96 results about "Diagnosis recommendations" patented technology

The invention relates to the technical field of image processing, in particular to an intelligent fracture diagnosis system based on image recognition, which comprises an image analysis module, a mode recognition module, a form analysis module, a risk assessment module and an auxiliary decision module. According to the method, skeleton gray level distribution and boundary consistency are analyzed through continuous frames of X-ray images, fracture feature extraction precision and time sequence coherence are improved, key point space distribution, symmetry standards and form proportions are fused, fracture area structured quantitative evaluation is achieved, the form change trend and abnormal offset point screening are combined, and the accuracy of fracture feature extraction is improved. The method enhances abnormal trajectory recognition precision, associates bone mineral density and form offset, calibrates high-risk time periods, improves risk assessment perspectiveness and individual adaptability, dynamically corrects output content according to an inter-frame suggestion change trend and extended feedback, enhances timeliness of diagnosis suggestions and closed-loop feedback quality, and improves risk assessment accuracy. Image evolution, structural geometry and physiological data are integrally fused, and multi-dimensional intelligent judgment of fracture recognition and evaluation is achieved.

The invention relates to the technical field of medical systems, in particular to an intelligent gastrointestinal postoperative intestinal obstruction diagnosis and intervention decision-making system and method, and the system comprises a data preprocessing module, a multi-modal data fusion module, a gastrointestinal function recovery model construction module, a self-adaptive progressive reinforcement learning optimization module AHRL and a diagnosis and intervention recommendation module. The self-adaptive progressive reinforcement learning optimization module optimizes an intervention strategy layer by layer through a five-layer progressive algorithm from the steps of dynamic adjustment of feature weights, recursive state propagation, layered accumulation optimization, symptom similarity matrix generation, progressive weight reverse feedback and the like, deep fusion of key symptoms by multi-modal data is achieved, and the accuracy of the key symptoms is improved. And the intervention scheme is updated in real time according to the patient state change. In the intervention decision recommendation module, the system generates personalized diagnosis suggestions based on the optimized strategy, carries out real-time risk assessment, and provides continuous and dynamic intervention support for the patient.

The embodiment of the invention provides a method and system for generating medical suggestions based on multi-modal data fusion, and the method comprises the steps: integrating a medical image, a physical examination report and dynamic physiological parameters of a patient through a multi-source data fusion module, generating a multi-modal data set, and synchronously inputting the multi-modal data set into a hybrid reasoning module and a dynamic knowledge graph engine. And the dynamic knowledge graph engine accurately recall a target diagnosis and treatment guide associated with the current multi-modal data set. The rule reasoning sub-module generates a first diagnosis suggestion containing a diagnosis conclusion, a treatment scheme and an evidence level based on a guide structured rule, and meanwhile, the neural network reasoning sub-module analyzes a multi-modal data set by relying on a triple topological structure and an edge weight; and generating a second diagnosis suggestion comprising the disease risk probability, the differentiated treatment suggestion and the evidence source. And finally, the interactive output module fuses the two suggestions to generate a medical suggestion report covering the diagnosis basis, the evidence level and the treatment scheme, so that the diagnosis and treatment precision of chronic disease management and health risk assessment is remarkably improved.

The invention discloses a multi-agent large-model disease diagnosis knowledge reasoning system based on data dual drive, and relates to the technical field of artificial intelligence assisted medical diagnosis. The system collects patient symptom follow-up records, laboratory test results, observation diagnosis probabilities and expert diagnosis recommendation results in a multi-source manner; time sequence evolution characteristics are extracted, a time sequence diagnosis sensitivity coefficient is calculated, and early recognition of disease risks is achieved; in combination with anti-fact simulation and statistical reasoning, a causal consistency coefficient is obtained and is used for verifying causal reasonability of observation diagnosis and contrast results; based on agent group consensus analysis, calculating a game consistency coefficient for judging the credibility of a diagnosis conclusion; positioning and multi-level verification are carried out on abnormal reasoning steps and knowledge fragments, so that the reliability and safety of a result are guaranteed; continuous optimization of the diagnosis model is realized through a log analysis and knowledge backflow mechanism; according to the invention, the accuracy, interpretability and safety of disease diagnosis can be obviously improved.

