733results about How to "Improve diagnostic efficiency" patented technology

The invention discloses a multi-working-condition-oriented equipment fault diagnosis method based on unbalanced learning, and the method comprises the following steps: collecting the operation data of equipment under different working conditions through a plurality of sensors, carrying out the preprocessing, feature extraction and feature selection of the collected original operation data, and obtaining a feature set; processing the feature set by adopting an unbalanced learning technology and a cost-sensitive learning algorithm to obtain a balanced data set; constructing a multi-working-condition fault diagnosis model based on learning, and optimizing model parameters by adopting a method of combining cross validation and grid search according to a balanced data set in a model training process; deploying a multi-working-condition fault diagnosis model obtained through training, carrying out the online diagnosis of equipment faults, and continuously updating and optimizing the model through a continuous learning mechanism. According to the method, the unbalanced learning technology is applied to multi-working-condition fault diagnosis, the accuracy and robustness of diagnosis can be remarkably improved, and real-time monitoring and predictive maintenance of the equipment health state are achieved.

The invention relates to the technical field of skin disease diagnosis, and discloses an AI auxiliary decision support system for skin disease diagnosis, which integrates an image acquisition module, an image analysis module and a comprehensive analysis and diagnosis decision module. A high-definition image of the skin of a patient is obtained through high-precision skin photographing equipment, and a training data set is formed by combining basic information of the patient. A deep learning algorithm is utilized to train a skin disease recognition model and an illness state evaluation model, and accurate recognition of skin disease types and objective evaluation of severity are realized. And the comprehensive analysis module fuses output of the two models, constructs a fusion decision model and provides comprehensive diagnosis suggestions. And the diagnosis decision module is used for generating a disease type judgment and illness state evaluation report based on the trained model in combination with real-time image data so as to effectively assist doctors in making decisions. The accuracy, the efficiency and the popularization degree of skin disease diagnosis are improved, and complex and changeable diagnosis requirements in clinical practice are met.

The invention relates to the technical field of health assessment, in particular to a big data-based breathing health assessment system, which comprises a breathing parameter analysis module, an interaction relationship assessment module, a breathing anomaly detection module and a breathing condition assessment module. According to the method, accurate extraction of breathing parameters and evaluation of breathing periodic changes are achieved by analyzing thoracic pressure waveform data, the breathing mode of a user is accurately recognized, physiological parameters are collected, the mutual relation between the physiological parameters and the breathing mode is analyzed, the influence of various physiological factors on breathing health is revealed, the dynamic monitoring capacity on the breathing health is enhanced, and the user experience is improved. According to the method, by combining detection and identification of abnormal breathing parameters, identification and classification of abnormal breathing reasons are achieved, medical professionals are helped to make treatment plans and health management strategies, diagnosis efficiency is improved, the misdiagnosis rate is reduced, and powerful support is provided for management of various chronic diseases by combining continuous trend analysis.

The invention discloses a multi-mode breast ultrasonic diagnosis system based on a dynamic feature fusion network, belongs to the field of medical image processing, and particularly relates to the multi-mode breast ultrasonic diagnosis system based on the dynamic feature fusion network. The invention aims to solve the problem that the existing method is low in breast ultrasonic recognition accuracy. The system comprises a multi-modal data preprocessing main module used for preprocessing a collected multi-modal mammary gland ultrasound image to obtain a preprocessed multi-modal mammary gland ultrasound image; the multi-modal convolutional neural network main module is used for building a dynamic feature fusion network model; the model training main module is used for training an established dynamic feature fusion network model by using the preprocessed multi-modal breast ultrasound image to obtain a trained dynamic feature fusion network model; and the model test main module is used for loading the trained dynamic feature fusion network model, classifying a to-be-diagnosed multi-modal breast ultrasound image, and identifying benign and malignant breast tumors in the multi-modal ultrasound image.

