3334 results about "Diagnostic methods" patented technology

The invention provides a power station equipment state real-time monitoring and diagnosing method and system based on cloud edge collaboration, and the method comprises the steps: adjusting a data collection period dynamically determined based on an adaptive sampling frequency adjustment algorithm, and collecting a vibration signal, a temperature signal and a current signal through a multi-source heterogeneous sensor array disposed in a power station equipment body; carrying out preprocessing by utilizing the edge computing node, generating a compressed feature vector, and uploading the compressed feature vector to a cloud end through an MQTT protocol; a multi-modal data fusion analysis module is started through a cloud, a three-dimensional evaluation matrix of the equipment health state is constructed in combination with historical operation data and environmental parameters of the equipment, and a calculation task distribution strategy between an edge calculation node and the cloud is adjusted in real time according to an evaluation result of the three-dimensional evaluation matrix. Abnormal mode recognition based on a deep residual network and fault source tracing double-channel analysis based on a physical model are executed, fault types and fault reasons are diagnosed, and the accuracy and timeliness of fault diagnosis are guaranteed.

The invention relates to an underground equipment fault early warning and diagnosis method based on big data analysis. The method is suitable for equipment operation state monitoring and intelligent diagnosis in underground operation scenes such as mines. The method comprises the following steps: collecting multi-source data such as an equipment running state, environment parameters and operation behaviors and preprocessing the multi-source data; multiple signal features are extracted and fused to construct a unified feature vector; performing health modeling by using the residual self-encoder model to generate a health index; an early warning threshold value is dynamically set through clustering analysis and Bayesian reasoning, and anomaly recognition is achieved; after early warning is triggered, fault type identification is carried out by adopting the fusion discrimination model; performing causal reasoning and maintenance suggestion generation based on the equipment fault knowledge graph; and continuously optimizing the model in combination with operation and maintenance feedback information, and constructing a closed-loop diagnosis mechanism. The method has the characteristics of high recognition precision, high response speed, explainable result and sustainable optimization of the model.

The invention discloses a data anomaly diagnosis method and system based on a knowledge graph and a large model. According to the method, multi-source data including structured database records, semi-structured technical documents and unstructured expert experience descriptions are obtained and preprocessed, a knowledge graph containing entity, relation and attribute levels is established, domain adaptation training is conducted on a large language model through the knowledge graph, and the large language model is obtained. And constructing a hierarchical reasoning framework to realize data exception identification, diagnosis and solution generation. The method solves the technical problems of non-uniform knowledge representation, limited diagnosis capability, single reasoning logic and the like of a traditional data anomaly diagnosis method, and has the following advantages: uniform representation and storage of multi-source heterogeneous data are realized; the domain understanding ability of the large language model is improved through knowledge injection; the accuracy and efficiency of abnormality diagnosis are improved by adopting a hierarchical reasoning framework; and experience accumulation and continuous optimization are supported, and high practicability and expansibility are achieved.

The invention relates to the technical field of medical image diagnosis, and discloses a medical image automatic diagnosis method and system based on deep learning. According to the method, multi-modal medical image data of a target object is acquired and standardized, a two-channel convolutional neural network is utilized to extract features, the features are processed through cross-modal feature fusion, adaptive attention weight distribution and other technologies, a cascaded two-way long-short-term memory network is adopted for modeling, abnormity is detected based on a probabilistic graph model, and the target object is identified. And the nidus is segmented by a multi-scale context information enhancement module, and finally a diagnosis suggestion is generated by a diagnosis inference engine driven by a knowledge graph. The system comprises a multi-modal image acquisition interface module, a distributed feature calculation cluster, a visual interaction terminal and a security audit module. According to the method, the accuracy and efficiency of medical image diagnosis can be improved, comprehensive diagnosis reference is provided for doctors, and meanwhile data safety and privacy are guaranteed.

