function; and calculating and solving to obtain goaf resistivity, seismic wave velocity three-dimensional distribution and uncertainty quantitative evaluation results, and reducing blind areas of goaf judgment.

The invention relates to the technical field of crack identification, and discloses a crack identification analysis method and system for a building shear wall, and the method comprises the steps: carrying out the multi-angle shooting and image standardization processing of the surface of the building shear wall, and obtaining a standardized image data matrix; performing multi-model degradation processing and multi-angle image combination to obtain target enhanced image data; inputting the image into a convolution denoising auto-encoder for noise separation to obtain a denoising feature image matrix; inputting a dynamic graph convolutional neural network to carry out local feature and global feature extraction to obtain a local crack feature matrix and a global crack feature matrix, and carrying out feature fusion to obtain a fused feature matrix; and inputting the twin metric learning network to carry out feature measurement and crack classification, and outputting a target crack classification result, thereby realizing effective capture of deep semantic information between crack areas of the building shear wall, and realizing efficient integration of crack features of different scales.

The invention relates to the technical field of small sample image classification, in particular to a multi-modal small sample image classification method and system based on multi-scale dynamic feature fusion. The method comprises the following steps: acquiring a small sample image data set, and dividing the small sample image data set into a support set and a query set; and inputting the two features into a feature extraction network to extract global features, and inputting the features into a regional proposal network (RPN) to obtain corresponding local features. Extracting semantic information according to the category label of the support set data, and inputting the semantic information into a text encoder to obtain text features; inputting the global feature, the local feature and the text feature of the input image into an adaptive weight network, and performing dynamic feature fusion to effectively integrate multi-modal information; and inputting the fused multi-modal features into a classification model which takes a prototype network as a learning device and takes an MAML algorithm as a meta-learning device for classification prediction, finally training a network model by using a composite loss function, and performing image classification by using the trained classifier.

The invention discloses a passive unsupervised domain adaptive medical image segmentation method and device based on pseudo labels, and relates to the technical field of computers, and the method comprises a supervised source domain model training stage, an unsupervised adaptive target domain model training stage and a segmentation stage. The method specifically comprises the following steps of: performing pixel-level self-marking on a source domain medical image by applying pseudo-tag pixel-level uncertainty estimation to generate a reliable pseudo-tag; and on this basis, a strong and weak enhanced teacher-student target domain model is used, and a high-quality pseudo tag is fully utilized to train a target domain medical image, so that the network adapts to representation specific to the target domain medical image. According to the method, unsupervised segmentation is carried out on the target domain data graph on the premise that the source domain model and the target domain unmarked data are known, source domain data do not need to be accessed, the features of the source domain model can be reasonably utilized, and the problem existing in SFUDA can be well relieved; and the accurate segmentation capability on the target domain is still maintained under the condition that less data is available compared with the UDA method.

The invention discloses an automatic fine ore sorting method and system based on XRT (X ray tomography) and hyperspectrum, which are used for realizing comprehensive fusion of ore surface and internal information by combining X-ray tomography and hyperspectral imaging technologies so as to improve the recognition precision and the sorting efficiency. XRT is used for extracting the internal structure and density characteristics of the ore, hyperspectral imaging is used for identifying the mineral composition characteristics of the ore surface, and information complementation is achieved. The system adopts a multi-modal convolutional neural network to perform independent feature extraction on XRT and hyperspectral data, and the extracted features are fused through a deep residual network. And the fused features are processed by a cross-modal attention mechanism to ensure that key features of different modals are fully integrated and optimized, and finally high-precision ore classification and sorting are realized. According to the method, the limitation of a single data source on feature extraction is overcome, the ore recognition and sorting precision is greatly improved, and the refined and intelligent development of ore sorting is promoted.

The invention discloses a satellite remote sensing image cloud classification method based on space-time coding guidance, and particularly relates to the technical field of satellite image processing and deep learning, and the method comprises the following steps: carrying out the preprocessing of the original data of a geostationary meteorological satellite and a Cloud Sat satellite; preprocessing operation such as cutting, projection conversion and feature selection is carried out on the geostationary meteorological satellite data, a top cloud type is selected for the Cloud Sat data to serve as a label, and space-time matching is carried out on the processed geostationary meteorological satellite data and the Cloud Sat data to obtain a training and testing data set; and establishing a GTCCT model, performing time and geocoding on the input data, inputting the coded feature data into a network for training, and finally obtaining the GTCCT model for cloud classification.