The invention discloses an on-load tap-changer fault diagnosis method based on lightweight YOLO11, and is used for solving the problem of low mechanical fault diagnosis efficiency of a traditional on-load tap-changer. Comprising the following steps of: converting vibration signals in a motor starting stage and a change-over switch action stage into high-resolution time-frequency images through continuous wavelet transform (CWT) so as to comprehensively reflect time and frequency characteristics of the signals; starNet is adopted to replace a YOLO model backbone network, and the feature extraction capability is enhanced while the network compactness is kept through star operation; and designing an efficient joint convolution detection head EJCD, and realizing model lightweight and multi-scale target detection through grouping normalization and convolution parameter sharing. According to the invention, through combination of the time-frequency image representation capability of continuous wavelet transform and an efficient target detection framework of deep learning, the limitation of a traditional method in non-stationary signal processing is overcome, and the diagnosis efficiency and the model performance are significantly improved.

The invention discloses a multi-source modal data fusion-oriented charging pile fault diagnosis method and system. The method comprises the following steps of: acquiring message stream data, audio data and infrared sensing data associated with a charging equipment fault through a sensor system; and based on the characteristics of each modal data, fault characterization and identification are carried out by adopting a random forest, a convolutional neural network and a YOLO-v8 target detection model. According to the marked fault data, a mapping relation among multi-source data is established, and a Dempster-Shafer multi-source fusion theory is utilized to aggregate a multi-modal fault judgment result, so that the equipment fault is accurately diagnosed. According to the method, the accuracy of fault diagnosis and diversified presentation of fault types are effectively improved, the repair rate is reduced, and the method has a good engineering application prospect.

The invention discloses a train key component online health monitoring method based on multi-sensor signal cooperation, and the method comprises the steps: selecting an indirect signal as an edge end train online abnormal state early warning index based on the correlation analysis of multi-sensor signals, and diagnosing whether the state of a train key component is abnormal or not; the vibration signals serve as signals for further diagnosis at the cloud, state features of the vibration signals are extracted at the edge end, online optimization of sparse coding is carried out, and the vibration signals are transmitted to the cloud to serve as diagnosis signal features for further diagnosis; real-time training precision and sample complexity are integrated into online estimation of a model training process, optimal features are transferred from a source domain-edge end to a target domain-cloud end, and the cloud end diagnoses the type and grade of a fault by means of the complex operation capability of the cloud end; a dynamic aggregation strategy is provided, the features of a prediction model are determined, optimization of nodes is enhanced, and a feature network is made to adapt to and complete an online prediction task.

The invention discloses an AE-GCN-based unmanned aerial vehicle system fault diagnosis method and equipment, belongs to the technical field of unmanned aerial vehicle fault diagnosis, and is used for solving the technical problems that an existing unmanned aerial vehicle fault diagnosis method depends on expert experience and feature engineering and has the defects of low efficiency, poor generalization ability and the like. The method comprises the following steps: constructing a corresponding structure topological graph based on a topological structure of a target unmanned aerial vehicle, and obtaining a topological matrix; performing node feature extraction on the structure topological graph to obtain a node feature vector; inputting the topological matrix and the node feature vector into a graph convolution neural network for graph convolution operation to obtain a node state feature vector; inputting the data into an attention mechanism module to obtain attention features, and mapping the attention features into node state updating parameters; comparing the node state updating parameter with the node state historical parameter, and positioning a fault node and a fault type; and predicting a fault propagation path based on other nodes directly connected with the fault node in the structural topological graph.