The invention discloses a multi-modal knowledge graph construction and intelligent fault diagnosis method and system for a communication power supply system. The method comprises the steps of multi-modal knowledge graph construction, graph increment updating and intelligent fault diagnosis. The multi-modal knowledge graph construction adopts a unified data acquisition semantic specification and a heterogeneous data fusion strategy, a dynamic evolution heterogeneous graph is established, and equipment full life cycle state perception and causal link modeling are realized; the efficient, atomicity and consistency updating of the atlas is realized by the hierarchical atlas increment through shadow composition, structural difference rate calculation and a subgraph replacement mechanism; according to the intelligent fault diagnosis, an alarm propagation sub-graph is constructed, a path convergence and multi-dimensional attribute scoring mechanism is adopted, and deep joint verification is carried out by using a multi-modal evidence fusion network; and in combination with a reinforcement learning optimization strategy embedded based on a graph structure, adaptive scheduling and diversity constraint of a diagnosis path are realized, and the fault positioning accuracy and the system intelligence in a complex scene are remarkably improved.

The invention provides an intelligent factory fault diagnosis method and system based on an AI prediction model, and the method comprises the steps: obtaining an equipment monitoring data flow of a target production line of an intelligent factory, carrying out the diagnosis feature construction processing of the equipment monitoring data flow, generating a state evolution feature and a component correlation feature, and carrying out the fault diagnosis of the target production line of the intelligent factory; and inputting the state evolution characteristics and the component association characteristics into a pre-trained fault prediction model for fault prediction, and generating diagnosis result data containing fault risk levels. And the potential fault type and the propagation characteristic information are identified according to the diagnosis result data, and finally the maintenance guidance data containing the fault positioning identifier are generated based on the potential fault type and the propagation characteristic information and are transmitted to the factory operation and maintenance system to trigger the fault intervention operation, so that the accuracy and the maintenance efficiency of intelligent factory fault diagnosis are effectively improved.

The invention discloses a brain disease classification method and system. Precise diagnosis is realized through multi-modal data fusion and dynamic modeling. The method comprises the following steps: collecting multi-modal brain image information and cognitive behavior information of a user; performing dynamic function connection analysis on the resting state functional magnetic resonance time sequence signal to obtain a time-varying brain network feature matrix, and performing white matter fiber bundle topology reconstruction on a structure connection matrix; constructing a four-dimensional correlation tensor by using the time-varying network features, the structural connection weights and the anatomical features through a neurodynamic model; performing multi-task learning on the four-dimensional correlation tensor based on a time-varying graph neural network model, and outputting a quantitative diagnosis result; and finally generating a clinical classification report integrating the individualized brain network remodeling target, the disease progress risk layering and the treatment response prediction. By dynamically fusing the structure and functional features, comprehensive characterization of the pathological mechanism of the brain disease is realized, and decision support with both accuracy and interpretation is provided for clinical diagnosis.

The invention discloses an AI-based traditional Chinese medicine dialectical auxiliary diagnosis method and system and a medium thereof, and relates to the technical field of intelligent auxiliary diagnosis, and the method comprises the steps: collecting physiological monitoring data, tongue condition data and pulse condition signals of a patient, obtaining patient symptom information, mapping the patient symptom information to a preset traditional Chinese medicine pathogenesis classification model, and generating an initial symptom feature vector; calling a knowledge base containing traditional Chinese medicine prescription rules and compatibility medication taboo, performing data space-time alignment processing and dynamic combination calculation on the initial symptom feature vector, and generating a personalized candidate prescription set conforming to the traditional Chinese medicine compatibility taboo; according to dynamic combination calculation, a solution algorithm based on a constraint satisfaction problem is adopted, and multi-dimensional association rule matching is carried out in combination with the mapping relation between syndromes and modern medical symptoms; and outputting a prescription medication recommendation scheme containing traditional Chinese medicine compatibility, dosage and decoction methods based on the candidate prescription set. The method supports the formulation of an accurate personalized diagnosis and treatment scheme, and meets the requirements of high-quality traditional Chinese medicine personalized auxiliary diagnosis and treatment.

