324 results about "Feature evaluation" patented technology

The invention discloses an industrial odor online monitoring system based on big data. The system comprises a data acquisition module, a preprocessing module, a feature fusion module, a multi-source sensing weight analysis module, a dynamic risk assessment module, a self-adaptive correction module and a real-time feedback module. Data interaction among the modules adopts a block chain encryption transmission protocol, and a feature evaluation mapping chain with a timestamp is established in a distributed database. Through the innovative design of heterogeneous sensor network collaborative perception, cross-modal feature organic fusion, pollution diffusion three-dimensional dynamic deduction and a credible data governance architecture, a new industrial bad smell monitoring normal form with environment intelligent adaptability is constructed. The four technical effects form complementary support from four dimensions of data acquisition credibility, feature modeling scientificity, risk assessment accuracy and system decision robustness.

The invention provides a telecommunication service fraud-related risk security assessment system based on a multi-modal fusion model, and the system comprises a multi-source data collection assembly which is responsible for obtaining original telecommunication service data in multiple ways, carrying out the preprocessing of the original telecommunication service data, and obtaining first telecommunication service data; performing feature extraction and behavior pattern analysis on the first telecommunication service data to obtain key features related to the telecommunication service; the assessment model construction component is responsible for constructing a fraud-related risk assessment model based on machine learning, inputting the key features into the fraud-related risk assessment model, outputting an intelligent assessment control matrix, classifying and rating fraud-related risks, and generating a risk assessment result; and the service collaborative linkage assembly is responsible for early warning the risk level of the telecommunication service system according to the risk assessment result, and taking prevention measures according to the risk level. According to the method, an objective evaluation standard is established, and the conversion of risk identification from experience judgment to data driving is realized.

The invention relates to the technical field of robots, and discloses a robot nondestructive testing method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-modal sensing data and motion state parameters; constructing a three-dimensional material feature model and analyzing to generate an initial defect feature evaluation model; loading material attributes and detection precision constraints to generate a comprehensive model, and combining motion parameter simulation to obtain path regulation and control data; training the dynamic defect prediction model to obtain a detection path correction model and generating initial detection parameters; obtaining dynamic correction simulation information based on the initial parameters and the like; constructing a multi-objective optimization model, and optimizing to obtain an optimal detection path parameter; and generating a safety detection probability by combining real-time model reasoning, and adjusting a detection strategy to realize dynamic path matching. The system comprises a data acquisition module, a feature modeling and analysis module, a comprehensive evaluation and simulation module and the like. The method improves the detection precision, efficiency and adaptability, and is suitable for nondestructive detection of complex targets.

The invention provides an image noise mark feature selection method and system, a storage medium and a computer. The method comprises the steps of obtaining a to-be-processed image noise mark data set; embedding a sample set in the image noise mark data set into a multi-granularity fuzzy cluster to construct a dynamic fuzzy membership evaluation matrix; dynamically evolving a multi-level high-precision granular ball cluster; obtaining mark distribution with high identification degree; constructing a rough perception feature evaluation framework based on granular ball topology driving, extracting decision equivalence classes by combining rough set upper and lower approximation and extended positive domain models, and determining and measuring the contribution degree of each feature to a decision system by fusing multi-granular-ball decision boundary information based on a dependency degree quantitative model; a particle and ball structure consistency verification mechanism is introduced, and multi-level evaluation is carried out on the importance of the features through dependency and consistency. According to the method, the optimal feature subset with strong anti-noise performance and high discrimination capability is obtained, and stable and efficient input support is provided for a subsequent image noise mark learning model.

The invention provides a sound standardization evaluation system for multi-dimensional acoustic parameter and emotional scene dynamic analysis, and aims to realize structured and objective evaluation of voice performance. The system comprises an audio acquisition module, an audio processing module, a standard evaluation module and an integration processing module. The audio processing module performs noise reduction, pre-emphasis, sampling rate adjustment, framing and slicing operation on the original voice data; the standard evaluation module executes character and pronunciation accuracy analysis, acoustic basic skill feature evaluation or emotion and scene adaptation degree analysis based on multi-modal fusion according to an evaluation type set by a user; parameters including fundamental frequency, formant, sound intensity, fundamental frequency perturbation, amplitude perturbation and the like are extracted in evaluation, and classification prediction is completed in combination with a machine learning model; and finally, the integration processing module generates an evaluation report including a scoring result, question prompts and personalized training suggestions, and provides closed-loop feedback to support sound training and expression optimization.