The invention discloses a medical ultrasonic image intelligent processing method and system, and relates to the technical field of medical image.According to the main scheme, normal feature coordinates of different organs and tissues are calculated by obtaining normal ultrasonic image data, to-be-processed image data and historical medical record information of a patient; extracting coordinates of a to-be-detected image and comparing the coordinates with normal coordinates for preliminary judgment; performing secondary judgment by combining ovality and circularity; setting a lesion gray threshold to judge whether a lesion exists or not; calculating the size of a lesion area when the lesion exists; according to the method and the system, the comprehensive blood flow evaluation index is calculated in combination with color Doppler blood flow imaging data, the disease evaluation value is calculated according to known medical record data and the like, the disease level is judged, the diagnosis report is automatically generated, the accuracy and the efficiency of medical ultrasonic diagnosis are improved, and powerful support is provided for clinical diagnosis.

The invention provides an electric power system main equipment defect diagnosis method and system based on a large language model, and relates to the technical field of electric power system operation and maintenance and intelligent diagnosis, and the method comprises the steps: collecting defect description information of electric power system main equipment; preliminarily understanding the defect description information by using a pre-constructed natural language representation model, obtaining corresponding embedded representation, executing a defect level classification task and a defect basis classification task, and outputting a defect level classification result and a defect basis classification result; and summarizing by utilizing a large language model, fusing the summarized classification information into a prompt text, exciting the reasoning ability of the large language model through a prompt project, and finally obtaining a diagnosis basis and a diagnosis result corresponding to the defect description information. According to the method, excellent results are obtained in defect level classification tasks and question and answer tasks, and high-quality classification evidences and guidance information can be generated.

The invention discloses an intelligent operation and maintenance fault diagnosis system based on a communication network, which belongs to the technical field of communication networks and comprises a data acquisition preprocessing module, a network dynamic evolution module, a quantum auxiliary diagnosis module, a fault prediction evaluation module, a fault decision module, a fault operation and maintenance module and a fault feedback module. By integrating the data acquisition and preprocessing module, the network dynamic evolution module and the quantum auxiliary diagnosis module, the system can acquire and process the state information of a communication network in real time, and quickly position and accurately identify a fault scene in combination with the high efficiency of quantum calculation, so that the fault diagnosis time is remarkably shortened, the diagnosis accuracy is improved, and the fault diagnosis efficiency is improved. Through the fault prediction and evaluation module in combination with the results of network dynamic evolution and quantum-assisted diagnosis, prediction of a plurality of future network states can be generated, different fault scenes are simulated for evaluation, potential risks are effectively predicted, and meanwhile, through risk evaluation and sorting, operation and maintenance personnel are helped to preferentially process high-risk scenes, and the operation and maintenance efficiency is improved.

The invention discloses a livestock intestinal parasitic disease diagnosis and treatment method based on a visual language large model, which comprises the following steps of: training the visual language large model by utilizing a parasite image-text feature alignment data set, so that the large language model has visual capability on parasites; then, constructing a livestock and poultry parasitic disease diagnosis and treatment dialogue data set, and training the LoRA fine-tuning visual language large model by using the diagnosis and treatment dialogue data set to obtain a diagnosis and treatment dialogue model; utilizing the diagnosis and treatment dialogue model to realize livestock parasitic disease diagnosis and treatment. The diagnosis and treatment efficiency of parasitic diseases of livestock and poultry can be improved, the misdiagnosis rate is reduced, and the diagnosis and treatment process is optimized.

The invention provides an intelligent digital image processing system for urinary surgery. The intelligent digital image processing system is suitable for accurate diagnosis and dynamic monitoring of diseases such as kidney tumors and bladder tumors. The system comprises a data acquisition module, a multi-modal image fusion module, an image segmentation module, a dynamic lesion tracking module, a three-dimensional modeling module, an intelligent diagnosis support module and a federal learning module. The data acquisition module receives CT, MRI and other image data; the multi-modal image fusion module generates a high-quality comprehensive image through a registration algorithm; the image segmentation module extracts a lesion area by using deep learning; the dynamic lesion tracking module calculates lesion volume, surface area and change trend thereof; the three-dimensional modeling module generates a three-dimensional visual model of the lesion area; and the intelligent diagnosis support module outputs a diagnosis report containing lesion features, development trends and treatment suggestions. According to the invention, through multi-modal fusion and dynamic monitoring, the accuracy of lesion area extraction is improved.

