50results about How to "Fully portrayed" patented technology

The invention discloses a concrete bridge crack detection method based on computer vision, and relates to bridge detection, and the method comprises the steps: collecting a crack image, GPS data and a depth image of a concrete bridge; according to the GPS data, establishing a mapping relation between the pixel coordinates and the fracture physical space coordinates; based on feature matching of ORB features and a registration optimization algorithm based on mutual information, obtaining alignment of the crack image and the depth image at a pixel level, and obtaining a color crack image and a depth crack image; a MaskR-CNN model is adopted to extract a pixel-level crack segmentation mask of the crack region; according to the crack segmentation mask and the registered depth crack image, depth information of a crack region is estimated through a depth completion algorithm based on a conditional generative adversarial network (CGAN), and a crack depth distribution map is obtained; according to the crack depth distribution map, geometric parameters of the crack are extracted through a morphological algorithm and a central axis transformation algorithm. Aiming at the low concrete bridge crack detection precision in the prior art, the detection precision is improved.

The invention provides a life detection method and system based on radar signal processing, and relates to the technical field of radar signal processing, and the method comprises the steps: collecting a radar echo signal of a target region, controlling a phased array radar transmitting antenna array to transmit an orthogonal frequency division multiplexing dual-frequency continuous wave radar signal, and transmitting the dual-frequency continuous wave radar signal. Inputting the processed radar echo signal into an orthogonal demodulation module for synchronous coherent demodulation to obtain a baseband signal; inputting the feature vector into a multi-task deep learning network integrated with an attention mechanism for feature fusion and target recognition to obtain a second processing signal; obtaining a target characteristic frequency spectrum by calculating the time-frequency distribution of the second processing signal, and judging whether a life body exists in the target area or not according to the time-varying characteristics of the respiratory frequency component and the heartbeat frequency component and the amplitude fluctuation rule; and calculating three-dimensional space coordinates of the life body through a multi-base-station cooperative positioning algorithm, and outputting a detection result containing the three-dimensional space coordinates and the life body feature information.

The invention provides a brain parenchyma MRI image-based infarction change prediction method and system, and relates to the technical field of image recognition, and the method comprises the steps: obtaining a brain parenchyma MRI image, constructing a multi-modal image set, carrying out the screening according to a screening standard, carrying out the artifact recognition, calculating a quality score, carrying out the preprocessing, obtaining standard image data, carrying out the feature extraction, and carrying out the prediction of the infarction change of the brain parenchyma MRI image. Obtaining a brain region segmentation mask; performing feature extraction to obtain multi-modal feature data, constructing a topological relation among different brain regions, extracting high-order semantic features, obtaining brain region feature data, and performing selection to obtain high-quality feature data; constructing a time sequence characteristic matrix, distributing weights, generating residual output, performing prediction through a full-connection neural network, generating an infarction change prediction result, generating a prediction prognosis result, and generating a comprehensive prediction report.

The invention discloses an automatic processing method and device and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring an invoice image; performing feature extraction on the invoice image to obtain layout features corresponding to the invoice image; matching a corresponding target invoice template according to the layout characteristics; determining region segmentation parameters for the invoice image, and performing key information extraction on the invoice image according to the region segmentation parameters to obtain a plurality of key information regions; performing OCR (Optical Character Recognition) on each key information area to obtain an invoice information text corresponding to each key information area; if it is determined that the invoice information text is correct, key field extraction is conducted on the invoice information text, invoice keywords are obtained, and structured invoice information data are constructed according to the invoice keywords; and associating the invoice image with the invoice information data, and generating an invoice identification code. By implementing the technical scheme provided by the invention, the workload of manual input and verification is reduced, and the efficiency is improved.

The invention relates to the technical field of meteorological cloud detection, and discloses a satellite-borne hyperspectral cloud layer identification method based on a dual-band fusion technology, and the method comprises the steps: firstly obtaining a hyperspectral image, carrying out the preprocessing of the hyperspectral image, and obtaining a spectrum calibration image; respectively calculating the visible light wave band transmittance and the near-infrared wave band transmittance of each pixel in the spectrum calibration image; identifying a semitransparent weather phenomenon area and a non-semitransparent weather phenomenon area according to the visible light wave band transmittance and the near-infrared wave band transmittance of each pixel; and finally, constructing a real cloud layer spectral feature model, performing real cloud layer identification on the non-translucent weather phenomenon area by using the model, and fusing the identified real cloud layer area and the translucent weather phenomenon area to generate a final cloud detection result graph. According to the method, spectral information of visible light and near-infrared wavebands is fused, a real cloud layer spectral feature model is constructed for cloud detection, and the cloud detection precision under complex atmospheric conditions is improved.

