764 results about "Physical examination" patented technology

The invention belongs to the field of natural language processing, and provides a pre-training large model physical examination text labeling method and system based on active learning. The method is realized through the following process: collecting and sorting structured and unstructured physical examination data, manually pre-labeling a small number of samples by an expert and standardizing a format; selecting a pre-training large model adaptive to the medical field task; designing a domain-specific cue word template to strengthen semantic understanding of the model; performing parameter optimization by adopting supervised fine tuning and low-rank adaptation technologies; an active learning closed loop is constructed, uncertain samples are selected through multiple rounds of iterative training to supplement labels, and the model is dynamically optimized by using a verification set; and the optimized model is applied to intelligently label the remaining data. According to the scheme, the key problems of low efficiency, poor rule method generalization ability, high machine learning model computing power demand and insufficient large model field adaptation of traditional manual labeling are solved, and the automatic extraction precision of medical entities such as pathological features and clinical indexes in the physical examination report can be effectively improved.

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

The invention relates to the field of disease prediction, in particular to an epilepsy prediction system based on synchronously acquired electroencephalogram-gastric electrical signals, comprising: a data acquisition module for acquiring data including electroencephalogram data and gastrointestinal electrical data; the first analysis module is used for obtaining an average electrode inconsistency index of the first channel and the second channel based on a first model according to the time sequence data; and the prediction module is used for comparing the average electrode inconsistency index of the subject with a preset evaluation threshold value so as to predict an epileptic. The system provided by the invention can more accurately and efficiently distinguish epilepsy patients from healthy people, and can be used as an auxiliary diagnosis tool to be applied to primary screening of epilepsy of relatively large people (such as communities and physical examination centers).

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 an intelligent physical examination data quality control analysis method and system, and relates to the technical field of medical quality control. The problems that existing physical examination data are diversified in source, complex in format, single in quality control means and the like are solved. Structured data, medical images and texts are integrated, and standardized processing and data mapping are achieved through a unified platform. Static and dynamic rule libraries are constructed, and the threshold is dynamically adjusted in combination with individual features, so that personalized risk assessment is realized. Structured data anomaly detection is performed by adopting machine learning, an image quality problem is identified by utilizing a convolutional neural network, and text anomaly is processed and analyzed through a natural language, so that the anomaly detection accuracy is improved. The system realizes multi-dimensional quality control and intelligent early warning, enhances the integrity and credibility of physical examination data, supports precise health management and disease early warning, improves the quality control efficiency, meets the intelligent analysis requirements of large-scale multi-modal physical examination data, and promotes the application of intelligent health management.

The invention relates to the technical field of health early warning, in particular to a human health early warning method and system based on physical examination data analysis, which comprises the following steps: collecting physical examination data of a human body, analyzing index changes of blood pressure, blood sugar, cholesterol and body mass index of the human body, sorting the physical examination data of the human body through time sequence sorting, and generating a time sequence sorting result. According to the method, time sequence sorting and spatial dimension evaluation are performed on the physical examination data, so that the health risk can be accurately identified and the early warning level can be dynamically evaluated, the data analysis precision is improved, the risk prediction dimension is increased, early warning is more timely and accurate, and the accuracy of early warning is improved by performing layout evaluation on the physical examination data points in a finite dimension space. Potential health abnormalities can be identified on multiple levels, and multi-dimensional analysis provides a new way for identifying complex health problems, so that health management is more active and predictable, health monitoring and risk management are optimized, and the health condition of a patient is improved.

The invention discloses a fireman occupational health risk dynamic prediction method based on multi-modal data fusion, relates to the technical field of occupational health risk assessment, and aims to solve the problems of information loss, difficulty in capturing cross-modal complex dependence, lack of interpretability of prediction results and the like when unstructured data is processed in the prior art. The method comprises the following steps: intelligently analyzing a multi-modal document, and analyzing unstructured physical examination information into structured data; performing semantic standardization and knowledge graph dual verification of domain knowledge enhancement; the time-space-static cross-modal attention deep fusion network is used for modeling physiological, dynamic and situational data; and carrying out interpretable risk prediction and attribution. By adopting the technical scheme, the method can realize early, accurate, dynamic and high-credibility prediction of the occupational health risk of the firefighter, provides quantitative attribution, and improves the transparency and application value of the system.