The invention belongs to the technical field of intelligent transportation, and discloses a driving state recognition method and system based on multi-modal fusion. According to the method, the wrist movement information of the driver is collected through the non-invasive physiological bracelet, the wrist movement information is combined with traditional physiological signals, driving performance data and the like, and the accuracy and robustness of driving state recognition are improved. The invention provides a feature dynamic weighting model, the model captures a long-term dependency relationship in a time sequence by using an LSTM module, and weights the features among different modes by using a cross attention mechanism module, so that the dynamic importance of each feature can be adjusted according to different driving states, and the accuracy of driving is improved. And deep interaction and feature enhancement of different modal data are realized by using a Transform module. According to the method, the XGBoost model is utilized, driving state classification is carried out based on the weighted multi-modal features, and different driving states can be accurately distinguished.

The invention provides a multi-mode fusion gas station leakage-fire early warning method and system, and belongs to the field of gas system leakage and fire monitoring. The method comprises the following steps: firstly, acquiring infrared image data, pressure and gas concentration time sequence data of a gas station pipeline; secondly, the collected data are preprocessed; thirdly, an abnormal heat source image data set is manufactured, and a yov5 abnormal heat source monitoring model is constructed and trained and used for recognizing an abnormal heat source and calculating the fire symptom abnormal probability; and then, merging the fire symptom anomaly probability with a sensor data set, making a leakage-fire data set, and establishing a multi-mode gas station leakage-fire anomaly identification model for training and tuning. And finally, the trained multi-mode model is used for carrying out leakage-fire abnormity monitoring on the gas station, and corresponding early warning information is sent out according to an output result. According to the invention, gas leakage-fire joint abnormity discrimination can be realized, and the safety and early warning accuracy of the gas station are improved.

The invention discloses a short text clustering method and device based on a large language model, and belongs to the technical field of natural language processing, and the method comprises the steps: converting a short text into vector representation, and carrying out the preliminary clustering of the vector representation through a clustering algorithm, and obtaining an initial category; for all the corresponding short texts in each initial category, generating an abstract by using a large language model, generating a plurality of theme titles for summarizing the core meanings of the texts according to the abstract, respectively taking the theme titles as sub-titles, inputting the plurality of sub-titles into the large language model, and carrying out theme combination to obtain a parent title, constructing the parent title and the child titles into a hierarchical theme system; the large language model allocates to-be-classified short texts to one or more categories according to the hierarchical topic system to complete short text clustering; and performing quality evaluation and error pattern analysis on a short text clustering result to optimize a clustering algorithm and a large language model. According to the method, the theme of the short text can be automatically found and classified, and the classification accuracy and efficiency are improved.

The invention discloses an image classification method based on an extensible quantum convolutional neural network, and relates to the technical field of image classification, and the method comprises the steps: carrying out the preprocessing of to-be-classified image data, and then carrying out the slicing processing; converting the slice data into quantum state information through angle coding; a quantum system is constructed, the quantum system comprises a quantum feature extraction part and a quantum classification measurement part, the quantum feature extraction part comprises a quantum variational circuit model with a quantum convolutional layer, and the model can perform feature extraction on quantum state information by using alternating single quantum bit gates and double quantum bit gates; training a quantum variational circuit model by using a part of quantum state information; and inputting the other part of quantum state information into the trained quantum variational circuit model, and measuring the output quantum state of the quantum variational circuit model. According to the method, the problems that traditional deep learning is high in computing resource demand, limited in quantum computer scale and the like are solved, and a novel and practical method is provided for efficient image classification.

The invention provides a relation extraction model construction method and system based on semantic enhancement and knowledge distillation, relates to the technical field of relation extraction, and aims to solve the problem that in the prior art, when continuous learning is processed, a model is often difficult to keep memorizing an old relation when a new relation is learned, and forgetting is caused. Model overfitting is easily caused by excessive dependence on a small amount of data, so that a new relationship cannot be effectively generalized. The method comprises the steps of obtaining a relationship prototype of a training sample and a typical sample, performing semantic enhancement on the relationship prototype to obtain an expanded enhanced data set and an enhanced memory set, and performing feature extraction, relationship classification prediction and relationship extraction based on a student model and a multi-teacher model. According to the method, the overfitting problem caused by scarcity of small sample data is effectively relieved through a semantic enhancement technology, old relation forgetting caused by new relation learning in the continuous learning process is reduced through a multi-teacher knowledge distillation method, and the classification accuracy and generalization ability of the model facing different relations are improved.