The invention discloses an automobile fault diagnosis method and system, and belongs to the technical field of fault diagnosis, and the system comprises an automobile fault identification model, an automobile fault knowledge graph model and a reasoning diagnosis integration model. S100, acquiring a natural language description text of the automobile fault, preprocessing the natural language description text of the automobile fault, and extracting a description entity related to the automobile fault; s200, constructing an automobile fault identification model, inputting the description entity related to the automobile fault into the automobile fault identification model, and outputting a fault type; s300, constructing an automobile fault knowledge graph model, inputting a description entity and a fault type related to the automobile fault into the automobile fault knowledge graph model in combination with Cypher to perform semantic similarity matching, and querying a matched answer; and S400, constructing a reasoning and diagnosis integrated model, inputting the description entity and the answer related to the automobile fault into the reasoning and diagnosis integrated model in combination with prompt, and outputting a fault analysis result and a visual correlation analysis result.

The invention discloses a multistage safety response control method of an industrial and commercial liquid cooling energy storage system, and relates to the technical field of electric energy storage systems, a sensor network collects an operation state data time sequence matrix, and abnormal data cleaning is carried out on the time sequence matrix; the method comprises the following steps: preprocessing a vibration signal of a cooling system, extracting characteristic data of abnormal vibration through Fourier transform and energy distribution analysis, and positioning a time-frequency distribution abnormal region; generating a time sequence multivariable data set according to the characteristic data of the abnormal vibration, determining a cause label according to the fused time sequence multivariable data set, obtaining the inherent frequency and the resonant frequency of the cooling system in combination with simulation analysis, and matching and determining the cause of the abnormal vibration; an LSTW neural network is used for predicting the abnormal resonance risk, and corresponding countermeasures are taken for the cooling network according to the prediction result; unnecessary vibration caused by error adjustment is avoided, the relation between cooling efficiency and safety is balanced, and unnecessary power loss is reduced.

The invention relates to the technical field of test result abnormal mode intelligent early warning systems, in particular to a multi-mode biochemical test result abnormal mode intelligent early warning system and method, and the system comprises a dynamic threshold module, a multivariable mode module, a time evolution module and a self-adaption module. Through fusion of a dynamic threshold technology, multivariable pattern analysis, time sequence modeling and an interpretability mechanism, comprehensive, dynamic and intelligent analysis of biochemical test results is realized. According to the method, the accuracy and timeliness of anomaly detection are remarkably improved, and visual and credible decision support is provided for doctors. The innovative solution is expected to play an important role in the aspects of improving medical diagnosis efficiency, reducing missed diagnosis and misdiagnosis rates, promoting precise medical treatment and the like, and substantive improvement is brought to health management of patients.

The invention discloses a chest CT (Computed Tomography) image processing method and system, and relates to the technical field of medical image processing, and the main scheme is as follows: acquiring image data of two groups of X-rays with different energies and attenuation measurement values of pixel points by adopting dual-energy CT scanning, respectively calculating concentration values of soft tissues and blood vessels, and generating concentration maps; generating an initial image based on the soft tissue and blood vessel concentration map, calculating a pixel value, obtaining a target image pixel value through contrast stretching, segmenting the target image pixel value to obtain a binary image, and distinguishing a blood vessel region and a soft tissue region in the binary image by identifying and analyzing a connected region in the binary image; carrying out center line extraction on the blood vessel region, calculating a local diameter, and respectively comparing the local diameter with a blood vessel dilation threshold value and a blood vessel constriction threshold value to judge abnormal blood vessel dilation or constriction; extracting features from the soft tissue region to form a first feature vector, and comparing the first feature vector with a preset second feature vector to judge abnormality; abnormal conditions are automatically labeled or highlighted, so that the diagnosis accuracy and efficiency are improved.