The invention provides a driving training teaching method and system based on imitation learning, and relates to the technical field of intelligent driving, and the method comprises the steps: obtaining the driving behavior demonstration data of a plurality of human drivers in a plurality of driving scenes, and extracting the driving behavior characteristics in the driving behavior demonstration data; calculating a first feature expectation of an actual driving behavior trajectory of the human driver based on the driving behavior demonstration data; a reinforcement learning algorithm is adopted to optimize a driving strategy of the intelligent agent, the intelligent agent executes the driving strategy in a simulation environment to obtain a simulation driving behavior track of the intelligent agent, and a second feature expectation is calculated; alternately optimizing and updating the parameters of the implicit reward function by minimizing the difference between the first feature expectation and the second feature expectation and combining a generative adversarial network; and applying the intelligent agent driving strategy to the auxiliary driving decision of the intelligent agent.

The invention provides a strategic key mineral resource comprehensive evaluation and decision support service method and system, and the method comprises the steps: S1, carrying out the data collection and preprocessing, and constructing a digital twinborn model; s2, carrying out whole industrial chain material flow analysis; s3, estimating the waste quantity and the recovery quantity of resource products, and calculating the stock quantity; and S4, performing comprehensive evaluation and early warning according to the calculated content, and visually displaying the data. According to the invention, digital modeling and simulation of the whole process of strategic key mineral resources from exploration and exploitation to recycling are realized, a data barrier of the whole industry chain is broken through, and a system solution of the whole life cycle is provided.

The present invention provides a life detection method and system based on radar signal processing, which relates to the technical field of radar signal processing. The method and system include collecting radar echo signals in a target area, controlling a phased array radar transmitting antenna array to transmit an orthogonal frequency division multiplexed dual-frequency continuous wave radar signal, inputting the processed radar echo signal into an orthogonal demodulation module for synchronous coherent demodulation to obtain a baseband signal; inputting a feature vector into a multi-task deep learning network with an integrated attention mechanism for feature fusion and target recognition to obtain a second processed signal; obtaining a target feature spectrum by calculating the time-frequency distribution of the second processed signal, judging whether there is a life form in the target area according to the time-varying characteristics of a respiratory frequency component and a heartbeat frequency component and the law of amplitude fluctuation; if it is determined that there is a life form in the target area, calculating the three-dimensional spatial coordinates of the life form through a multi-base station collaborative positioning algorithm, and outputting a detection result containing the three-dimensional spatial coordinates and the life form feature information.

The invention provides a logistics state prediction method based on spatial-temporal characteristics. The method comprises the following steps: acquiring historical production logistics time sequence data, historical production logistics state sequence data and historical logistics states from a workshop information system; analyzing the workshop layout description file of the production workshop system to generate a workshop layout topological structure; based on the workshop layout topological structure, performing feature extraction on historical production logistics time sequence data and historical production logistics pattern sequence data to obtain historical logistics time features and historical logistics space features; the production logistics state space-time model predicts the logistics state based on the historical logistics time features and the historical logistics space features to obtain a predicted logistics state; and according to the historical logistics state and the predicted logistics state, optimizing model parameters of the production logistics state space-time model until a training ending standard is reached.

The invention discloses a petroleum proppant finished product sorting control method and system based on machine learning, and relates to the technical field of petroleum proppant production. A three-dimensional geometric model of the airflow classifier is constructed, and three-dimensional tracks of the spherical particle samples and the non-spherical particle samples are generated; for proppant particles to be graded, shape characteristic parameters of the proppant particles are obtained, a petroleum proppant particle sample with the highest shape similarity with the proppant particles is found, and the speed deviation is calculated; calculating a comprehensive deviation index based on the shape characteristic parameters and the speed deviation of the proppant particles, if the comprehensive deviation index is smaller than or equal to a comprehensive deviation index threshold value, directly grading the proppant particles, and otherwise, performing secondary treatment; the optimal correction parameters are predicted through the intelligent correction model, the proppant particles are corrected, and the corrected proppant particles are fed into an airflow classifier; an optimization objective function is constructed, and optimal process parameters are searched; and the quality control of petroleum proppant finished product sorting is realized.