The invention discloses a spatial-spectral combined hyperspectral image classification method based on a multi-scale adaptive attention mechanism, and the method comprises the steps: firstly carrying out the extraction and fusion of features of different scales through a multi-scale feature fusion module, and comprehensively capturing the detailed spectral information of a hyperspectral image; secondly, dynamically adjusting a feature extraction process by using an efficient self-adaptive spectrum-space feature extraction module, and optimizing fusion and representation of spectrum-space features; and finally, enhancing the ability of the model to understand the context relationship in the image by using the context-aware fusion network. The three modules provided by the method are based on a deep learning framework, a convolutional neural network in deep learning can utilize multiband rich spectral information, and the method is suitable for hyperspectral remote sensing image data sets with different spatial resolutions, including a foreign hyperspectral data set and a domestic hyperspectral data set. The method has high adaptability and transportability, and can meet the requirement of image classification more easily.

The invention belongs to the technical field of computer vision and artificial intelligence, and discloses a combined zero sample image classification method based on hierarchical feature fusion. According to the method, a solution based on hierarchical feature fusion is innovatively provided for the technical difficulty of combined zero sample image classification. According to the method, features of different depths in a CLIP model visual encoder are selected and subjected to hierarchical fusion, multi-level and multi-scale visual information is effectively extracted, the fused features are further used for visual and semantic cross-modal interaction, and the understanding ability of the model for complex combinations is remarkably improved. In addition, by introducing a specific loss item, attributes and object features are effectively separated, and the feature aliasing problem is avoided. In a combined zero sample image classification task, the method shows excellent generalization ability and recognition effect, and can significantly improve the classification performance of the model for unknown complex combined images.

The invention relates to the technical field of data security, in particular to an unstructured video data automatic classification method, which comprises the following steps: S1, video data preprocessing: standardizing video data to a uniform format and size, and carrying out noise processing; s2, video data feature extraction and multi-mode feature fusion: feature extraction is performed on the preprocessed video data from three modes of audio, video and text, time sequence processing is performed, and multi-mode feature fusion is performed; s3, video data same-category matching: classifying the fused multi-modal features to complete video classification; according to the invention, multi-modal information in a video can be captured more accurately, and automatic processing of video data is realized; high processing efficiency and classification accuracy are kept in large-scale data, meanwhile, the degree of automation is improved, the necessity of manual participation is reduced, and the overall process is optimized.

The invention discloses an improved YOLOv8 fish image recognition method based on transfer learning, and the method comprises the following steps: collecting a fish image data set, and carrying out the preprocessing of the data set; correspondingly marking category names on the fish image data set according to fish categories, and dividing the fish image data set into a training set, a verification set and a test set in proportion; a standard YOLOv8 model is improved; all fish images in the ImageNet data set are adopted to pre-train the improved YOLOv8 model, and obtained weight parameters are migrated to the model; performing optimization parameter training on the improved YOLOv8 model after transfer learning by using the training set and the verification set; and performing target detection on the test set through the optimized improved YOLOv8 model, and outputting a fish identification result. According to the method, the feature extraction and recognition precision is effectively improved, the computing resource demand is reduced, and the robustness and adaptability of the model are enhanced; according to the method, the learning efficiency and the recognition precision of the model are remarkably improved, data dependence is reduced, and the model has high generalization ability.

The invention discloses a full life cycle quality monitoring system, and relates to the technical field of quality monitoring, the system comprises a network modeling module, a life cycle management module and a display feedback module, and the life cycle management module comprises a process monitoring unit, a quality inspection determination unit and a tracing unit; according to the technical key points, a full-life-cycle monitoring platform is established, a block chain network is established through a production node, a quality inspection node and a buffer node by combining a block chain technology and a monitoring knowledge base, corresponding calling interfaces are provided among the nodes, and a judgment function and an evaluation function in a contract are determined based on a process to execute quality evaluation analysis. Designing a first classification model and a second classification model to accurately classify feature analysis of each process, and judging whether the production line executes a defect management mechanism or not based on a quality inspection planning contract; seamless connection of data among the processes is realized through a plurality of intelligent contracts, so that monitoring and tracing of information of the subsequent processes are facilitated.