The invention discloses a nervous system disease auxiliary evaluation method based on a large model and related equipment. The method comprises the following steps: constructing a structured knowledge graph based on multi-modal heterogeneous data of nervous system disease guide texts, symptom descriptions and examination results; a graph neural network module is embedded based on the structured knowledge graph in combination with a large language model, an inference network model of a dynamic graph structure is generated, and a dynamic diagnosis path tree is formed by calculating the relation weight between the medical nodes in the graph; and generating interpretable diagnosis suggestions through the reasoning network model according to the current symptom description, the examination result and the historical medical history of the patient. The problems that an existing model still has obvious bottlenecks and clinical challenges, depends on single modal data, is insufficient in quantification of dynamic signs such as a reasoning level, tremor frequency and gait abnormity, does not achieve disease development time sequence modeling and is difficult in clinical conversion can be solved.

The invention relates to the technical field of artificial intelligence and medical decision systems, and discloses a reasoning agent construction method and system for intelligent medical treatment guidance, and the method comprises the steps: constructing a hierarchical modal completion network and a modal correlation knowledge graph; constructing a medical feature cross-modal mapping network based on comparative learning, and mapping different modal medical data to a shared feature space; according to the uncertainty of the complemented data, constructing an uncertainty quantitative model and automatically adjusting a diagnosis confidence threshold; aiming at different disease types and symptom combinations, constructing a disease modal incidence matrix and a modal reliability evaluation network; through weighted voting, evidence convergence and specialist authority evaluation, cross validation and collaborative decision-making of multiple specialist knowledge are realized, and a final diagnosis suggestion is formed; according to the invention, the problem of limited diagnosis capability caused by lack of medical data in a medical resource limited environment is solved.

The invention provides an equipment fault diagnosis recommendation method based on a knowledge graph and a multi-dimensional relevance index, and the method comprises the steps: converting a fault text into triple structured data based on an equipment fault knowledge graph ontology model, and constructing an equipment fault knowledge graph; constructing a multi-dimensional correlation index of the fault mode, and converting explicit feedback in the historical fault diagnosis data into implicit feedback; obtaining a fault mode which is strongly correlated with the abnormal phenomenon from historical fault diagnosis data based on a multi-dimensional correlation index, and taking the fault mode as a seed fault mode; and taking the seed fault mode as a starting point, iteratively propagating in the fault knowledge graph to obtain a ripple set, calculating an association probability between an abnormal phenomenon and a candidate fault mode according to the ripple set, accumulating each order of response according to the association probability, and outputting a prediction definite diagnosis probability. According to the method, the diagnosis relation strength between the abnormal phenomenon and the fault mode is revealed according to the multi-dimensional relevance index, and a flexible reasoning mechanism is adopted on the basis, so that the diagnosis accuracy and adaptability are improved.

The invention discloses an intelligent electronic medical record generation management system and method, a terminal and a medium. The generation management system comprises a data acquisition module, a template generation module, an intelligent prompt module, an editing and saving module, a data security protection module and a permission retrieval recording module. The generation management method comprises the following steps: performing identity permission verification; integrating to form multi-modal structured data, and screening and matching to generate a template framework; a diagnosis suggestion sorting table is generated, and data is stored and backed up; transmitting and storing data, and recording operation behavior data. According to the invention, real-time automatic acquisition of basic information and examination results of patients is realized; a medical record template is automatically matched and optimized based on disease characteristics of a patient and an examination result, so that the workload of a doctor is reduced, and the medical record structure conforms to clinical specifications; transmission and storage data are encrypted in a layered mode, current account information operation behavior data are recorded based on a timestamp, and safe storage and real-time management of medical record data are effectively ensured.