The invention relates to the technical field of vehicles, and discloses a vehicle diagnosis method, vehicle diagnosis equipment, a vehicle diagnosis system and a storage medium, and the method comprises the steps: obtaining a to-be-executed diagnosis function instruction of a to-be-detected vehicle; according to a stored first corresponding relation, all execution paths of a diagnosis function instruction to be executed in the vehicle diagnosis equipment are determined, and the first corresponding relation is the corresponding relation between the diagnosis function instruction and the execution paths; acquiring vehicle configuration information of a to-be-detected vehicle; according to a stored second corresponding relation, a target execution path matched with the vehicle configuration information of the to-be-detected vehicle is determined from all the execution paths, and the second corresponding relation is the corresponding relation between the execution paths and the vehicle configuration information; and executing a diagnosis function corresponding to the target execution path on the to-be-detected vehicle. Through the above mode, the vehicle diagnosis efficiency is improved.

The invention discloses a rapid maintenance method for a distribution line, and relates to the technical field of electrical variable measurement, and the method comprises the steps: transmitting a multi-frequency electromagnetic wave to the distribution line through a multi-frequency electromagnetic wave detection device, receiving an echo signal, and extracting the electromagnetic characteristic data of the line; performing hierarchical analysis on the electromagnetic characteristic data to generate a fault preliminary report; based on the characteristics of the fault preliminary report and the echo signal, constructing a three-dimensional fault positioning map, and outputting fault positioning data; based on the repair suggestion report, utilizing an electric field to guide liquid metal to flow to the fault point, and performing filling and curing repair on the fault area; after repairing is completed, secondary detection is carried out on the repairing area and the adjacent area through multi-frequency electromagnetic waves, and the operation characteristics of the repairing point and the adjacent area are verified; according to the method, the electromagnetic characteristic data is extracted by using the echo signals, the three-dimensional fault positioning map is generated, the spatial position, geometric characteristics and material distribution of the fault point are accurately identified, and accurate support is provided for repair.

The invention provides a liver echinococcosis ultrasonic image intelligent processing method based on deep learning, electronic equipment and a storage medium. The method comprises the following steps: data collection: collecting liver ultrasonic image data; carrying out image preprocessing on the collected liver ultrasonic image data by using an adaptive denoising and enhancement model, carrying out classification labeling on the data after image preprocessing, establishing a classification data set, and labeling the images as cystic echinococcosis, alveolar echinococcosis and other liver lesions; and based on the classification data set, training an optimized VIT classification model DFEV-VIT. The beneficial effects of the invention are that the method carries out the image preprocessing of the collected data through employing the optimized CLAHE algorithm ADEN, and carries out the training of the classification model DFEV-VIT based on the classification data set after the image preprocessing, can greatly improve the diagnosis efficiency and accuracy, and reduces the missed diagnosis and misdiagnosis of the echinococcosis.

The invention discloses an AI-driven complete-cycle scoliosis management system, which belongs to the field of scoliosis management systems and comprises a data fusion module, a feature extraction module, a text processing module, a diagnosis inference module and an output presentation module. Wherein the data fusion module is used for performing deep fusion on sensor data of the wearable equipment and X-ray / CT medical image data to generate comprehensive fusion prediction data; the feature extraction module uses a pre-trained fusion encoder to perform feature extraction and encoding on the fusion prediction data to generate potential feature representation; the text processing module adopts a pre-trained text encoder to encode free text diagnosis information provided by a doctor and a patient and extract semantic features; and the diagnosis inference module is used for fusing the fusion features and the text features. According to the invention, the sensing data of the wearable device and the medical image data can be effectively integrated to give full play to the complementary advantages thereof, so that the diagnosis accuracy is improved.