The invention discloses an indoor air quality monitoring method and system based on multi-dimensional analysis, and belongs to the technical field of air quality monitoring. The indoor air quality monitoring method based on multi-dimensional analysis comprises the steps that various data of a chemical sensor, an optical sensor and an acoustic sensor are collected; preprocessing the data, including unifying sampling frequency and normalizing different data formats; performing multi-dimensional feature extraction to obtain a multi-modal feature; carrying out feature fusion by adopting a machine learning algorithm; the air quality state is evaluated based on the fusion features; through multi-source data fusion analysis, the problems of one-sided monitoring result and insufficient reliability of a single sensor are solved, the monitoring accuracy and reliability are improved, and the method can be widely applied to scenes such as smart homes and office buildings.

The invention provides a packing material defect detection method and system based on machine vision, and relates to the technical field of machine vision, and the method comprises the steps: collecting a multi-angle image sequence to construct a depth map, and carrying out the geometric correction and illumination compensation of an image; generating a feature map sequence by using a multi-scale direction filter bank and a self-adaptive direction enhancement factor, and constructing a feature pyramid structure to realize hierarchical segmentation of a flaw region; extracting a multi-dimensional feature set containing local textures, gray statistics and shape description, distributing weight coefficients through a feature evaluation matrix, and fusing to generate a feature vector; a double-order cascade classifier is used for classification, a lightweight decision tree is used for rapid pre-classification in the first order, and features are extracted and fused through an attention-enhanced deep network in the second order. According to the invention, the accuracy and real-time performance of the defect detection of the packing material are improved, and the adaptability of the detection system to different types of defects is enhanced.

The invention discloses a heart failure patient remote monitoring and early warning system based on the Internet of Things, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing multi-source physiological and behavior data, and generating an input sample set; the feature evaluation module is used for constructing an extreme gradient boosting tree algorithm model and obtaining feature importance scores; the feature screening module is used for executing a recursive feature elimination process and outputting an optimal feature subset; the risk judgment module is used for inputting the optimal feature subset into an extreme gradient boosting tree algorithm model to generate a heart failure risk score; the early warning triggering module is used for triggering early warning and sending early warning information to the patient and the platform; the feedback recording module is used for generating feedback information; and the dynamic optimization module is used for updating the input sample set based on the feedback information, retraining the extreme gradient boosting tree algorithm model and screening the features. According to the method, the extreme gradient boosting tree and recursive feature elimination are fused, and accurate evaluation and dynamic early warning of the multi-source data of the heart failure patient are realized.

The invention discloses an underwater robot real-time three-dimensional map reconstruction system and method fused with an SLAM algorithm, and belongs to the technical field of three-dimensional map reconstruction. By constructing a confidence coefficient system of three-dimensional points, a spatial trajectory fluctuation index and an inter-frame texture change rate are introduced as dynamic feature evaluation indexes; and a regression model is adopted to score and grade the severity of the false points, and differential processing of different grades of point clouds is realized in combination with a dynamic suppression mechanism, so that an unstable structure is effectively eliminated, and the stability and reliability of the sparse three-dimensional map are remarkably improved. Therefore, the positioning precision, the path planning capability and the task execution safety of the underwater robot in a complex environment are enhanced.

The invention provides a dynamic data traceability method and system oriented to network security, and relates to the technical field of network security and data governance crossing, and the method comprises the steps: carrying out the real-time visual monitoring of a user interaction interface of a data transaction platform, and capturing the visual behavior information related to data calling; extracting feature parameters based on the visual behavior information to obtain a visual feature data set, and performing structured conversion on the visual feature data set to obtain a structured call log record containing visual behavior associated information; based on the structured call log record, extracting a time sequence, a frequency and a spatial distribution feature of a call behavior as basic indexes; a plurality of feature sampling states are dynamically selected in a monitoring time window, and a dynamic feature evaluation set is constructed based on the feature sampling states. According to the invention, the accuracy and controllability of network security protection in a data transaction scene can be improved.