The invention discloses a cervical lesion intercellular relation modeling and analysis system based on a graph neural network, and the system comprises a medical image collection module which is used for collecting a digital image of a cervical tissue pathological section or a cervical TCT slide; the cell detection and segmentation module is used for extracting spatial position information and morphological characteristics of cells; the cell feature extraction module is used for extracting and fusing the spatial position, morphology, texture and biological marker features of the cells; the cell relation graph construction module is used for constructing a heterogeneous cell relation graph with cells as nodes and inter-cell relations as edges; the graph neural network analysis module is used for carrying out feature learning and modeling on the heterogeneous cell relation graph; the intelligent auxiliary diagnosis module is used for generating auxiliary diagnosis suggestions; and the data management and automatic control module is used for realizing automatic control and case data management of the whole process of the data. The intelligent and automatic level of cervical lesion cell analysis can be comprehensively improved, and the accuracy and efficiency of diagnosis are improved.

The invention relates to clinical medicine, in particular to an AI-based auxiliary diagnosis method, which comprises the following steps of: collecting a large amount of clinical data, and processing the clinical data; key features are extracted from the clinical data, and the clinical data are labeled to construct a training set, a verification set and a test set; performing model training on the auxiliary diagnosis model by using the training set to obtain a trained auxiliary diagnosis model; deploying the trained auxiliary diagnosis model into a medical information system so as to process new clinical data in real time and give corresponding diagnosis suggestions; according to the technical scheme provided by the invention, the defects that the diagnosis accuracy and efficiency cannot be effectively improved and the workload of doctors cannot be reduced in the prior art can be effectively overcome.

The invention relates to the technical field of medical image diagnosis, and discloses a cardiovascular disease diagnosis model construction method based on image processing. The method comprises the steps that cardiac medical image data of a target patient is collected, a standardized data set is generated through preprocessing, and a morphological and hemodynamic feature set is extracted; establishing a heart state evolution characteristic spectrum according to a characteristic dynamic evolution rule, dividing a pathological state space, and calculating the characteristic distribution density of a historically diagnosed case; acquiring real-time image data of a patient to be diagnosed, and constructing a real-time diagnosis feature vector; mapping the vector to a pathological state space, and calculating a space matching degree to generate a pathological association index; and combining the association index and the two types of feature sets to construct a heart pathology probability prediction model, outputting a pathology probability prediction value and generating a hierarchical diagnosis suggestion. According to the method, through multi-dimensional feature analysis and space matching analysis, precise and graded diagnosis of the cardiovascular diseases is realized, and an efficient and feasible technical path is provided for diagnosis of the cardiovascular diseases.

The invention relates to the field of rail transit vehicle intelligent diagnosis, in particular to a train fault intelligent auxiliary diagnosis method fusing multi-source knowledge, which comprises the following steps of: constructing a multi-source knowledge document library, and performing text cleaning, segmented disassembly and vectorization processing to obtain a vector database; then obtaining a fault alarm list and capturing event information, and performing diagnosis according to the vector database to obtain a diagnosis suggestion; then time sequence operation parameters before and after the fault are extracted and analyzed, and a trend chart and characteristic indexes are generated to obtain an analysis result; constructing a multi-dimensional auxiliary analysis mechanism based on the diagnosis suggestion and the analysis result, and generating a maintenance suggestion in combination with historical maintenance work order information; and finally, generating a comprehensive auxiliary diagnosis report according to the maintenance suggestion, automatically generating a maintenance dispatching task list, determining a task execution sequence according to a preset priority scheduling algorithm, and executing the maintenance dispatching task list. The accuracy and timeliness of train fault diagnosis can be improved, and the safety, the operation and maintenance efficiency and the intelligent level of rail transit equipment are improved.

