1309 results about "Patient Base" patented technology

The invention discloses a traditional Chinese medicine nursing management system and method based on a cloud follow-up visit management platform, and belongs to the technical field of intelligent nursing. The management system comprises the following modules: a dynamic data acquisition module for acquiring physiological parameters, living habits and environmental data of a patient in real time and performing primary processing; the dynamic health archive construction module is used for constructing and continuously updating an individualized health archive of the patient based on the data collected in real time; the traditional Chinese medicine knowledge map and reasoning module depends on the traditional Chinese medicine nursing knowledge map and combines an intelligent reasoning technology to realize accurate traditional Chinese medicine syndrome differentiation and treatment analysis and automatically generate a personalized nursing scheme; the multi-dimensional patient feedback module is used for constructing a feedback mechanism fusing objective data, patient subjective feeling and medical care evaluation so as to extract key nursing effect information; and the nursing scheme iterative optimization module dynamically adjusts and optimizes the traditional Chinese medicine nursing scheme based on the feedback data, and realizes personalized and self-adaptive continuous improved nursing.

The invention discloses an apoplexy patient management method and system based on a multi-mode RAG and LLM model, and the method comprises the steps: obtaining the standardized electronic medical record data of a patient based on an FHIR standard electronic medical record; performing feature extraction on the electronic medical record data to obtain multi-modal data; integrating and representing a time-space relationship among the multi-modal data, and obtaining a dynamically updated dynamic knowledge graph; analyzing and integrating the electronic medical record data to obtain integrated information; constructing a stroke risk causal network based on an LLM model in combination with a causal reasoning mechanism; obtaining a personalized health report based on the dynamic knowledge graph and a stroke risk causal network; and based on a personalized report result and feedback data of the patient, a treatment scheme and a management strategy of the patient are optimized in real time. According to the application, the causal reasoning mechanism is embedded into the Transform architecture of the LLM, so that the modeling and prediction capabilities of the stroke risk causal relationship are enhanced.

The present invention relates to the technical field of medical triage, and discloses an emergency patient triage system based on artificial intelligence, including: a data acquisition module: used to obtain the patient's identity and basic information by performing face recognition and fingerprint recognition on the patient, and receive health data uploaded by a wearable device, and transmit it to the patient's corresponding historical pathology library; a medical interaction module: used to provide an interactive platform for patients to remotely input pathological conditions based on 5G wireless communication technology; a multimodal data fusion module; a pathological feature matching module; a triage matching module; and a system integration and display module. Through the convolutional neural network model combined with pathological feature matching, based on the patient's question reply content, the condition and the judgment of the severity, a rapid judgment of the possibility of the disease, the generation of consultation content and the optimization of triage results are achieved, thereby improving the accuracy and efficiency of triage and reducing misdiagnosis and missed diagnosis.

The invention relates to a data fusion processing method and system for an intelligent sensor based on MEMS, and the method comprises the following steps: carrying out the non-stationary signal decomposition of a multi-modal physiological parameter time sequence data stream, and obtaining a key vital sign feature sequence; performing phase coupling degree calculation on the key vital sign feature sequence to obtain an inter-organ function correlation intensity matrix; performing high-dimensional feature space projection on the critical patient based on the inter-organ function association intensity matrix to obtain a pathological state feature map of the patient; performing nonlinear kinetic analysis on the pathological state characteristic spectrum of the patient to obtain a disease progress dynamic trajectory; and performing clinical decision support reasoning based on the disease progress dynamic trajectory to obtain personalized treatment suggestions, thereby solving the technical problem that the existing analysis method is often limited to observation of a single physiological parameter or simple statistical analysis and lacks the ability to understand the disease mechanism from the overall perspective.

The invention discloses an exercise rehabilitation assessment method and system based on limb posture and emotion recognition, and the method comprises the steps: collecting a motion video, recording the age and gender information of a patient, constructing a self-made data set, defining a candidate region containing the rehabilitation motion of the patient, constructing a basic motion posture data set, and carrying out the recognition of the rehabilitation motion of the patient based on a posture estimation algorithm. Personalized limb skeleton key points are extracted, and emotion features are obtained through face key point detection and face action unit analysis; analyzing the motion trail of the knee joint based on the personalized limb skeleton key points, and extracting limb posture information; and carrying out feature fusion on the limb posture information and the emotional features to form a quantitative rehabilitation evaluation result. According to the invention, high-precision capture of the key points of the human skeleton is realized through computer vision and pattern recognition technologies. In addition, in combination with analysis of the emotional state of the patient, the evaluation accuracy is enhanced, and the rehabilitation training effect evaluation is more comprehensive and accurate.

The invention discloses a neurology patient rehabilitation nursing method based on multi-modal data analysis, and relates to the technical field of medical health, and the method comprises the steps: extracting neural function features through a multi-modal data fusion algorithm, and carrying out the calculation through a weighted fusion and statistical analysis method, and obtaining a neuroplasticity index vector; combining the neuroplasticity index vector with the unified multi-modal feature representation, carrying out multi-modal abnormal mode recognition analysis, obtaining a rehabilitation risk early warning signal and a personalized intervention suggestion, and generating a personalized rehabilitation scheme; performing dynamic optimization and self-adaptive adjustment on the individualized rehabilitation scheme by using a feedback loop mechanism to generate an optimized individualized rehabilitation scheme; physiological signals in individualized rehabilitation training based on the optimized individualized rehabilitation scheme are collected in real time and preprocessed, and a standardized physiological response data sequence is generated. According to the invention, the scientificity, timeliness and individual adaptability of regulation and control are improved, so that nerve function remodeling is accelerated and the rehabilitation risk is reduced.