The invention discloses an intelligent early warning diagnosis method based on an intelligent power plant, and the method comprises the steps: detecting an abnormality through employing an unsupervised learning method, and correcting a detection result in combination with a heuristic rule; adjusting the length of a dynamic time window according to the running state of the equipment, ensuring the requirement of data trend analysis, carrying out smoothing processing on trend data captured in the adjusted time window, and extracting fault trend characteristics; and performing abnormal distribution analysis on the fault trend characteristics, performing layered early warning on the faults according to abnormal levels, constructing a step-by-step troubleshooting model based on a Bayesian network, and analyzing fault causes step by step. According to the intelligent early warning diagnosis method, the influence of equipment faults on a power plant system can be effectively reduced, and the overall safety and stability of a power plant are improved.

The invention relates to an equipment fault mode identification and diagnosis method based on deep learning, and aims to improve the accuracy and generalization ability of equipment fault diagnosis. A vibration signal, a temperature signal, an acoustic signal, a current signal and image data of equipment are synchronously acquired through a multi-modal data acquisition system, and weighted fusion is performed on different modal data by adopting a self-adaptive multi-head attention mechanism. And then, performing time sequence modeling by using a bidirectional long-short term memory network (BiLSTM), and finally outputting a fault type and a fault saliency map to help operation and maintenance personnel to position a fault area. And through a transfer learning technology, the adaptability and diagnosis precision of the model under different equipment and working conditions are further improved. The method can be widely applied to fault diagnosis and intelligent operation and maintenance of various devices, and the operation reliability and the maintenance efficiency of the devices are effectively improved.

The invention provides an equipment health examination method and system based on multi-agent collaboration. The method comprises the following steps: a data acquisition step: acquiring target data to generate a multi-dimensional feature vector; the target data comprises frequency data, process data and control data; an equipment monitoring step: monitoring the equipment based on the generated multi-dimensional feature vector, and outputting a monitoring result; and a deployment processing step: deploying and implementing a corresponding processing scheme according to a monitoring result. The invention provides an equipment early warning and diagnosis method and system based on multi-agent cooperation, and the method comprises the steps: combining the advantages of a professional mechanism model, an AI model and a large language model through the multi-agent cooperation of feature extraction, threshold adjustment, fault diagnosis, decision generation, summary review and closed-loop feedback; the problems that a traditional method is single in model, high in false alarm rate, lagged in decision and the like are solved.

The invention discloses a multi-modal data fusion power equipment health state diagnosis method and system, and belongs to the field of power equipment state monitoring, and the method comprises the steps: S100, collecting infrared thermal imaging data, vibration signals, current harmonic data and partial discharge signals of power equipment, and carrying out the time synchronization and space registration; and S200, acquiring infrared thermal imaging data, vibration signals, current harmonic data and partial discharge signals, and inputting the infrared thermal imaging data, the vibration signals, the current harmonic data and the partial discharge signals into the dynamic weight fusion model to obtain an equipment health state score and a fault type. And S300, according to the equipment health state score and the fault type, triggering grading alarm. According to the invention, timely early warning can be carried out on potential fault hidden dangers of power equipment.

The invention relates to a power equipment fault diagnosis method based on time sequence-spectrogram-discrete data multi-modal fusion, and belongs to the field of power equipment fault diagnosis. A multi-modal feature fusion technology based on an attention mechanism is adopted, and a power equipment fault diagnosis model based on multi-modal information fusion is constructed. Compared with a traditional deep learning fault diagnosis method, the problem that a fault single-modal sample is insufficient in diagnosis model accuracy can be solved through multi-modal data fusion, and the universality and diagnosis precision of the model are improved.