The invention relates to a voice interaction large model family health assistant dialogue method and device, equipment and a medium. The method comprises the following steps: carrying out fragmentation processing according to an original voice stream of a user to generate an audio fragment with a medical mark, and carrying out voice recognition and entity extraction on the audio fragment to generate a dynamic entity map; generating an evidence-based decision prompt based on the map, inputting the prompt into a preset medical big model for processing, and outputting a result containing an essential symptom list; and according to the symptom matching degree of the necessary symptom list and the dynamic entity map, generating a diagnosis report or a question-asking list, if the diagnosis report is output, performing medical rule chain verification operation on the diagnosis report to generate a quality control report, and based on the question-asking list or the quality control report, generating a synthetic voice stream. According to the method, through medical intention directional screening, map entity analysis, large model diagnosis, voice synthesis and the like, the voice recognition accuracy, the diagnosis suggestion reliability and the inquiry interaction efficiency of the family health assistant in the medical scene are improved.

The invention relates to the technical field of medical health intelligent assessment, in particular to a patient nutrition risk intelligent assessment system based on multi-parameter fusion. The system comprises a data acquisition module for acquiring nutrition-related indexes of a patient; the conflict detection module is used for establishing a parameter logic constraint rule base and generating a conflict parameter matrix; the evidence fusion module is used for carrying out fusion processing on the contradictory evidence by adopting an improved Dempster-Shafer evidence theory; the knowledge distillation module is used for realizing parameter complementation through a multi-hop propagation inference mechanism of a nutrition parameter association diagram; the uncertainty quantization module is used for establishing a causal inference graph to quantify the contradictory contribution degree; and the diagnosis suggestion module is used for generating clinical examination suggestions. According to the method, the problem that a traditional method cannot be used for evaluation due to nutrition parameter deficiency and metabolic index contradiction of a patient is solved, and the accuracy and practicability of nutrition risk evaluation are improved through deficiency parameter inference, contradiction evidence fusion and causal root tracing.

The invention discloses an emergency rescue room multi-parameter collaborative diagnosis and treatment system based on intelligent chip operation, and relates to the technical field of emergency rescue. The system comprises a data acquisition and preprocessing module, an intelligent chip operation module, a diagnosis decision support module, a multi-department collaboration module, a data storage and management module and a user interface module. According to the system, multi-parameter data is collected and integrated in real time, analysis is carried out by utilizing the rapid operation capability of the intelligent chip, accurate diagnosis suggestions and treatment schemes can be provided for medical staff in a short time, the diagnosis and treatment decision time is greatly shortened, and the emergency rescue efficiency is improved. Various physiological parameters are comprehensively analyzed, the limitation of single parameter analysis is avoided, the illness state of a patient can be judged more comprehensively and accurately, and misdiagnosis and missed diagnosis are reduced.

The invention provides a diagnosis recommendation method and device, equipment, a storage medium and a program product. The diagnosis recommendation method comprises the steps that target specific diagnosis corresponding to a region where a patient is located is determined; wherein the target specific diagnosis refers to historical diagnosis with distinction degree from other regions in the region where the patient is located; determining a target historical medical record similar to the to-be-diagnosed medical record of the patient, and obtaining a target historical diagnosis corresponding to the target historical medical record; determining candidate diagnoses based on the target historical diagnoses and the target specific diagnoses; and based on the candidate diagnosis, determining a diagnosis recommendation result corresponding to the to-be-diagnosed medical record. The accuracy of diagnosis recommendation can be improved.