The invention discloses an intelligent inspection method for a special operation physical examination room, and relates to the field of path planning, and the method comprises the steps: obtaining examination room data and environment parameters in real time, and building a live-action navigation map based on the examination room data, the live-action navigation map being used for real-time linkage with the examination room data; acquiring an examination room image in real time through inspection equipment; image features of the examination room image are extracted, the inspection path is divided into n areas based on the image features, and n is a positive integer; executing a dynamic path planning strategy based on the live-action navigation map and the image features to generate an inspection path of the inspection equipment; performing fusion analysis on the environmental parameters and the image features to determine a congestion index on the inspection path; and determining a dangerous area in the n areas based on the congestion index, and visualizing the dangerous area. The inspection accuracy of the special operation physical examination room can be effectively improved, so that the risk hidden danger of the examination room is effectively reduced.

The invention relates to the technical field of data analysis, in particular to a city physical examination accurate analysis method and system based on AI multi-modal data collaboration, and the method comprises the following steps: constructing a self-adaptive fractal space-time grid, and mapping a city multi-modal data source into a space-time coding vector with a fractal dimension; establishing a physical field driven cooperative resonance network, and extracting an abnormal cooperative mode exceeding a normal resonance threshold; calculating a modal entropy chain value according to the real-time metabolic rate of the urban system, and generating an optimal weight matrix through a non-equilibrium thermodynamic model; and fusing the abnormal cooperation mode and the dynamic weight matrix, and generating a space-time causal map for displaying a fault source and a propagation path by using a causal discovery algorithm. According to the method, the multi-modal data collaboration and causal analysis technology is utilized, the emergency response capability of an urban system in the case of sudden failures is improved, the stability of urban operation is guaranteed, and the space-time causal atlas provides comprehensive visual information for the failure propagation process.

The invention discloses a cancer early warning management method and system based on a physical examination report, and belongs to the field of medicines.The method comprises the steps that health data of individuals are collected, and unstructured texts in the physical examination report are processed through combination of optical character recognition and a natural language processing technology; calculating a cancer risk based on a single physical examination report, evaluating a specific cancer by using a cancer risk scoring system, and analyzing whether an index exceeds a normal range or not through a rule engine; based on historical data trend prediction risks, index change rates are calculated, and an abnormal trend is analyzed and predicted in combination with a time sequence; multi-modal deep learning is utilized to predict individual cancer risks, and a Bayesian network is combined to perform joint analysis on multiple factors to generate a health management scheme; further examination is arranged according to the early warning level, and a screening strategy is provided for specific cancers. According to the method, the OCR + NLP technology is adopted, and the data quality is improved. And in combination with a multi-modal deep learning model, abnormal changes are found in advance, and early warning is realized.

The invention discloses a health examination data analysis method and system based on artificial intelligence, and the method comprises the steps: receiving multi-modal health examination original data of a user, and generating a multi-modal feature matrix fused with time-space correlation; inputting the multi-modal feature matrix into a lightweight dual-channel network to obtain a decoupled dual-modal feature group; constructing a hidden Markov chain based on the bimodal feature group, and generating a health state transition trajectory diagram with a timestamp; inputting the health state transition trajectory diagram into a discriminator of the generative adversarial network, and outputting a confidence score and a pathology trigger threshold of a high-risk node; and activating a rule engine according to a pathological trigger threshold, and generating a hierarchical health intervention instruction set in combination with individual living habit data. By using the embodiment of the invention, the physical examination data can be efficiently analyzed, and the accuracy of disease early warning and health intervention is optimized.

The invention provides a hip joint impact syndrome intelligent diagnosis method, system and device based on multi-modal data fusion and a computer readable storage medium. The intelligent diagnosis method for the hip joint impact syndrome based on multi-modal data fusion comprises the following steps: acquiring a hip joint X-ray image and health condition text information of a patient; wherein the health condition text information comprises general information, medical history information, symptom information and physical examination information of the patient; inputting the hip joint X-ray image and the health condition text information into a preset hip joint impact syndrome diagnosis model, and outputting a hip joint impact syndrome diagnosis result; according to the hip joint impact syndrome diagnosis model, text features and image features are extracted and fused for diagnosis. According to the embodiment of the invention, the hip joint impingement syndrome can be diagnosed more accurately.