The invention relates to the technical field of electrical fault detection, in particular to an electromechanical equipment health state diagnosis method and device based on machine learning, and the method comprises the following steps: collecting the current, voltage and temperature parameters of equipment, calculating the real-time average value and standard deviation of each parameter through numerical processing, and obtaining a parameter monitoring result; and monitoring a result based on the parameters. The current, voltage and temperature parameters of the equipment are collected in real time, numerical processing is carried out on each parameter, the real-time average value and the standard deviation are calculated, and accurate basic data support is provided for state monitoring. Based on the monitoring data, parameter abnormity is judged through threshold analysis, abnormal changes can be dynamically recognized in the equipment operation process, and the timeliness and accuracy of fault discovery are improved. In combination with past performance data, the future change trend of parameters is predicted through a regression algorithm, early warning for equipment operation can be formed, and problem expansion or out-of-control is avoided.

The invention provides an enterprise intelligent diagnosis method, system and device based on a large model and a medium, and belongs to the technical field of enterprise diagnos.The method comprises the steps that enterprise heterogeneous data are collected through a multi-source data interface, cleaning, feature extraction and cross-modal alignment fusion are carried out, and enterprise real-time data are obtained; constructing a knowledge graph through a graph attention network on the basis of an industry index to which an enterprise belongs, and updating association weights among nodes at regular time; inputting enterprise real-time data into the pre-trained multi-modal large model for preliminary analysis, and outputting a risk thermodynamic diagram; key abnormal indexes are identified from the risk thermodynamic diagram, sub-graphs related to the key abnormal indexes are extracted from the knowledge graph, structured prompt words are generated from the sub-graphs, then the structured prompt words and standardized enterprise real-time data are jointly input into a multi-modal large model for joint reasoning analysis, and then a visual diagnosis report is generated. Accurate identification and intelligent diagnosis of enterprise risks are realized, and decision-making efficiency is improved.

The invention discloses a rolling bearing fault diagnosis method based on a multi-scale residual attention network and an adaptive Transform encoder. The rolling bearing fault diagnosis method comprises the following steps: acquiring original vibration data in the running process of a rolling bearing; segmenting the collected original vibration data into samples with specified lengths, and dividing the samples into a training data set and a test data set; inputting the training data set into a multi-scale residual attention network to perform preliminary multi-scale feature extraction; inputting the feature information extracted by the multi-scale residual attention network into an adaptive Transform encoder to obtain time sequence features; finally obtained feature information is subjected to GAP processing and then is input into a Softmax layer for fault diagnosis; the forward propagation calculation and the back propagation calculation are repeatedly executed to optimize model parameters until the diagnosis accuracy and loss of the training data set reach a stable level; and inputting the test data set into the trained model for fault diagnosis, and determining the health condition of the rolling bearing. According to the method, the adaptability and the diagnosis accuracy in time sequence dependence scenes such as rolling bearing fault diagnosis are enhanced.

The invention discloses a variable working condition rolling bearing fault diagnosis method and system, and the method comprises the steps: firstly obtaining bearing vibration signal data, temperature data and current spectrum data of a motor under different working conditions, carrying out the adaptive decomposition through employing an EEMD algorithm, and extracting a stable feature subset; then, constructing a cross-working-condition diagnosis model by utilizing transfer learning, and introducing an HMM (Hidden Markov Model) time sequence model to capture signal dynamic characteristics; during diagnosis, the working condition of the signal is firstly judged, and the corresponding model is selected for fault diagnosis. And if the diagnosis confidence is low, triggering an online updating mechanism, and performing incremental learning by using new data. Through continuous data collection and model updating, a dynamic evolution system capable of automatically adjusting the diagnosis strategy according to the working condition change is constructed, the problem that the diagnosis performance is reduced due to the working condition change in bearing fault diagnosis is effectively solved, and high-accuracy self-adaptive diagnosis is achieved.

The invention discloses a server health state diagnosis method based on a GAT-LP algorithm, and relates to the technical field of server health diagnosis, and the method comprises the steps: collecting multi-dimensional operation state data of a server, transmitting the multi-dimensional operation state data to a data analysis platform, and carrying out the preprocessing, inputting the preprocessed historical operation state data of the server into a GAT-LP network for training to obtain a GAT-LP network model, evaluating health conditions of the server and the process and generating an overall health condition according to a health state diagnosis result of the server and health score features extracted based on the GAT-LP network model, converting an evaluation result into a visual health state, and displaying the visual health state in the GAT-LP network model. And triggering an alarm mechanism when the health state of the server is lower than a preset threshold value. By constructing a multi-dimensional health assessment index system, the running state of the server is visually displayed, potential risks are found in advance, and prevention measures are made.