The invention relates to the technical field of medical information processing, and discloses an acute pulmonary embolism auxiliary diagnosis decision-making system based on a large model, and the system comprises a data collection and preprocessing module which is used for receiving multi-source clinical data and preprocessing the multi-source clinical data; the large model reasoning module is used for inputting the preprocessed multi-source clinical data into a large model fused with a multi-modal causal attention and identification adversarial learning mechanism, and outputting a reasoning result; the dynamic learning module is used for accessing desensitized case data of a hospital electronic medical record system, continuously learning new cases through federal learning, and optimizing the recognition capability of a large model pair; the diagnosis suggestion generation module is used for generating structured diagnosis suggestions according to the reasoning result output by the large model; the result output and interaction module is used for displaying diagnosis suggestions to doctors and updating reasoning results in real time by a large model when the doctors supplement data; the diagnosis efficiency is improved.

The invention discloses a clinical diagnosis and treatment path guiding system and method. Specifically, the clinical diagnosis and treatment path guiding system comprises an input module, a dynamic diagnosis reasoning module and a display module. The input module is used for acquiring medical record data of a patient; the dynamic diagnosis reasoning module is used for obtaining diagnosis suggestions of the patient based on the medical record data of the patient; the diagnosis suggestions are updated based on the new clinical data until the determined disease diagnosis is obtained or all examinations are completed; the examination result area is used for displaying examination items corresponding to the diagnosis suggestions in response to confirmation information of the diagnosis suggestions and obtaining input information of examination results corresponding to the examination items so as to obtain new clinical data; the diagnosis suggestion area is used for displaying diagnosis suggestions updated in real time. The guiding path obtained through the guiding system is dynamic, and the guiding system can reduce the burden of medical staff, optimize the diagnosis and treatment process and improve the diagnosis accuracy.

The invention discloses a traditional Chinese medicine cross-language auxiliary system based on artificial intelligence, and the system comprises a multi-language real-time translation module which supports the input and output of multi-language voices and texts, carries out the optimization of traditional Chinese medicine terms and cultural contexts, and achieves the real-time language conversion between a doctor and a patient; the body parameter acquisition module is integrated with various sensors so as to acquire physiological parameter information of a user; the intelligent diagnosis and analysis module is used for analyzing the collected physiological parameter information and user description information in combination with a traditional Chinese medicine knowledge base and a deep learning model and generating traditional Chinese medicine syndrome differentiation and diagnosis suggestions; and the data interaction module is used for carrying out data interaction and sharing with external equipment. Through the light, thin and portable design, multi-language real-time translation and intelligent diagnosis analysis functions, efficient auxiliary support is provided for traditional Chinese medicine diagnosis, and the cross-language communication problem and the diagnosis data integration problem are solved.

The invention discloses a system and method for deducing diabetes based on an error value detection and correction algorithm, and the system comprises a data collection and integration module which is used for collecting multi-source data corresponding to a diabetic patient, and carrying out the format unification and arrangement of the multi-source data; the error value preliminary screening module is used for preliminarily screening abnormal data in the multi-source data; the depth error value correction module is used for correcting the preliminarily screened abnormal data based on a causal inference algorithm to obtain corrected data; the feature extraction and association module is used for extracting potential features related to diabetes in the corrected data and establishing an association model between the features; and the diabetes mellitus inference module is used for inputting the associated feature data into a pre-trained inference model for processing, and the inference model outputs the diabetes mellitus illness probability and related diagnosis suggestions.

The embodiment of the invention provides a collaborative diagnosis method. The method comprises the following steps: acquiring consultation information associated with a first object; then, based on the consultation information, a diagnosis party is triggered to carry out collaborative diagnosis on the consultation information to obtain a diagnosis suggestion, the diagnosis party comprises a second object and an intelligent agent, and the diagnosis suggestion is obtained based on interaction of the second object and the intelligent agent for the consultation information; determining an execution process indicated by the diagnosis suggestion; and triggering a diagnosis party to carry out cooperative judgment on diagnosis and treatment actions generated by the execution process so as to obtain a diagnosis conclusion. Therefore, a multi-party interactive collaborative diagnosis process is realized, deep analysis of difficult symptoms and personalized treatment scheme suggestions are provided through collaborative work of intelligent agents, the problems of single input limitation and limited information processing capability are solved, and the accuracy of the diagnosis process is improved.