The invention discloses a tracking and tracing method and system based on an RFID Internet of Things technology. The method comprises the following steps: firstly, realizing full-link abnormal risk quantification through multi-source data fusion and dynamic evaluation; the method comprises the following steps: carrying out feature embedding processing on RFID full-link sensing data of a tracked article, and extracting a sensing feature vector; generating fusion depth features reflecting full-link association through depth feature fusion; evaluating the fusion depth feature based on a multi-traceability link to obtain the abnormal confidence of each link; analyzing the perception feature vector to obtain an influence coefficient of each link on the whole link abnormity; and dynamically aggregating the abnormal confidence coefficient and the influence coefficient, and calculating a full-link abnormal probability. According to the method, the problems of multi-source data time sequence alignment and feature fusion are solved, accurate positioning of abnormities of all links and dynamic evaluation of full-link risks are achieved, the method is suitable for full-link tracking scenes such as agricultural products and aquatic products, and the quality control efficiency is improved.

The invention discloses an automobile circuit board drilling burr detection method and system based on area growth, and aims to improve the quality control efficiency and accuracy of automobile circuit board manufacturing. The system is used for quickly and accurately identifying and evaluating burr defects in the automobile circuit board drilling process; a high-definition image and three-dimensional scanning data of an automobile circuit board drilling area are obtained through a high-precision image acquisition device, and image preprocessing including noise reduction, enhancement and graying processing is carried out. An intelligent seed point selection strategy is adopted, three-dimensional data are combined, deep analysis is carried out on the areas, and various features including the shape, the size, the brightness and the like are extracted. According to the method, the extracted features are classified by training a primitive machine learning model, the burrs are effectively identified and classified, the positions of the burrs are accurately positioned, the potential influence on the performance of the automobile circuit board is evaluated according to the features, a detailed detection report is generated, and a scientific basis is provided for quality control and production optimization.

The invention provides an efficient data retrieval method and system oriented to a large language model. The method comprises the following steps: determining an intention vector set matched with a user intention; determining a specified number of candidate text fragments with the highest correlation with the user intention in an index database; obtaining multi-dimensional feature evaluation data of each candidate text fragment, and constructing a multi-dimensional feature numerical matrix of each candidate text fragment according to the multi-dimensional feature evaluation data; according to the multi-dimensional feature numerical value matrix, selecting selected text fragments from the candidate text fragments; sorting the selected text fragments, and sequentially performing scene matching on the selected text fragments and a pre-cached high-frequency query scene according to the sorting, so as to extract cache content matched with the target high-frequency query scene from the cache as a query result when a matching result represents that the current query scene accords with the target high-frequency query scene. The invention provides an efficient data retrieval method, and the quality and precision of data retrieval are improved.

The invention relates to the technical field of underwater detection, and discloses a forward-looking sonar underwater detection system based on multi-beam forming. The system comprises seven modules: a sonar data acquisition module obtains a multi-beam sonar original data sequence; a historical sonar database retrieval module retrieves a matched historical detection record set according to the region identifier; the heterogeneous sonar record distinguishing module classifies the historical sets and determines a plurality of distinguishing sets; a sonar feature extraction module traverses and distinguishes the set calculation to obtain a sonar feature set value and an associated bandwidth; the multi-scale sonar analysis module processes a network layer receptive field by taking a feature association bandwidth as a beam, and analyzes original data to obtain a multi-scale detection feature set; a cross-scale sonar fusion module performs interactive fusion on the multi-scale features to generate a target interactive fusion sonar feature set; and the underwater target area evaluation module outputs a thermodynamic map containing potential target area probability distribution based on fusion feature evaluation, and adapts to complex underwater environment detection.

The invention discloses an intelligent inspection method and system for a construction site, and belongs to the technical field of image processing. The method comprises the following steps: processing a remote sensing image of a construction site cement pavement, and positioning a pavement pixel area; calculating a relative difference value of each pixel point according to a pixel mean value of the region, and generating a difference value distribution image; and extracting a maximum value and a minimum value in each window region of the distribution image to respectively obtain a high-difference value compressed image and a low-difference value compressed image. Then, marking a non-pavement pixel region as an abnormal point, carrying out grid division on the two types of compressed images, calculating a difference characteristic value based on the abnormal point in a grid, and forming a high and low difference characteristic matrix; respectively calculating the joint deviation degree of each grid difference characteristic value to obtain a high joint deviation matrix and a low joint deviation matrix; and finally, processing the matrix through a pavement defect feature evaluation network, and outputting a pavement quality score. According to the invention, through multi-dimensional feature extraction and intelligent evaluation, the precision of pavement evaluation is effectively improved.