The application discloses a kind of based on big language model and ability atlas's wisdom pastor authentication examination method and system, belong to artificial intelligence technical field.The application first constructs the ability atlas based on knowledge, skill, behavior three-dimensional ontology model;Obtain examinee multimodal data and generate initial ability vector;Through double-path dynamic gate graph neural network, real-time update ability vector, the network includes erase gate and increase gate and is updated by graph convolution layer propagation;According to ability short board, determine aggressive factor and combine incidental factor, use big language model to dynamically generate the next logical unique test question;Repeat interaction until the end of examination;Finally, according to all updated ability vector, generate digital ability portrait report.The application realizes dynamic individualized surveying and mapping, anti-cheating and deep characterization of talent ability structure, improves the precision and security of wisdom pastor authentication examination.

The application discloses an emotion recognition method based on online cross-modal knowledge distillation, comprising the following steps: acquiring electroencephalogram and electrocardiogram original signals and windowing and cutting; constructing electroencephalogram and electrocardiogram student models, extracting intermediate features from each modal data segment through an encoder, and obtaining non-normalized prediction output through a classifier; constructing a teacher probability distribution through a joint encoder fusion; introducing adaptive contrast loss to align the cross-modal intermediate features, introducing distillation loss to constrain the prediction probability distribution of each modal to align with the teacher probability distribution; synchronously optimizing new student model parameters through online collaborative training; and performing actual inference prediction based on the student model after training. The application combines double modal signals to make up for the defects of single modal information, excavates the complementarity of modes, realizes dynamic generation of teacher supervision signals and real-time collaborative learning of modes through online distillation, does not increase test calculation overhead, effectively improves the recognition accuracy, model robustness and generalization ability, and has good application prospect.

The invention relates to the field of intelligent traffic, deep learning and artificial intelligence, in particular to a deep learning method for urban traffic node mode similarity cognition. Traffic speed data is organized into a graph data format consisting of a feature matrix and an adjacency matrix. The processed speed data are sent into a dynamic multi-graph constructor, an output dynamic adjacency matrix and a space adjacency matrix are sent into a multi-scale graph convolution module together, and the speed data are sent into the multi-scale graph convolution module after being subjected to expansion causal convolution processing in each space-time layer; and splicing from each space-time layer through jump connection to obtain a final result. And finally, analyzing the traffic mode similarity reflected by the dynamic multi-graph. According to the method, the space-time dependent features in the intelligent traffic scene can be extracted more efficiently, and the problem that the perceptual view in the graph convolutional network is limited and the problem that the long-term traffic mode is not considered sufficiently in traffic mode similarity analysis are solved.

The invention discloses an underwater AUV (Autonomous Underwater Vehicle) service caching and switch state switching method based on a D3QN (Digital 3QN). The method comprises the following steps: firstly, for an ocean network formed by a water surface buoy node and an AUV, establishing an AUV service cache state and dormancy / activation state coupling control model; then, through consistent binding of a target switch state and a service cache write-in action, dormancy isolation constraint and a dormancy cache freezing rule, a feasible action mask meeting cache capacity constraint and anti-repeated write-in constraint is constructed; the method comprises the following steps of: firstly, constructing a comprehensive cost function according to cache update time overhead, write-in energy consumption, switch state switching cost and invalid activation penalty, and finally, solving in a legal action subspace by using D3QN, and outputting an AUV service cache and switch state switching control strategy. According to the invention, AUV service cache updating and switch state switching under the dynamic load can be effectively realized, and underwater network energy consumption and service cache updating delay are reduced.

The invention relates to an online community creator feedback prediction method and system based on a dynamic reputation graph and text analysis. Relates to the technical field of feedback prediction. The method comprises the following steps: S1, acquiring and preprocessing historical interaction data containing comment texts, user IDs, timestamps and like and treading records; s2, constructing a like and treading double-view interaction map; s3, segmenting the data according to the day, and calculating a daily positive and negative reputation value through a PageRank algorithm; s4, introducing a time decay function, and carrying out weighted summation on the daily granularity reputation value in the close time window to obtain a dynamic reputation; s5, text semantic features are extracted through RoBERTa, the text semantic features and the dynamic reputation features are spliced and fused, and the full-connection neural network is input to output the like or treading probability. The method gives consideration to positive and negative reputation and time dynamics, makes up for the defects of plain text prediction, remarkably improves the feedback prediction precision, especially optimizes the click behavior prediction effect, and can provide support for community atmosphere guidance and network violent early warning.