The invention provides a method and a device for constructing a female osteoporosis prediction model based on physical examination data. The construction method comprises the steps of obtaining a potential prediction variable set of female osteoporosis based on literature query; screening a sample set from the physical examination data; dividing the sample set into a training set and a verification set, and based on the preprocessed first training set, obtaining an intersection of variables screened by using a Boruta algorithm, an LASSO algorithm, a varSelRF algorithm and an SVM-RFE algorithm, and taking the intersection as a feature variable set of the osteoporosis prediction model; obtaining a second training set and optimizing the plurality of constructed initial prediction models by using the second training set to obtain a plurality of parameter tuning prediction models; and obtaining a test set, evaluating each parameter tuning prediction model by using the test set, and determining a female osteoporosis prediction model based on an evaluation result. The female osteoporosis can be predicted on the basis of eight conventional physical examination data, and the method has the advantages of high accuracy and medium sensitivity.

The invention relates to the technical field of flight ability evaluation, in particular to a pilot emergency processing ability evaluation method and system fused with physiological signals. Initial ability indexes are extracted based on literature research and physical examination standards, and physiological data of a pilot in a normal state and a simulated emergency state are collected; the method comprises the following steps: extracting potential ability indexes through dimensionality reduction and denoising of a component analysis algorithm and factor analysis (FA), and screening remarkably changing physiological indexes (plt; 0.05), and performing correlation combination with the initial index (a correlation coefficient gt; 0.8) constructing an emergency capability evaluation index system including situation awareness, decision making, communication, pressure management, team cooperation, reaction time, strain and flexibility; the pilot emergency ability assessment method and system are constructed through multi-source physiological signal fusion and dynamic modeling, accurate quantitative analysis and personalized training guidance are achieved, and civil aviation safety specifications are adapted.

The invention relates to a data processing method and device for a multi-parameter health monitoring sensor, and the method comprises the following steps: carrying out the continuous recording of a target user, and obtaining a multi-channel monitoring data flow; carrying out heartbeat profile description on the multi-channel monitoring data stream based on a heartbeat feature reconstructor to obtain heart activity parameters; carrying out heartbeat rule analysis on the target user based on the heart activity parameters to obtain heart working characteristics; performing heart state evaluation on the heart working characteristics to obtain heart operation data; and if the heart running data is not within the preset heart running range, performing dynamic health tracking on the target user based on the heart running data to obtain a heart health report, thereby solving the problem that the traditional heart health monitoring means mainly depends on regular physical examination and static electrocardiogram detection, and the monitoring accuracy is high. Dynamic changes and individual differences of heart activities are difficult to capture, so that potential heart risks cannot be early warned and intervened in time.

The invention relates to the technical field of physical examination scoring, in particular to a solid ball examination method and system based on AI, and the method comprises the following steps: based on a solid ball throwing video of an athlete, employing an inter-frame difference method, calculating the pixel change between continuous video frames, recognizing a motion area, separating a foreground from a background, and determining the positions and movement of the athlete and the solid ball; and generating motion area data. According to the method, the dynamic positions of the athlete and the solid ball can be accurately identified and tracked by applying the inter-frame difference method and the optical flow method, so that more accurate and continuous movement track information is provided, and key actions are identified and classified more accurately by using the time convolutional network and the time sequence clustering; accuracy and efficiency of action analysis are improved, and more objective and meticulous evaluation is realized on technical execution quality scoring and comprehensive technical evaluation through application of a support vector machine and a decision tree.