The invention provides a bearing fault diagnosis method and system, a storage medium and a computer, and the method comprises the steps: obtaining a bearing vibration signal, and extracting time-frequency features in the bearing vibration signal according to variational mode decomposition and fast Fourier transform; performing convolution operation on the time-frequency features, and processing a convolution output result by adopting an ECA attention mechanism to extract frequency domain features in the time-frequency features; capturing a global time sequence dependency relationship in the time-frequency features based on a Transform model to extract long-distance dependent time domain features in the time-frequency features; and fusing the extracted frequency domain features and time domain features through a bidirectional cross attention mechanism, transmitting the fused features into a full connection layer for classification, outputting fault categories of the bearing, converting a classification result into probability distribution, and selecting the category with the highest probability as a diagnosis result. The bearing fault diagnosis method provided by the invention is high in fault diagnosis accuracy.

The invention discloses an intestinal flora and obesity personalized diagnosis method based on a graph attention network, and relates to the technical field of obesity diagnosis, and the method comprises the following steps: collecting gene data, building a graph structure-based model for the data to predict obesity risk and provide personalized health suggestions, each piece of microbiome data is expressed as a weighted undirected graph, a microbiome graph is constructed, the association strength between microbiome groups is calculated, and the microbiome data is expressed as a graph structure. According to the method, the graph structure model considering the relevance between the microbiome groups is constructed, the self-adaptive graph convolutional network and the heterogeneous graph attention network are utilized, multi-modal information interaction between the microbiome and clinical data is deeply mined, the processing capacity of high-dimensional and complex microbiome data is improved, and the multi-modal information interaction between the microbiome and clinical data is improved. The accurate prediction of obesity risks and the provision of personalized health suggestions are realized, the prediction accuracy and generalization ability of the model are significantly improved, and a more effective personalized obesity diagnosis effect is achieved.

The invention discloses a diagnostic method for performance detection of a pressure sensor, and relates to the technical field of sensor equipment. Comprising the following steps: initializing a pressure sensor, sensitivity and response time under different pressures, dynamic response characteristics in a rapidly changing pressure environment, changes under different temperature conditions, anti-interference capability to external factors, long-time stability, and existence of non-linear errors and zero drift; and the overall performance of the pressure sensor is comprehensively evaluated. According to the diagnosis method, the dynamic response, the anti-interference capability, the temperature adaptability and the long-term stability of the sensor can be comprehensively quantified by setting the dynamic response characteristic model and multi-working-condition environment simulation. According to the method, neglected nonlinear dynamic behaviors in a traditional evaluation means can be captured, the potential performance problem of the sensor in a complex working environment can be effectively revealed, optimization design and application deployment are guided, and the reliability and precision of the sensor in an actual scene are remarkably improved.

The invention belongs to the field of power system engineering, and discloses a power distribution automation terminal diagnosis method and system based on multi-source wave recording feature fusion, and the method comprises the steps: obtaining the electric quantity data and equipment operation state data collected by a power distribution automation terminal; performing adaptive decomposition on the electrical quantity data by using a variational mode decomposition algorithm to obtain an intrinsic mode function; constructing a deep residual network model, carrying out fusion analysis on the time-frequency domain features of the intrinsic mode function, and generating a fault feature vector; establishing a multi-dimensional evaluation matrix based on the fault feature vectors, and integrating a plurality of indexes to output fault types and credibility scores; according to the fault type and the credibility score, generating a fault isolation strategy based on a Petri network model; and executing a dynamically adjusted self-adaptive self-healing control algorithm. According to the method, complex and changeable fault modes can be effectively identified, a complete collaborative verification mechanism is formed, seamless connection from fault diagnosis to self-healing control is realized, and the operation reliability of the power distribution network is remarkably improved.