The invention relates to a new energy output prediction method and system based on an NHITS model, and the method comprises the steps: obtaining the input characteristics of a to-be-tested new energy object, forming new energy time series data, and enabling the new energy to comprise photovoltaic or wind power; the input characteristics corresponding to photovoltaic output prediction comprise time, solar irradiance, air temperature, air pressure, humidity and historical power data, and the input characteristics corresponding to wind power output prediction comprise time, meteorological variables and wind speeds and wind directions at different heights; new energy output prediction is carried out by adopting an NHITS prediction model, the NHITS prediction model carries out multi-time scale decomposition on a time sequence through a hierarchical recursive structure, the time sequence is divided into a plurality of modules stacked according to layers, and each module carries out partial interpretation on input new energy time sequence data under different time scales and outputs a prediction component; and superposing the prediction components of each layer to obtain a final prediction result. Compared with the prior art, the method can maintain high prediction precision and stability.

This invention relates to the field of fault early warning technology, and provides a method and system for early warning of faults in large oil-filled equipment, comprising: Step 1, collecting acoustic fingerprint data throughout the entire lifecycle of the large oil-filled equipment and constructing an acoustic fingerprint sample set; Step 2, preprocessing the acoustic fingerprint sample set, extracting acoustic fingerprint feature parameters, and establishing a standardized acoustic fingerprint database; Step 3, constructing a multi-channel deep learning acoustic fingerprint recognition model and a discharge severity assessment model based on deep learning; Step 4, based on the output of the trained acoustic fingerprint recognition model and combined with a preset multi-level early warning threshold system, performing real-time assessment of the operating status of the large oil-filled equipment, and generating corresponding early warning information when the assessment result meets the early warning triggering conditions; Step 5, pushing the early warning information to the operation and maintenance terminal, and automatically generating operation and maintenance suggestions based on the defect type and severity. This invention can effectively provide early warning of faults in large oil-filled equipment.

The invention discloses an emotion recognition method based on online cross-modal knowledge distillation. The emotion recognition method comprises the steps that electroencephalogram and electrocardio original signals are obtained and subjected to window segmentation; constructing an electroencephalogram and electrocardio student model, extracting intermediate features from each modal data segment through an encoder, and obtaining non-normalized prediction output through a classifier; teacher probability distribution is constructed through joint encoder fusion; self-adaptive comparison loss alignment cross-modal intermediate features are introduced, and distillation loss is introduced to constrain prediction probability distribution of each modal to align to teacher probability distribution; new student model parameters are optimized synchronously through online cooperative training; and performing actual reasoning prediction based on a student model after training. According to the method, bimodal signals are combined to make up single-modal information defects, modal complementarity is mined, teacher supervision signal dynamic generation and modal real-time collaborative learning are realized through online distillation, test calculation overhead is not increased, identification precision, model robustness and generalization ability are effectively improved, and the application prospect is good.

The invention provides a space optical target time sequence feature extraction method and system, and the method comprises the following steps: 1, video collection: obtaining a target video shot by an optical detection device in real time through a computer and a network; step 2, video frame sequence conversion: the collected real-time tracking video stream is segmented into image frame sequences according to a frame rate; step 3, automatically detecting and positioning the position of an interested space optical target frame by frame, and obtaining a position square frame of the target in an image frame; 4, based on the target position square frame, extracting a target effective pixel region corresponding to the target actual imaging region, and calculating the area and gray value information of the target effective pixel region; 5, calculating a minimum bounding rectangular frame of the target based on the effective pixel area of the target, and calculating the position and scale information of the rectangular frame; step 6, generating a target time sequence characteristic curve graph according to the characteristic information such as the position, the posture, the scale and the gray scale, realizing visualization of the target time sequence characteristics, and analyzing a rule of the target characteristics changing along with time; the method has the advantages of automation, comprehensive feature extraction and visualization.