The invention provides an AI analysis method and system based on a straight ethyl mirror image, and relates to the technical field of AI.The AI analysis method comprises the steps that multi-scale features of the straight ethyl mirror image are extracted through a pre-trained deep convolutional neural network, and feature fusion is carried out through a self-adaptive feature weight distribution module; and performing lesion area detection on the fused feature map by using a double-branch detection network, and classifying the detected lesion areas. And performing three-dimensional reconstruction based on a continuous orthoscope image sequence, mapping a detection result into a three-dimensional model, realizing accurate positioning and three-dimensional visualization of a lesion area, and recording three-dimensional coordinate position information of the lesion area. The method can improve the accuracy and efficiency of the image analysis of the straight ethyl mirror, and assists a doctor in diagnosis.

The invention relates to the technical field of fault diagnosis, and discloses a vehicle fault diagnosis system and method, a vehicle terminal and a storage medium, the system comprises the vehicle terminal and an edge calculation center, the vehicle terminal is used for collecting diagnosis fault codes and vehicle operation data of the vehicle terminal, and if a first fault code is found in the diagnosis fault codes, the vehicle operation data is sent to the edge calculation center; if yes, determining a first fault result according to the first fault code; if a second fault code is found in the diagnosis fault code, an edge node is determined according to the position information of the vehicle terminal, the edge node is connected, the second fault code and second operation data corresponding to the second fault code are sent to the edge node, the edge node is at least one of edge calculation centers, and the edge calculation centers are connected to the edge node; the edge node is used for performing fault diagnosis according to a third fault code and third operation data corresponding to the third fault code if the third fault code is found in the received second fault code to obtain a second diagnosis result; through the method, a more practical and more accurate vehicle fault diagnosis system is provided.

The invention discloses a medical ultrasonic image quality control system based on multi-modal fusion. The medical ultrasonic image quality control system comprises a data preprocessing module used for collecting medical ultrasonic image data of different modals from different crowds in real time and preprocessing the medical ultrasonic image data; the dynamic self-adaptive fusion module is used for dynamically adjusting a fusion strategy according to the medical ultrasonic image data acquired in real time and introducing a self-adaptive mechanism to dynamically adjust weights of different modal data; the image enhancement module combines medical ultrasonic image data with a three-dimensional anatomical structure of a human body by utilizing an augmented reality technology to generate a visual and three-dimensional diagnostic image; the visualization module adopts a dynamic image display mode to present the change process of the diagnosis image in real time; a complete medical ultrasonic image quality control system is constructed by integrating a data preprocessing module, a dynamic adaptive fusion module, an image enhancement module, a visualization module and the like, the system can automatically process and analyze medical ultrasonic image data, and the quality control efficiency and accuracy are improved.

The invention discloses a wind turbine generator variable pitch system fault diagnosis method for data balance. The method specifically comprises the following steps: acquiring operation data and fault data of a wind turbine generator through a monitoring and data acquisition system equipped on a wind power plant; based on the obtained data, screening sensitive features related to the fault of the variable pitch system, reducing the dimension of a data sample and reducing the input of redundant information; according to the quantitative relation, dynamic balance factors are distributed for various types of samples, the dynamic balance factors are introduced into a multi-stage diagnosis model Dualformer based on double Transformers, and a DBFDformer data balance fault diagnosis model is constructed; and training a DBFDform model by using the screened feature data, and completing dynamic updating of balance factors and optimization of model hyper-parameters through a snow ablation optimization algorithm. The wind turbine generator variable pitch system fault diagnosis method can effectively improve the diagnosis precision and performance of the wind turbine generator variable pitch system fault under the unbalanced data, has excellent generalization ability and robustness, provides a new thought for the fault diagnosis of the wind turbine generator variable pitch system under the unbalanced data sample, and is helpful for improving the utilization rate of wind energy.