To improve the defect detection accuracy of an object in a focused process of inspection.SOLUTION: A defect inspection device that inspects a defect of an object in a focused process of inspection, acquires a training image that is an image of the object in the focused process of inspection, and further trains a base model neural network intended for feature extraction using the training images and data regarding defects visible in the training image, then acquires an inspection image that is an image of the object's target inspection area in the focused process of inspection, and uses an individual model obtained by additional training of the base model in a fine-tuning unit to evaluate a defect of the object in the focused process of inspection from the inspection image, evaluates a defect of the object in the focused process of inspection based on a feature quantity corresponding to the defect of the object depicted in the inspection image to perform defect inspection of the object based on the evaluation result from a first evaluation unit and the evaluation result from a second evaluation unit.SELECTED DRAWING: Figure 2

The invention provides a health state depth feature extraction method for key components of an extra-high voltage direct current transmission converter valve, and belongs to the technical field of extra-high voltage transmission equipment.The health state depth feature extraction method comprises the steps that firstly, different fault state models are constructed through PSCAD simulation, data are collected, and dimension reduction processing is conducted on multi-source heterogeneous data through principal component analysis; and establishing a time sequence sliding window mechanism to calculate data characteristic parameters. And then temperature features are extracted based on a thyristor junction thermal diffusion mechanism equation, deep feature extraction is performed by using a multi-head self-attention enhanced bidirectional long-short-term memory network, multi-source data such as infrared temperature measurement and ultraviolet discharge are fused, and abnormal feature representation is calculated through an isolated point feature evaluation function. And finally, constructing a feature vector space containing electrical features, temperature features, isolated point features and multi-component correlation features, outputting complete health state feature representation, and providing a basis for state evaluation and fault prediction of the converter valve.

The invention discloses a music playing intelligent evaluation system and method based on multi-mode and man-machine cooperation, and relates to the technical field of music playing intelligent evaluation, and the method comprises the steps: collecting audio signals, video images and playing motion data in a music playing process, and obtaining a multi-mode information data set; performing feature extraction on the multi-mode information data set, and analyzing the audio signal, the video image and the playing action data by applying a convolutional neural network and a recurrent neural network model to generate a basic feature set; based on the basic feature set, utilizing a deep learning algorithm to identify and analyze emotion content conveyed in the playing process, and outputting an emotion intensity value and an emotion category; analyzing intonation error, rhythm deviation and strength matching degree according to comparison between the standard music score and the playing record, evaluating action normalization of the player in combination with action characteristics extracted from the playing action data, and calculating scores of all dimensions; and adjusting the weight between the objective score and the subjective score according to the application scene.

The invention discloses a video small target detection method and system based on density features, and the method comprises the following steps: taking a plurality of frames of images of a video as input, carrying out the feature extraction of a backbone network, enabling a spatial-temporal feature fusion module to fully utilize the features of adjacent reference frames to enhance the target features of a current frame through deformable convolution, and carrying out the feature extraction; and the information loss in the feature extraction process is effectively compensated. The space-feature fusion module introduces feature features and combines space and channel attention mechanisms to dynamically generate reasonable query, so that the detection capability of small targets in a dense region is remarkably enhanced. The coarse-to-fine reference feature screening and enhancement module is composed of a reference feature evaluation and selective feature enhancement module, richer information is provided for small target detection, and finally a target detection result is output through a prediction module. The modules cooperate with each other to improve the overall performance of video small target detection.

The invention discloses a methanol fuel filling detection and alarm system for a vehicle, and particularly relates to the technical field of data acquisition and correction, comprising a data acquisition module for acquiring environmental data and sensor data in real time; the fluctuation analysis module analyzes data fluctuation and determines whether to activate an evaluation mechanism; the feature evaluation module performs environmental adaptability and compensation correction evaluation when the evaluation mechanism is activated; the decision classification module inputs an evaluation result into a machine learning model and divides job types; the alarm module gives an alarm when the height interference operation occurs; the switching optimization module adjusts a compensation model according to the interference level to ensure the filling precision and stability; according to the method, environment adaptability evaluation and compensation correction evaluation are combined, so that the system can evaluate and reflect the condition in the filling process in time when the environment condition changes, the response capability to the environment change is enhanced, the compensation coefficient can be automatically adjusted under the condition that the adaptability of the fixed compensation model is insufficient, and the compensation efficiency is improved. And the accuracy of sampling data in the filling process is ensured.