The invention relates to the technical field of neural image analysis, and particularly provides a brain function connection intelligent screening method and system for autism spectrum disorders, and the method comprises the steps: firstly obtaining resting state functional magnetic resonance imaging data and phenotype information thereof, and extracting a blood oxygen level dependence value time sequence of each brain region after preprocessing; then constructing a multi-scale brain network comprising a low-order function connection matrix and at least one high-order function connection matrix; the matrix is converted into a brain function connection graph containing sub-graphs of different scales through threshold sparsification; meanwhile, phenotype embedding features are extracted from phenotype information; the graph data and the phenotypic features are input into a multi-channel neural network model for parallel processing and fusion, and joint feature representation is obtained; and finally, outputting an auxiliary diagnosis result of the autism spectrum disorder through the classifier. According to the method, by fusing the multi-scale brain function connection information and the individual phenotype features, the accuracy of autism classification diagnosis and the generalization ability of the model are effectively improved.

The invention belongs to the technical field of power distribution network operation fault diagnosis, and particularly relates to a district voltage quality digital portrait construction and low-voltage fault identification method and system.The district voltage quality digital portrait construction comprises the steps that firstly, a low-voltage fault evaluation index system is constructed, and index data are collected; then, performing multi-scale time-frequency analysis on the acquired voltage signals by adopting different signal processing algorithms, splicing and fusing output results of the different signal processing algorithms to form a fused frequency domain feature vector, and jointly using the fused frequency domain feature vector and each index data as a digital portrait for describing the voltage operation state of the transformer area; according to the transformer area voltage low-voltage fault identification method, a low-voltage fault identification model is firstly constructed, and the model realizes low-voltage fault identification based on a digital portrait. According to the method, the operation characteristics of the power distribution area can be comprehensively and visually described through the digital portraits, high-quality input is provided for follow-up fault recognition, and finally the fault recognition precision is improved.

The application provides a well-ground differential electromagnetic three-dimensional inversion method considering the effect of well casing, and relates to the geophysical logging technical field; an additional term is added to modify the average resistivity formula, so that numerical simulation containing the steel casing is realized without refining the grid, and the resistivity model containing the casing effect is inverted by the Gauss-Newton method. The resistivity model parameters containing the casing effect are inverted, the well-ground differential three-dimensional electromagnetic inversion considering the effect of well casing is realized, and the precision and accuracy of the well-ground electromagnetic inversion are further improved.

This invention relates to the field of intelligent lighting technology and discloses a method and system for predicting urban lighting demand based on a spatiotemporal graph neural network. The system includes: a multi-source data perception and processing module, a spatiotemporal graph construction and feature learning module, a meta-learning adaptive prediction module, a lighting strategy optimization and execution module, and a model online update module. By integrating multi-source heterogeneous data from IoT terminals and urban management databases to construct a unified spatiotemporal dataset, and utilizing a spatiotemporal graph neural network to simultaneously learn the complex dependencies of different nodes in the city in the temporal and spatial dimensions, it can more comprehensively and accurately depict the formation and evolution of urban lighting demand, thereby improving the accuracy and reliability of predicting future lighting demand intensity and providing a basis for energy-saving regulation. By introducing a meta-learning strategy, the prediction model can utilize historical experience to adapt to new urban areas and time periods, solving the problem of insufficient generalization ability of the model in unfamiliar scenarios.

The present invention relates to a method and system for identifying structural surfaces and flow paths induced by fluid injection. The method comprises selecting rocks in a reservoir to be injected with fluid for a rock injection experiment, obtaining corresponding acoustic emission point cloud data, acoustic emission source mechanism, and pore fluid pressure; determining identification features of structural surfaces and flow paths according to the acoustic emission point cloud data, acoustic emission source mechanism, and pore fluid pressure; and performing microseismic monitoring on the reservoir to be injected with fluid to obtain monitoring results, and identifying structural surfaces and flow paths induced by fluid injection according to the identification features of structural surfaces and flow paths based on the monitoring results. The present invention can break through the limitations of traditional methods in describing and characterizing reservoir structural surfaces, thereby more comprehensively characterizing and characterizing underground geological structures.

The present disclosure provides a push method and system based on joint prediction, the method comprising: obtaining first data of a requesting party at a first participant and second data at a second participant, wherein the first data at the first participant is different from the second data at the second participant, and the first data and the second data are not interoperable; performing a joint prediction based on the first data and the second data obtained by the shared learning combination to perform joint prediction-related push to the requesting party; and feeding back the result of the joint prediction to the first participant and the second participant.