A diagnostic device (10) for guiding a doctor in performing diagnostic procedures on a patient's body comprises a main body (12) having a top surface (13) and a bottom surface (14), with one or more user output modules (22) positioned on the top surface (13) and a set of sensors comprising peripheral sensors (16) and diagnostic sensors (18) arranged on the bottom surface (14). A processor (302) executes instructions stored in a memory (306) for receiving and processing preliminary inputs about the patient through a preliminary data acquisition module (307), determining a target body region and starting point, and performing scans to determine boundaries and examine specific organs. The device employs an artificial intelligence / machine learning (AI / ML) engine (310) to generate real-time audio, visual, and haptic instructions for guiding the operator, process multiple data streams, identify abnormalities, and provide diagnostic recommendations.

The invention discloses a clinical surgery medical information sharing system based on big data, which relates to the technical field of medical information sharing and comprises a data acquisition module, a data processing module, a sharing management module and an application analysis module. Patient data quality can be accurately quantified, it is ensured that the data meets the standard, the precision and reliability of the system are improved, the system guarantees the safety of medical data in the transmission and storage process through encryption protection analysis, sensitive information of the patient is prevented from being leaked, meanwhile, the system can conduct intelligent diagnosis recommendation according to the health condition of the patient, and the diagnosis recommendation efficiency is improved. And the patient health score and the doctor professional score are combined to reasonably match the medical resources to meet the patient demand.

The invention discloses a disease auxiliary diagnosis method and device based on reward sorting and online reinforcement learning, and relates to the field of medical data processing.The disease auxiliary diagnosis method comprises the steps that a fine adjustment data set and a disease auxiliary diagnosis model are constructed, and the disease auxiliary diagnosis model adopts a pre-trained large language model subjected to preliminary fine adjustment; fine-tuning the disease auxiliary diagnosis model again by adopting a fine-tuning data set and a reward sorting fine-tuning mode, so that the disease auxiliary diagnosis model can be continuously learned and improved through self-generated data under an unsupervised condition to obtain a fine-tuned disease auxiliary diagnosis model; and acquiring multi-modal medical data of a patient to be predicted, and inputting the multi-modal medical data into the disease auxiliary diagnosis model subjected to fine adjustment to obtain a corresponding diagnosis suggestion. According to the method, the problems in the aspects of model alignment, human feedback utilization, generation result control, generalization ability, online learning ability and the like in the prior art can be solved.

The present invention provides a guided medical consultation method, system, electronic device and storage medium based on a large language model. By introducing the large language model, the patient's self-reported symptoms can be quickly understood and processed. Compared with traditional manual medical consultation, the consultation time is greatly shortened and the diagnosis and treatment efficiency is improved. By utilizing the knowledge graph of the relationship between traditional Chinese medicine symptoms, the patient's symptoms can be accurately matched with the relevant symptoms in traditional Chinese medicine theory. Through big data analysis and model learning, the accuracy of the medical consultation can be gradually optimized, and more accurate diagnostic suggestions can be provided to the patient. According to the patient's initial feedback, subsequent symptom inquiries can be intelligently raised, simulating the meticulous medical consultation process of traditional Chinese medicine practitioners, ensuring the comprehensiveness, depth and interpretability of the medical consultation.

The invention provides a training method and device for a diagnosis suggestion generation model and electronic equipment, and the method comprises the steps: training a base model through employing a training sample set, and obtaining a to-be-verified generation model; for each description-diagnosis sample in a test sample set, inputting the description-diagnosis sample into the to-be-verified generative model, and determining a prediction diagnosis suggestion of the description-diagnosis sample; comparing the prediction diagnosis suggestion with the reference diagnosis suggestion of the description-diagnosis sample, and determining a first evaluation index of the description-diagnosis sample; determining a second evaluation index of the description-diagnosis sample in combination with a medical diagnosis word list; and based on the first evaluation index and the second evaluation index of each description-diagnosis sample, determining the total model loss of the to-be-verified generation model, analyzing the total model loss, and training to obtain a diagnosis suggestion generation model. Therefore, by means of the first evaluation index and the second evaluation index on different evaluation dimensions, the diagnosis suggestion generation model with relatively high precision can be obtained through training.