The invention discloses a product recommendation method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is used for determining recommended customers adapted to insurance products. Firstly, an advanced natural language processing model is utilized to accurately analyze a user intention, and key semantic information is extracted; secondly, user requirements are accurately mapped to specific industries and business scenes through semantic mapping rules; newest market data and policy information are obtained in real time through a networking search interface, and timeliness and comprehensiveness of the data are guaranteed. Based on a preset report template, the system automatically integrates the upstream and downstream structures and policy dynamics of the industrial chain, and generates a structured and visual market insight report. And finally, extracting high-value customers meeting business requirements from the report through a customer portrait matching algorithm, and forming an accurate recommendation customer set in combination with multi-dimensional feature evaluation. According to the invention, precise product recommendation and high-value customer mining based on natural language input of the user are realized.

A methodology for bias mitigation in large language models (LLMs), leverages correlations between linguistic feature evaluations and bias benchmarks. A CL framework is used to investigate potential relationships between model behaviors and biased outcomes, providing a deeper understanding of the mechanisms underlying bias in LLMs. These insights are applied in a multi-task learning framework to demonstrate a more generalizable bias mitigation approach, achieving measurable reductions in gender and age biases with minimal trade-offs in model performance.

The invention provides an electric power remote safety protection system, and belongs to the technical field of electric power protection, and the system comprises a monitoring module which is used for monitoring the data information of electric power terminal equipment in a power grid, and obtaining a terminal data set; the extraction module is used for performing feature extraction on the terminal data set; the analysis module is used for acquiring the detection instruction book from the test management platform and analyzing the detection instruction book to generate a detection object and a compliance index associated with the detection object; the establishing module is used for establishing a feature evaluation model based on a preset algorithm and in combination with the compliance index; and the analysis module is used for carrying out situation analysis on the extracted features according to the feature evaluation model, and judging whether an abnormal behavior warning is sent out or not according to a situation analysis result. The problem that a large amount of time and manpower are consumed because an existing protection system needs a person to judge accident reasons on site is solved.

The invention relates to a blockchain-based mesenchymal stem cell safety inspection method and system, and relates to the field of data processing, core detection data and environmental parameters of stem cells are acquired for data formatting, timestamps and equipment ID metadata are embedded, a structured data object is established, and data state feature evaluation is executed; an encryption strategy is configured for encryption according to the evaluation result and the disturbance strategy, zero-knowledge proof is generated for an encrypted ciphertext and encryption parameters, the disturbance strategy and the encryption parameters serve as metadata records, and contract management is controlled through special disturbance; and uploading the encrypted ciphertext, the zero-knowledge proof and the special disturbance control contract to the block chain according to the preset block data structure to perform safety inspection management, thereby solving the technical problems that the safety inspection data of the mesenchymal stem cells are easy to tamper and lose, the processing efficiency is low, and tracing and sharing are difficult.

A cognitive function evaluation system (100) comprises an imaging unit (10), a generation unit (30), a preprocessing unit (40), and an evaluation unit (60). The imaging unit (10) captures an image of an evaluation subject (SJ) performing a dual task in which a cognitive task and an exercise task are simultaneously imposed, to generate an imaging signal. The generation unit (30) generates, on the basis of the imaging signal, time-series data of a skeleton representing at least a part of the entire body of the evaluation subject (SJ) performing the dual task. The preprocessing unit (40) decomposes the time-series data of the skeleton for each cycle to generate cycle-specific skeleton data, and generates input data by matching the phases of the cycle-specific skeleton data. The evaluation unit (60) extracts features of the motion of the evaluation subject (SJ) on the basis of the input data, and evaluates the cognitive function of the evaluation subject (SJ) on the basis of the extracted features of the motion of the evaluation subject (SJ).