The invention discloses a passenger transport airport service range prediction method based on multi-source data fusion, and relates to the technical field of airport service information processing, and the method comprises the steps: constructing a starting point individual attribute feature vector, an airport terminal point attribute feature vector, and constructing a spatial impedance variable; taking the starting point individual attribute features, the ending point airport attribute features and the spatial impedance variable as input, constructing and training a depth gravity model, applying the trained depth gravity model to global space grid units of a research area, calculating the probability of each grid unit for selecting each airport, and calculating the airport selection probability of each grid unit; determining an affiliation airport of each grid unit according to a probability maximum principle so as to generate continuous space service range distribution; the problem that a traditional airport service range prediction method is low in prediction precision in a multi-airport competition environment is solved.

The invention discloses a peanut biomass inversion method based on feature extraction and screening. The method comprises the following steps: S1, collecting hyperspectral image data of a sample peanut by adopting an unmanned aerial vehicle carried hyperspectral camera; s2, obtaining the actually measured biomass of the sample peanut; s3, preprocessing the collected hyperspectral image data of the sample peanut to obtain initial spectral reflectivity data; s4, performing feature extraction and screening on the initial spectral reflectivity data; and S5, building a peanut biomass inversion model through the random forest model after particle swarm optimization, a back propagation neural network and a support vector machine by using the screened spectral features, and training and testing the peanut biomass inversion model by using the actually measured biomass of the sample peanut as a data set. According to the invention, screening and feature combination of the effective features of the peanut hyperspectral image are realized, so that the accuracy and precision of the peanut biomass inversion model are improved.

The invention discloses a corn low-temperature cold damage vulnerability assessment method. The method comprises the following steps: 1, selecting indexes; step 2, constructing a cold damage index; step 3, identifying a cold injury event; 4, simulating the growth condition; 5, establishing a fragile curved surface; in the fourth step, the key physiological index loss rate is obtained by subtracting the key physiological index under the cold injury condition from the key physiological index, dividing the key physiological index under the normal condition and multiplying by 100%; according to the method, from the perspective of a soil-crop-atmosphere continuum, indexes capable of representing the low-temperature cold damage on different scales are selected, the comprehensive cold damage index is formed after weighting, the actual low-temperature cold damage condition of corn can be more accurately represented, a complete cold damage event is identified according to a run-length theory, and the method has the advantages of being high in accuracy and high in accuracy. The three-dimensional vulnerability curved surface is formed in combination with the cold damage duration, crop disaster conditions corresponding to different cold damage degrees can be represented, and cold damage events can be described more visually and comprehensively.

This invention discloses a remote sensing identification method and system for saline-alkali permafrost along railways, relating to the field of engineering construction. The method includes: acquiring various data within a pre-defined buffer zone centered on the railway line, and constructing a multi-source data cube for the railway corridor through coordinate registration, orthorectification, and spatial resampling. Subsequently, remote sensing sensitive factors such as spectral salinity index, radar scattering coefficient combination factor, annual surface temperature variation factor, InSAR linear deformation rate, and seasonal deformation amplitude are extracted, and a comprehensive index for saline-alkali permafrost is constructed and corrected based on the heat-salt coupling relationship. The comprehensive index and sensitive factors are input into a multi-task deep learning model, outputting the probability of permafrost presence, salinity level, and engineering risk level. Finally, a risk profile is constructed along the railway mileage direction, and risk zoning identification is completed, achieving visualization and refined assessment of permafrost risks along the railway line.

This invention discloses a rapid detection method and system for the reheating degree of meat dishes based on electrochemical sensing, belonging to the field of food testing technology. Addressing the problem of difficulty in rapidly and accurately evaluating the reheating degree of meat dishes in existing technologies, this invention first places the meat dish to be tested in a space, generating headspace gas at a preset equilibrium temperature and time. An electrochemical sensor unit collects the complete time-response curve of the interaction between the headspace gas and the sensitive layer, extracting dynamic characteristic parameters such as response rise time, peak response value, time to reach peak value, curve integral area, and half-life of the fall phase. Simultaneously, it acquires ingredient attribute parameters and reheating method parameters. These parameters are then input into a pre-trained deep learning prediction model, which outputs the reheating degree level and displays it on a display unit. This method can be used in the catering industry, food processing, and home cooking for rapid detection of the reheating degree of meat dishes, ensuring food quality and food safety.