The invention provides an intelligent processing system for hospital medical record informatization management, and the system comprises a data storage module which is used for carrying out the butt joint with an existing information system of a hospital through an API gateway, constructing an original database through cloud object storage, and carrying out the centralized storage of structured medical record data and unstructured medical record data; and the term standardization module is used for mapping the medical terms in the medical record to a national standard medical term set. According to the method, automatic analysis, recognition and relation extraction are performed on the text information in the medical record through the medical natural language processing engine, and the text information is filled into the structured data model, so that the medical record information is standardized, and a good basis is provided for clinical scientific research and hospital management. By integrating patient information, historical medical records and medical knowledge maps, diagnosis suggestions and safe medication early warning are provided for doctors in the diagnosis and treatment process, the scientificity of clinical decision making is enhanced, medical risks are reduced, and patient safety is improved.

The present invention relates to the technical field of artificial intelligence and medical decision-making systems, and discloses a method and system for constructing a reasoning agent for intelligent medical guidance. The method comprises: constructing a hierarchical modality completion network and a modality correlation knowledge graph; constructing a medical feature cross-modality mapping network based on contrastive learning to map medical data of different modalities to a shared feature space; constructing an uncertainty quantification model and automatically adjusting the diagnostic confidence threshold based on the uncertainty of the completed data; constructing a disease modality association matrix and a modality reliability assessment network for different disease types and symptom combinations; and realizing cross-validation and collaborative decision-making of multi-specialty knowledge through weighted voting, evidence aggregation and specialist authority assessment to form a final diagnostic recommendation. The present invention solves the problem of limited diagnostic capabilities caused by missing medical data in an environment with limited medical resources.

The invention provides an edge-end collaborative medical image diagnosis method based on a graph theory and deep reinforcement learning. The method is used for solving the problems that in edge-end collaborative reasoning scheduling of a medical image DAG topology DNN, the structure is complex, segmentation is difficult, and the scheduling efficiency is poor. The method comprises the following steps: firstly, converting a complex DAG structure into a chain structure through topological sorting to obtain a CDNN structure; meanwhile, for an end-side collaborative system model, the number of times of floating-point operation per second is used for representing computing resources of user side equipment and an edge server; then, constructing a scheduling unloading model based on a CDNN structure, and carrying out MDP modeling on the scheduling unloading model to minimize inference delay and energy consumption; thirdly, a DP-A3C strategy is put forward for optimization solution, and an optimal model division strategy is obtained; and finally, the model division strategy is fed back to user side equipment, and rapid and effective diagnosis suggestions are provided for doctors to process complex tasks. The method can adapt to different network topologies and task requirements, and provides stable performance.

The present invention provides an automatic analysis and labeling system for medical images based on deep learning. By adopting a deep learning model, based on historical medical images, multiple medical image recognition models are trained to obtain a set of image recognition models, and applicable image features of each medical image recognition model are determined. Based on the association between the target image features of the target medical image and the applicable image features, multiple medical image recognition models are selected from the image recognition model set to identify and label the target medical image, and a labeled area of ​​the target medical image is obtained. Through multi-model recognition and labeling, and through automatic analysis and labeling of medical images, the workload of doctors can be greatly reduced, the diagnostic efficiency can be improved, and the model accuracy can be improved. Finally, the labeled area is automatically diagnosed to obtain the lesion characteristics and diagnostic suggestions of the target medical image, thereby improving the work efficiency and diagnostic accuracy of doctors and reducing human errors.