The invention relates to the technical field of safety circuits, and provides a diagnosis method and circuit of a digital-to-analog conversion circuit, equipment and a medium. According to the method, two independent AI acquisition modules are arranged to respectively acquire analog quantity signals acquired at the same target measurement point, the acquired analog quantity signals are converted into digital signals through a built-in ADC circuit, and then the two paths of digital signals are transmitted to a safety logic module to be compared and judged; when the difference value of the two paths of digital signals is smaller than the preset safety threshold value, it is determined that the ADC circuit passes the diagnosis, and when the difference value of the two paths of digital signals is larger than or equal to the preset safety threshold value, it is determined that the ADC circuit does not pass the diagnosis, and therefore it is not needed to independently configure a DAC circuit for the ADC circuit in each AI collection module; the implementation difficulty and the implementation cost of the diagnosis circuit are reduced, the development period of the diagnosis circuit is shortened, and the diagnosis efficiency is improved.

The invention discloses an equipment fault diagnosis method and system, a storage medium and an electronic device, and relates to the technical field of industrial automation, and the equipment fault diagnosis method comprises the steps: determining a target service instance which is distributed when equipment registration service is completed for current equipment in a basic service module; a target service module to which the service instance belongs is utilized to analyze device data of the current device, whether the current device has a fault is determined according to an analysis result, the target service module belongs to the upper layer service module, and the target service module belongs to the lower layer service module. The upper-layer service module at least comprises one of an equipment state acquisition service module, an asynchronous message service module, an equipment trend service module, a historical data persistence service module and an equipment state analysis service module, and by adopting the technical scheme, the technical problem of low equipment fault diagnosis efficiency is solved.

The invention discloses an anxiety disorder diagnosis and treatment assisting and relieving system of a massage chair. The anxiety disorder diagnosis and treatment assisting and relieving system comprises a multi-mode biological signal collection function, an emotion detection and analysis function, an intelligent massage function and a feedback function. The anxiety state of the user can be comprehensively monitored by fusing multi-modal physiological signals such as body temperature, heart rate, skin conductivity and respiratory rate, and multi-dimensional real-time evaluation of the anxiety state of the user is realized by combining facial expression and voice emotion recognition. According to the anxiety degree, the intelligent massage system of the chair can automatically adjust the massage strength and mode to help the user to effectively relieve physical and mental pressure. Meanwhile, a built-in system can record physiological data and emotion changes of the user to form a health record, and change and improvement of anxiety disorder can be tracked conveniently for a long time.

The invention discloses a heart disease prediction system based on an electrocardiosignal and a large model, and relates to the field of electrocardiosignal processing, and the system is characterized in that the electrocardiosignal of a user is collected by using a heart coat, and baseline offset suppression and noise processing operation are carried out on the electrocardiosignal by using a data processing center; a variability sequence is extracted from the processed electrocardiosignal, an electrocardiograph scatter diagram is drawn according to variability, and scatter points in the scatter diagram are numbered and coded in a partitioned mode to obtain the sequence information entropy of the heart rate variability sequence; segmenting the electrocardiosignal based on the sequence information entropy and the wave crest, obtaining a waveform segment of a single cardiac pulse according to a segmentation result, inputting the waveform segment into the large medical prediction model, and outputting the characteristic change of the electrocardiosignal; and comparing the feature change with a pre-stored disease feature standard and outputting a disease prediction result. According to the method, the complex characteristics of the electrocardiosignals and the prediction capacity of the large model are combined, and the diagnosis precision and the early-stage discovery rate of heart diseases can be remarkably improved.

The invention discloses an intelligent equipment fault diagnosis method based on multi-modal fusion, and the method achieves the quick and accurate diagnosis of an intelligent equipment fault through integrating various sensor data such as sound, images, vibration and temperature, and carrying out the automatic analysis through a deep learning model. The invention aims to improve the accuracy and efficiency of fault diagnosis, reduce manual intervention, reduce the maintenance cost and improve the reliability of equipment operation. The technology is suitable for the fields of industrial automation, intelligent manufacturing and the like, and an efficient solution is provided for equipment maintenance and management.