According to the physical examination item recommendation method, device and equipment based on the large language model and the computer readable storage medium, interactive physical examination questions and answers are performed with the target user according to the initial risk factors and the related question list corresponding to the candidate disease information list, the collection efficiency of the dynamic health information of the target user is improved, and the user experience is improved. And mining dynamic health information. In combination with dynamic health information, symptom information and health risk factors are further mined, and a disease information list of the target user is enriched, so that personalized physical examination item recommendation of the target user is realized. And the basic health information of the target user is combined with the dynamic health information, and the potential disease information of the target user is determined comprehensively, so that the physical examination item information more adaptive to the target user is obtained according to the comprehensive target disease information list, and the recommendation efficiency and recommendation accuracy of the physical examination item information are improved.

The invention relates to the technical field of artificial intelligence, is suitable for financial and medical scenes, and particularly relates to a low-illumination physical examination image noise reduction enhancement method, device and equipment and a storage medium, the low-illumination physical examination image noise reduction enhancement method comprises the following steps: obtaining a to-be-processed low-illumination physical examination image; based on a Retinex decomposition model, acquiring a preliminarily estimated illumination component of the low-illumination physical examination image, acquiring an accurately estimated illumination image according to the preliminarily estimated illumination component, and enhancing the accurately estimated illumination image through gamma transformation to obtain an enhanced image; performing noise reduction on the enhanced image based on a BM3D algorithm to obtain a noise-reduced image; and combining the enhanced image and the noise reduction image to obtain a noise reduction enhanced image. The problem that in the prior art, when enhancement processing is carried out on the low-illumination physical examination image through an image enhancement algorithm, synchronous processing is not carried out on noise and amplified noise in the enhanced physical examination image is solved, and the accuracy and efficiency of physical examination image recognition are greatly improved.

The invention discloses a method for realizing standardization of examination items of a cross-mechanism physical examination report based on a large model. The examination item names in the physical examination reports of different institutions are reasoned and judged through the large language model, and effective understanding and standardized mapping of medical text data from different sources are achieved. Finally, the unification of the same index names of different hospital institutions can be realized, the unification is not limited to the knowledge base range and the dependence on codes, part of manpower is replaced by a large language model, and the labor cost is saved.

The invention belongs to the technical field of information, and discloses a time-phased physical examination reservation and management system based on intelligent optimization, and the system comprises a physical examination reservation client which is used for a user to complete the selection of a physical examination package, the reservation of a time period and the like online through a mobile phone client according to the actual condition of the user, the self-service machine only needs to print a physical examination guide sheet and sign in and take a number on the site on the same day, and then the whole physical examination process is completed according to system prompts; and the system management end is used for project maintenance, personnel management, system management and the like of physical examination center management personnel. According to the invention, by introducing a time-phased physical examination appointment mode, an intelligent hospital guide service is provided for a user, and a set of time-phased physical examination appointment and management system based on intelligent optimization is designed. The result is that the user only needs to make physical examination before the reservation time period through the online physical examination reservation, and examination is carried out according to the intelligent hospital guide prompt. Through time-phased appointment management, physical examination persons can be averagely arranged in all time periods, the number of physical examination persons on site is effectively controlled, the physical examination queuing time of the users is shortened, and the satisfaction degree of the physical examination users is further improved.

The invention relates to the technical field of report optimization, in particular to a user medical history-based physical examination report conclusion priority adjustment method, which comprises the following steps of: acquiring multi-source data, and preprocessing the multi-source data to obtain a structured multi-source data set; based on the structured multi-source data set, mining an association relationship between health data and diseases through association analysis, constructing a health trend prediction model in combination with a deep Q network, and obtaining a multi-dimensional diagnosis analysis result; determining a diagnosis conclusion priority in combination with a multi-dimensional diagnosis analysis result and a priority calculation formula; calculating a comprehensive risk score through the data fusion score, and dividing risk levels according to the comprehensive risk score to match corresponding stage intervention strategies; and inputting a multi-dimensional diagnosis analysis result, intervention strategy execution feedback data and user health preference into a comprehensive diagnosis decision model, and obtaining a differentiated health association strategy in combination with a diagnosis conclusion priority. According to the scheme, the severity of the disease is accurately identified by dynamically adjusting the priority.