The invention discloses an emotion recognition method based on a multi-domain attention fusion network, and the method comprises the steps: firstly obtaining an EEG signal, carrying out the preprocessing of the EEG signal, carrying out the channel feature extraction through a space-frequency domain mixed channel attention module, and capturing a weighted attention feature; secondly, weighting attention features, and extracting space and frequency domain features of EEG signals through a CNN module; and combining the improved multi-dimensional self-attention with the BiSRU, and capturing comprehensive features of space and frequency domain features. And finally, classifying the comprehensive features through a Softmax function, and outputting an emotion recognition result. According to the method, the capability of capturing the long-term dependency relationship of the electroencephalogram signals is enhanced, and the emotion recognition performance and efficiency are effectively improved.

The invention relates to the technical field of nondestructive testing, and particularly discloses a detection and positioning method and system for foaming defects of a sandwich foaming board, and the detection and positioning method comprises the following steps: S1, equipment preparation and environment control; s2, data acquisition: comprehensively scanning the sandwich panel by using an infrared thermal imager, and recording temperature distribution data; s3, performing normalization processing on the infrared thermal imaging and electromagnetic induction data, extracting key features such as temperature gradient from the infrared thermal imaging data, and extracting key features such as electromagnetic induction intensity change from the electromagnetic induction data; s4, establishing a sandwich foam board defect identification and classification model, and training a neural network model by using the data of the sandwich foam board marked with defects; and S5, inputting the preprocessed data into the trained sandwich foam board defect identification and classification model. The advanced thermal infrared imager is combined with the electromagnetic induction technology, and the deep learning model is used for intelligent identification and classification, so that the detection precision is improved, and good adaptability and real-time performance are achieved.

The invention relates to the technical field of data management, in particular to an artificial intelligence-based automatic data grading management method, which comprises the following steps of: acquiring to-be-graded data through a multi-source data acquisition channel, and performing data preprocessing on the to-be-graded data to generate a training data set; constructing a feature extraction network based on an improved Transform architecture, extracting data features in the training data set in combination with a cross-modal mechanism, and generating a dynamic feature mapping matrix; and constructing a time-space domain fusion model based on a graph neural network by using the dynamic feature mapping matrix, calculating a data sensitivity index, and performing adaptive classification. According to the method, complex cross-modal data can be processed, time-space domain features are fused, and efficient hierarchical management is realized through an improved deep learning model, so that the problems of data diversity, real-time performance and accuracy of an existing data hierarchical method are effectively solved.

The invention discloses an AI auxiliary screening method based on a mammary gland molybdenum target image, and relates to the technical field of image data identification processing, and the method comprises the following steps: S1, image collection: collecting mammary gland molybdenum target image data of a patient, carrying out the standardization processing, removing the noise in the image, standardizing the gray value, and enhancing the local contrast, and obtaining a structured image data set. According to the AI auxiliary screening method based on the mammary gland molybdenum target image, multiple machine learning algorithms are fused for focus classification, so that the classification accuracy is improved; multiple classification models are constructed in an integrated learning mode, prediction results are fused through a weighted average mechanism and a voting mechanism, model prediction confidence is calculated for weighted fusion, the advantages of all the models are brought into full play, and the limitation of a single model is reduced; the screening accuracy and efficiency of the mammary gland molybdenum target image are improved, the focus detection and classification difficulty is reduced, and the condition that the model has more false negative or false positive results is improved.

The invention discloses a data set classification method and system based on unbalanced incremental learning, and the method comprises the steps: obtaining an original data set, and carrying out the preprocessing of the original data set, and obtaining a training set and a test set; determining an unbalanced incremental learning model, and performing model training and model optimization on the unbalanced incremental learning model according to the training set and the test set to obtain a data set classification model; and obtaining a current to-be-processed data set, inputting the current to-be-processed data set into the data set classification model, and outputting a data set classification result. According to the method, the non-equilibrium incremental learning model is trained and optimized to obtain the data set classification model, and the classification efficiency and accuracy of the data set can be effectively improved through the data set classification model.