The invention discloses a complex scene multi-source heterogeneous data acquisition and monitoring diagnosis method and device, and the method comprises the steps: collecting multi-source heterogeneous data of an object to be monitored and diagnosed, and carrying out the data cleaning, data preprocessing, feature extraction and fusion of the multi-source heterogeneous data, so as to obtain a feature data set; and constructing and training a causal decoupling representation learning model based on input and output driving by using the feature data set and the data after data cleaning, so as to extract decoupling representation of a to-be-monitored diagnosis object, deduce a causal relationship between variables, and obtain a to-be-monitored diagnosis object. Wherein the causal decoupling characterization learning model comprises an external source control conversion network and a double-view evolution modeling and causal inference network, and the trained causal decoupling characterization learning model is used as a heterogeneous signal monitoring and diagnosis model and is used for monitoring the state of an object to be monitored and diagnosed and diagnosing the root cause of a fault of the object to be monitored and diagnosed. According to the invention, data acquisition, state monitoring and fault reason diagnosis can be integrally realized, the cost is saved, and the application prospect is wide.

The invention provides an artificial intelligence auxiliary diagnosis method and system for a knee joint, and the method comprises the steps: synchronously collecting a multi-angle dynamic X-ray image and six-degree-of-freedom contact force distribution data in an articular cavity, dynamically tracking a patella sliding track, and generating a mechanical phase track; a personalized knee joint model is constructed in combination with the biological parameters of the lower limbs of the patient, the mechanical phase trajectory and the contact force data vector are superposed to generate a dynamic mechanical fingerprint, and then the dynamic mechanical fingerprint is mapped into a biomechanical attribute map; electromyographic signal time-frequency characteristics and tibia rotation angles in the movement process are collected, an electromyographic activation mode is constructed, and the electromyographic activation mode and a biomechanical attribute map are subjected to characteristic fusion to form a joint dynamics comprehensive contour; analyzing a stress wave conduction path by using a joint anomaly dynamic propagation model to identify a compensatory injury mode; a diagnosis map is generated according to the recognition result, a preset biomechanical constraint condition is matched to correct the diagnosis map, and an individualized rehabilitation strategy is formulated. The treatment effect is improved, and the recovery speed of a patient is increased.

The invention discloses a fault diagnosis method and device, a medium and a product, and relates to the technical field of data processing. Multi-dimensional data such as text logs, time series data, topological graph structures, operation records and environmental parameters are obtained, various factors possibly involved when the fault occurs are included, different types of data can complement and verify each other, the fault features are accurately described, the limitation of single data type analysis is avoided, and the analysis efficiency is improved. And the accuracy of fault diagnosis is effectively improved. Then converting different types of multi-dimensional data into target feature representations, matching the target feature representations with data in a historical fault case library, and when a historical fault of which the matching degree is greater than a preset matching threshold value is found, determining a diagnosis conclusion and a repair scheme of the historical fault as a diagnosis conclusion and a repair scheme of the current fault. And each new fault does not need to be analyzed and reasoned, so that the fault diagnosis time is shortened, and the fault diagnosis efficiency is improved.

The invention provides a valve leakage diagnosis method and system based on an improved Hereum algorithm optimized SVM (Support Vector Machine), and relates to the technical field of valve leakage diagnosis. Decomposing the collected original signal to obtain a limited number of IMF components; calculating a fuzzy entropy, and constructing a feature vector; taking the feature vectors as data samples, and dividing the data samples into a training set, a verification set and a test set; using the training set to train the SVM model, and according to the classification error rate of the verification set on the trained SVM model, performing iterative optimization on the parameters of the SVM model based on an improved He-Ma algorithm to obtain an optimized SVM model; inputting the test set into the optimized SVM model to obtain a valve leakage diagnosis model; and acquiring a new acoustic emission signal, and inputting the new acoustic emission signal into the valve leakage diagnosis model to obtain a diagnosis result. The method not only can improve the classification accuracy and generalization ability of the diagnosis model, but also can enhance the stability of the model under different working conditions and environmental noise.