The invention relates to the technical field of medical information processing, in particular to a method for constructing a model for predicting the onset risk of hyperglycemia-related ovarian cancer, which comprises the following steps of: performing isolated analysis on the blood glucose level of a patient during each physical examination, and calculating the blood glucose abnormality of the patient during each physical examination in combination with the local trend characteristics of the blood glucose level, so as to determine the onset risk of hyperglycemia-related ovarian cancer; analyzing the difference between the blood glucose value of the patient and the overall trend information, and evaluating the abnormal blood glucose condition in combination with the abnormal blood glucose degree of the patient; the prediction credibility of the blood glucose value of the patient during each physical examination is calculated by utilizing the incidence relation between the blood glucose value and the tumor marker concentration in different periods and combining the evaluation result of the abnormal blood glucose condition of the patient during each physical examination; therefore, the blood sugar value data is subjected to data cleaning so as to further predict the onset risk of the hyperglycemia-related ovarian cancer of the patient. According to the method, the prediction accuracy of the onset risk of the hyperglycemia-related ovarian cancer is effectively improved.

The invention discloses an ultrasonic anesthesia positioning method and system based on AI preoperative evaluation and a storage medium, and relates to the technical field of medical assistance. The method comprises the following steps: comprehensively collecting body health data of a patient, including a physical examination result, a laboratory examination result and a medical history, reasoning the body health data of the patient based on an operation risk knowledge graph model, and determining an operation risk level; if the operation risk level is higher than a preset risk level value, the operation risk is evaluated again in combination with past operation case information, and if the operation condition is met, the crowd is judged to be the high-risk crowd of the operation; an ultrasonic image of an in-vivo structure of an operation high-risk crowd is collected by utilizing an ultrasonic technology, the ultrasonic image is recognized by utilizing an image recognition AI algorithm, the relationship between an anatomical position and an adjacent part is determined, and then an anesthesia position is determined. The medical quality and the service level can be improved, and safer and more efficient surgical treatment experience can be brought to patients.

The invention relates to the technical field of chronic disease examination, in particular to an AI-based chronic disease risk prediction and early intervention method, which comprises the following steps of: acquiring physiological data, living habit data, environmental data and historical health data of a user through various devices and platforms, and then preprocessing the acquired data to obtain an AI-based chronic disease risk prediction and early intervention result; the method includes the steps of data cleaning, missing value filling and standardization, then conducting feature selection through a principal component analysis method, extracting key features related to chronic disease risks, building a chronic disease risk prediction model through an improved random forest algorithm on the basis of the key features, and finally predicting the chronic disease risks in combination with a model prediction result. Personalized intervention suggestions are provided for the user, and diet adjustment, exercise plans, drug prevention, regular physical examination and other aspects are covered; according to the method, high-risk people with chronic diseases can be accurately identified in an early stage, and accurate risk prediction and personalized intervention strategies are provided, so that occurrence and development of the chronic diseases are effectively prevented.

The invention relates to the technical field of biomedicine, in particular to a gestational period premature delivery risk auxiliary prediction method and system, and the method comprises the steps: obtaining the historical body data and delivery cycle of a pregnant woman, and determining the historical premature delivery degree of the pregnant woman according to the delivery cycle; according to the premature delivery degree difference between different historical pregnant women and the deviation degree change condition difference of the same type of body data during each physical examination, determining the premature delivery correlation degree of different types of body data of the historical pregnant women; clustering the historical pregnant women according to the deviation time difference of the same type of body data among different historical pregnant women, determining the overall correlation of each type of body data according to the number of pregnant women in the cluster and the premature delivery correlation degree of the corresponding type of body data, and screening out the target type of body data; and based on the body data of the target type of the historical pregnant woman during different times of physical examination, constructing a premature delivery prediction model to perform premature delivery prediction. According to the method, the accuracy of premature delivery prediction is effectively improved.

The invention relates to the technical field of medical health monitoring, and particularly discloses a chronic disease patient physical examination data and complication nursing monitoring system based on the Internet of Things, multi-dimensional physical examination data such as vital signs and blood biochemical indexes of a patient are collected in real time through a multi-source sensing equipment network, and an individualized data stream is constructed; extracting parameter collaborative change characteristics by adopting multi-time scale analysis, and constructing a dynamic physiological characteristic spectrum; identifying the abnormal offset by calculating the deviation degree of the feature vector distribution and the reference distribution; based on intelligent matching of the spatial distribution features of the feature vectors and a clinical case library, generating structured early warning information including risk assessment and personalized nursing schemes; and finally, visual display and preventive nursing intervention of the complication risk evolution process are realized.