The invention provides a light guide plate surface defect detection method based on a GCC-YOLOv8 network model. The method comprises the following steps: acquiring an image of a light guide plate to be detected; the image is input into a GCC-YOLOv8 network model for defect detection; outputting surface defect information of the light guide plate based on a detection result; wherein the GCC-YOLOv8 network model is constructed based on a YOLOv8 network model, and comprises a backbone network unit, a neck network unit and a detection head network unit with three parallel detection branches, which are arranged in sequence; two GKA-C2f1 modules are arranged at the tail end of the backbone network unit; the neck network unit comprises an FPN network unit and a PAN network unit; the FPN network unit comprises two up-sampling modules, two C2f2 modules, two Concat modules, an SPPF module and a CPCA module; the PAN network unit comprises two CBS modules, two Concat modules, two C2f2 modules, three CPCA modules, a CFC module and an SFC module. The light guide plate surface defect detection method has good light guide plate surface defect detection performance and robustness, and also has good detection speed.

The invention belongs to the field of medical ICD automatic coding, and particularly relates to a medical ICD coding classification method based on a graph neural network, and the method comprises the steps: obtaining a data set, and carrying out the preprocessing of the data set, and obtaining a text vector matrix; constructing a graph structure according to the text vector matrix, wherein the graph structure comprises a node feature matrix and an adjacent matrix; inputting the graph structure into a graph neural network model, outputting a prediction ICD coding result, and training the model by adopting a multi-label loss function based on cross entropy; inputting to-be-coded medical record data into the trained graph neural network model to obtain a predicted ICD code; according to the method, the semantic similarity between the medical terms, the level distance between the ICD codes and the co-occurrence frequency relation are included into a graph structure, the complex relation between words and labels can be comprehensively captured, the potential relevance between the ICD codes is better utilized, and the context understanding ability and accuracy of a classification model are enhanced.

The invention relates to a construction method of an unmanned aerial vehicle remote sensing image classification model. Performing structured representation of the remote sensing image based on hierarchical feature decomposition; the method comprises the following steps: dividing an unmanned aerial vehicle image into image blocks of different levels through a pyramid decomposition algorithm, performing hierarchical combination for features of each level, and forming a multi-scale structured feature vector through a feature weighting mechanism; capturing dynamic features fused with a spatial topological relation: after a graph structure is constructed, performing feature aggregation and spatial relation information propagation on each node by using a graph convolutional network GCN, so that the features of each node comprise own information and the influence of fusing surrounding ground features; for multi-tense image data, a dynamic graph convolutional network D-GCN is introduced, modeling is carried out on spatial relations at different moments, and changes of ground objects along with time and influences of the ground objects on classification are captured; self-supervised clustering learning of ground feature features: for ground features which are difficult to distinguish, a soft clustering method is introduced, so that the ground features belong to a plurality of categories at the same time;

The invention discloses a motor imagery electroencephalogram recognition method based on a domain adaptation network, and the method comprises the steps: collecting multi-channel motor imagery electroencephalogram signals EEG of a subject, carrying out the band-pass filtering, obtaining a plurality of EEG signals of different frequency bands, taking the EEG signals of the target subject as target domain data, and taking the rest as source domain data. Secondly, on the basis of the source domain data and the target domain data, source domain features and target domain features are obtained through a multi-view convolution Transform feature extractor composed of a parallel space-time convolution module and a Transform module in sequence; and finally, performing adversarial domain adaptation by using the source domain features and the target domain features to obtain a model capable of adapting to target domain data, and identifying a motor imagery category corresponding to the electroencephalogram signal. According to the method, source domain data distribution can be guided to be transferred to a target domain, and the classification accuracy of cross-subject motor imagery electroencephalogram signals is improved.

The invention discloses a meat product foreign matter on-line detection method and system, and belongs to the field of visual detection.The detection method comprises the steps that a fixed detection area is set, a multi-mode sensor is installed above the fixed detection area, and the multi-mode sensor collects data and transmits the data to a data processing center through a wireless network; the data processing center decompresses and preprocesses the received data and fuses the image data twice to obtain a final fused image; according to the feature information in the final fusion, extracting fusion feature data; analyzing the feature data by using a support vector machine, and judging whether foreign matters exist or not and classifying the types of the foreign matters; a detection result is fed back to a production line control system in real time, and meat products containing foreign matters are positioned and removed. According to the method, the multi-mode sensor and an efficient data analysis technology are combined, and the accuracy and the real-time performance of meat product foreign matter detection are effectively improved.