The invention, which relates to the technical field of transformer monitoring, discloses a digital cloud-based online monitoring and diagnosis method comprising the following steps: acquiring real-time operation parameters of a transformer; uploading the real-time operation parameters to a cloud platform, and establishing a transformer digital twinborn model; calculating a deviation value between the real-time operation parameter and a preset standard parameter by using a transformer digital twinborn model, and generating a transformer health state index; based on the transformer health state index, adopting an adaptive multi-mode deep learning framework to predict the health state of the transformer to obtain a fault prediction result; and formulating a preventive maintenance strategy according to the fault prediction result and pushing the preventive maintenance strategy to the terminal equipment. According to the method, the transformer digital twinborn model is constructed, so that the operation state of the transformer is accurately simulated and monitored in real time, and the accuracy of state evaluation is improved.

The invention provides an AI-based servo driver fault on-line rapid diagnosis method and system, and the method achieves the quantitative evaluation of fault data through obtaining the candidate system fault monitoring data of a servo driver control system, and converting the candidate system fault monitoring data into a diagnosis difficulty index and a fault description knowledge attribute. When the diagnosis difficulty index exceeds the preset index and the fault data lacks dynamic change information, the method can further utilize a plurality of fault penetration derivation data and the associated fault knowledge networks matched with the data, and effectively identifies and integrates similar fault modes by calculating the state feature distance between the associated fault knowledge networks, thereby improving the fault diagnosis accuracy. Compared with the prior art, the method has the advantages that the accuracy and efficiency of fault diagnosis are remarkably improved, particularly in a complex and changeable fault scene, the fault reason can be quickly positioned, and data support is provided for instant maintenance and recovery of the servo driver, so that the downtime of a system is shortened, and the overall operation reliability and stability of equipment are improved.

The invention discloses a multi-granularity knowledge graph auxiliary diagnosis method based on DeepSeek and Agent. The multi-granularity knowledge graph auxiliary diagnosis method comprises the following steps: step 1, receiving electronic medical record text data and medical image data of a patient; 2, constructing a knowledge graph; updating the knowledge graph every day to reflect the latest medical research result; step 3, analyzing the text data of the electronic medical record through DeepSeek-R1; 4, extracting spatial structure feature nodes of the medical image data through a multilayer three-dimensional convolution kernel; the method comprises the following steps: segmenting medical image data into sequence blocks through a Vision Transform; 5, the output of the DeepSeek-R1, the output of the multi-layer three-dimensional convolution kernel and the output of the Vision Transform are input into a multi-modal fusion module; step 6, outputting a high-confidence diagnosis conclusion and probability distribution; and step 7, generating an intelligent report of the structured text. According to the method, by combining natural language processing, computer vision and the knowledge graph technology, accurate and efficient medical examination is achieved.

The invention discloses a multi-module collaborative rapid electrocardiogram automatic diagnosis method and system, and belongs to the technical field of automatic diagnosis. The method comprises the following steps: preprocessing an original ECG signal; the preprocessed ECG signals serve as input, and after the preprocessed ECG signals are sequentially processed by a lightweight convolution sub-module, a deformation convolution sub-module and a non-local attention sub-module, multi-level features containing local details and global time sequence dependency are output; fusing the multi-level features, and sending the fused multi-level features into a classifier to complete prediction of heart rhythm types to obtain prediction probabilities of all categories; a sliding window mode is adopted, the ECG signal fragments with the fixed length are sent to the diagnosis model for reasoning, the diagnosis model continuously outputs diagnosis results, and alarm or diagnosis prompt information is output in time according to the diagnosis results. According to the method, by designing a lightweight network structure, the parameter quantity and calculation complexity of a diagnosis model are effectively reduced, and real-time online ECG signal automatic diagnosis is realized on the premise of ensuring high diagnosis accuracy.