The invention discloses a clinical intelligent decision-making method based on proxy workflow and a storage medium. Comprising the following steps: acquiring and preprocessing clinical information of a patient, and constructing a candidate disease set; and constructing a proxy directed workflow. In the retrieval stage, the diagnosis criteria corresponding to the candidate diseases are retrieved and aggregated from the diagnosis criteria library rechecked by the experts to form working memory. The preliminary diagnosis stage model node generates a preliminary candidate diagnosis set in combination with work memory and patient medical history and physical examination. And the final diagnosis stage generates a final diagnosis result based on the preliminary candidate diagnosis and the complete clinical information. And when the global confidence is lower than a threshold value, the model node pointedly checks an information source and updates reasoning and confidence. A successful reasoning track forms a demonstration set after manual auditing, the demonstration set is used for supervising a fine tuning model to obtain an initial strategy, multiple structured outputs are generated through grouping sampling, relative strategy updating is carried out in combination with reward signals and reference strategy regularization constraints, and optimization and stable improvement of the model diagnosis capability are achieved.

The invention relates to the technical field of deep learning, in particular to an intelligent report interpretation method and system based on deep learning, and the method comprises the steps: obtaining a physical examination report containing a plurality of physical examination items, and recognizing text information and image information; inputting the text information and the image information into a report identification model, extracting image features of the physical examination items through an image encoder, and extracting text features of the physical examination items through a text encoder; dynamically distributing the weights of the image features and the text features through an attention fusion layer, aligning the weighted image features to the dimensions of the text features, and splicing the weighted image features with the weighted text features to obtain fusion features; performing classification processing on the fusion features through a classifier, identifying concerned items in the physical examination report, and outputting health categories corresponding to the concerned items; inputting the attention item and the health category corresponding to the attention item into a knowledge graph to obtain an interpretation result of the physical examination report; according to the invention, the accuracy and efficiency of physical examination report interpretation are improved.

The invention relates to the technical field of mining hydraulic support detection, in particular to a method for detecting the sealing performance of a piston rod cavity after overhaul of a mining hydraulic support stand column, and the method comprises the following steps: S1, preparation before detection, S2, equipment connection, S3, low-pressure performance test, S4, high-pressure performance test, S5, processing after detection, and S6, report generation. According to the invention, a multi-stage segmented detection mode is adopted, a low-pressure performance test is firstly carried out, just like a primary physical examination is carried out on the stand columns, so that the stand columns with sealing hidden dangers in a low-pressure environment can be preliminarily screened out, a high-pressure performance test is carried out after the stand columns are qualified, and the sealing performance of the stand columns is greatly improved through a test mode of gradually increasing the pressure in a segmented manner. Therefore, stable pressure change is ensured, the performance of the sealing performance of the stand column under different pressure conditions can be found more accurately, and the detection method can accurately and efficiently detect the sealing performance of the piston rod cavity after the mining hydraulic support stand column is overhauled through a comprehensive detection process and reasonable parameter control.

The invention provides a physical examination data exception association analysis method and system based on a knowledge graph, and relates to the technical field of exception association analysis, and the method comprises the steps: determining all physical examination items of a target physical examination center; aiming at a plurality of selectable physical examination indexes, executing abnormal generality association and generality confidence degree analysis among the indexes, and establishing an abnormal generality association map; receiving a physical examination report; executing abnormal generality association verification based on the abnormal generality association graph; performing abnormal distribution characteristic analysis in a preset period for the plurality of selectable physical examination indexes, and performing distribution abnormality verification on the physical examination report; and performing joint abnormity verification according to the distribution abnormity verification result and the generality verification result, then performing abnormal physical examination item positioning, and sending the abnormal physical examination item positioning to a target physical examination center for reminding. The technical problem that potential health risks are difficult to recognize in time and the accuracy of physical examination results is affected in the prior art can be solved, and the technical effect of improving the accuracy of physical examination anomaly detection is achieved.