The invention discloses a text content risk identification method and system, a computer device, a medium and a product. The text content risk identification method comprises the following steps: splitting a to-be-identified multi-language text initiated by a target user according to different languages to obtain a plurality of language texts; translating a non-target language text in the plurality of language texts to obtain a translated text of a target language; obtaining texts of all target languages, and recording the texts as target texts; obtaining multi-dimensional data of a target user from a business system database based on an MCP mechanism, and constructing a user portrait according to the multi-dimensional data; the target text, the user portrait and a risk feature evaluation algorithm are input into a semantic analysis understanding large model, semantic analysis information is obtained, and the semantic analysis information comprises risk feature scores and risk text positions; and determining a risk type according to the risk feature score, and triggering a corresponding processing mechanism according to the risk type. According to the method, rapid and accurate multi-language text risk identification can be realized.

The embodiment of the invention discloses a news content recommendation method based on a value-aware double-layer attention network. A specific embodiment of the method comprises the steps of inputting candidate news information into a pre-trained double-layer attention network to generate a news feature evaluation value, and performing value perception on the candidate news information to generate candidate news value features; performing attention feature extraction on the candidate news value features to generate candidate news value attention features; encoding the candidate news information to generate candidate news codes; performing feature evaluation on the candidate news value attention features and the candidate news codes to generate news feature evaluation values; and according to the news feature evaluation value, adding the candidate news information to a corresponding position in a news information recommendation queue, and sending the news information meeting a preset pushing condition in the news information recommendation queue to the user terminal. According to the embodiment, waste of transmission resources can be reduced.

The aerosol visual detection system comprises a base, a light source cavity, a smoke diffusion cavity and a camera shooting cavity are formed in the base in sequence, a shading insertion plate is inserted between the light source cavity and the smoke diffusion cavity, a transparent smoke isolation insertion plate is inserted between the smoke diffusion cavity and the camera shooting cavity, and the light source cavity, the smoke diffusion cavity and the camera shooting cavity are communicated through the transparent smoke isolation insertion plate. A plane light source is arranged on one side of the base, a cover plate is arranged on the other side of the base, a camera is arranged on the side, facing the camera shooting cavity, of the cover plate, and a smoke exhaust pull plate is detachably arranged at the front end of the smoke diffusion cavity. According to the aerosol visual detection system and the characteristic parameter evaluation method, the camera can directly obtain the two-dimensional space distribution of the aerosol through plane imaging, and in combination with an image analysis technology, not only can the concentration information be inverted, but also multi-dimensional characteristics such as textures, contours and dynamic diffusion can be extracted; the defects of a traditional photoelectric method in spatial information and multi-feature analysis can be overcome, and a new technical path is provided for fine detection and feature evaluation of aerosol.

The invention relates to the technical field of energy consumption interval prediction, and particularly discloses a building energy consumption interval prediction method, system and equipment, and the method comprises the steps: collecting historical energy consumption data, meteorological data, building characteristic data and use mode data of a target building, and carrying out the hierarchical processing, thereby obtaining a hierarchical data set; performing wavelet transform decomposition on the historical energy consumption data, and decomposing the energy consumption time sequence into a trend term, a periodic term and a random term to obtain multi-scale decomposition features; constructing a feature evaluation model, calculating a contribution degree and dynamically allocating weights to obtain a weighted feature set; constructing an integrated prediction framework based on the weighted feature set, and generating a prediction interval through quantile regression; and performing adaptive adjustment according to the historical prediction deviation and the environmental factor change to obtain a final prediction interval. Through multi-scale feature processing, dynamic weight optimization and prediction interval adaptive calibration, the accuracy and reliability of the prediction interval are improved, and more accurate support is provided for building energy management.

The invention relates to the technical field of medical image auxiliary diagnosis, and provides an intervertebral joint osteoarthritis image feature evaluation method based on deep learning, which can synchronously identify five types of FJOA image features such as joint space stenosis, osteophyte, hypertrophy, subchondral bone erosion and subchondral cyst, and improves the comprehensiveness and efficiency of evaluation. The nnU-Net model is adopted to perform high-precision segmentation on an intervertebral joint region, so that the positioning precision is effectively improved; by introducing a shared feature extraction network based on ResNet-18 and five parallel classification sub-networks, joint modeling of multi-scale semantic information is realized, and the recognition accuracy and generalization ability of the model are improved; according to the method, a Grad-CAM technology is combined to generate an activation thermodynamic diagram, a model interpretation basis is superposed to an original image, visual display of an evaluation result is realized, interpretability and clinical applicability of the model are enhanced, and FJOA image features in axial lumbar vertebra CT images from two central data sets can be quantitatively evaluated so as to comprehensively quantify individual FJOA image features.