A digital healthcare architecture including a cloud-based virtual clinic platform configured to facilitate remote therapy of one or more patients based on biometric authentication systems and methods. In an example arrangement, one or more biometric indicia of a user (e.g., a clinician and / or a patient) as well as one or more non-biometric input factors may be used in authenticating the user prior to granting access to a protected resource or application (e.g., a therapy application executing on a UE device) configured for effectuating remote programming of an implantable medical device (IMD) operative to provide therapy to the patient.

The invention belongs to the technical field of hospital triage systems, and provides an outpatient and emergency triage system, comprising: an information collection module collecting basic information and symptoms of a patient as original data, and preprocessing the data; the intelligent triage module classifies and predicts symptom data input by a patient through a medical knowledge graph and a machine learning algorithm; the triage result output module allocates departments to the patients according to the classification and prediction results of the intelligent triage module, and predicts the waiting time of the patients based on historical data; the interaction module displays and informs the patient according to the distribution information of the triage result output module and the predicted waiting time; through the intelligent triage module, the severity of the illness state of a patient can be rapidly judged, critical patients can be processed preferentially, so that distribution of medical resources is optimized, the waiting time of the patient in a hospital can be shortened through prediction and scheduling of waiting time of the patient, and the treatment efficiency is improved.

The invention provides a dynamic myopia management lens prescription customization method and system, and relates to the technical field of lens design and customization. According to the dynamic myopia management lens prescription customization technology provided by the invention, ophthalmic basic data of a patient is acquired; then periodically follow-up the patient to obtain ophthalmology measurement follow-up data of the patient; the joint evaluation model dynamically updates a myopia risk evaluation grade of the patient based on ophthalmology basic data and ophthalmology measurement follow-up visit data; and finally, dynamically updating the lens prescription based on the myopia risk assessment level of each time by the joint assessment model. According to the technical scheme, the combined evaluation model can perform dynamic lens prescription adjustment according to follow-up visit ocular axis progress and choroid change conditions, dynamic myopia management lens prescription customization, whole-course tracking of a myopia cycle and individualized prevention and control management are realized, and thus the effectiveness and continuity of myopia prevention and control are improved.

The invention relates to a pressure injury intelligent prediction and prevention method and system, and the method comprises the steps: determining target comprehensive monitoring data of a patient, the target comprehensive monitoring data comprising microenvironment data indicating body surface temperature, body surface humidity and pressure on a pressed part, and individual feature data indicating the gender, age and whether the patient suffers from diabetes; based on the comprehensive monitoring data, a machine learning model based on ensemble learning is adopted for analysis to obtain a risk score, the machine learning model comprises a plurality of basic learners, and each basic learner performs performance evaluation in advance based on a method of combining cross validation and feature importance evaluation; corresponding weights are distributed according to scores of the performance evaluation indexes, weighted average combination is carried out based on prediction results of all the basic learners, and a prediction result of the integrated model can be obtained; and performing quantitative evaluation on the pressure injury occurrence risk of the pressed part of the patient based on the risk score, and generating a risk report.

The invention discloses a tumor needle biopsy robot and an image real-time navigation system thereof, and relates to the technical field of medical instruments, and the method comprises the following steps: extracting historical data, and constructing a segmentation model according to the historical data; acquiring a plurality of medical images of the patient, and constructing a three-dimensional model of the patient according to the plurality of medical images; constructing a visual puncture path according to the three-dimensional model; the method comprises the following steps: acquiring optical tracking data, acquiring a needle point position according to the optical tracking data, correcting a puncture path according to the needle point position, acquiring a path point sequence according to a puncture starting point, a target point, a cost function and a graph structure when an optimal path is planned, and continuously adjusting the path through a path optimization algorithm. The path is far away from key tissues such as important blood vessels and nerves, the path is prevented from excessively approaching or directly penetrating through a dangerous area, so that the rationality of optimal path planning is ensured, the precision controllability of the puncture process is enhanced, the puncture path is corrected in real time through the position of the needle tip, and the possibility of the scheme is further improved.

The invention discloses an examination item scheduling system and method based on patient examination demand optimization, and relates to the technical field of medical first aid, and the system comprises an information obtaining module which is used for obtaining the basic information and the emergency examination demand of a patient; the priority evaluation module is used for evaluating the priority of the patient based on the vital signs and the disease severity of the patient; the scheduling generation module is used for generating an optimized examination item scheduling table so as to ensure that high-priority patients are examined preferentially; the state monitoring module is used for monitoring the state of the patient in real time and dynamically adjusting the examination item scheduling table according to the state change, and the result sending module is used for sending the final examination item scheduling table to the corresponding medical equipment and the doctor terminal; according to the examination item scheduling system and method based on patient examination demand optimization, the problem that in the prior art, when critical patients in the emergency department are rapidly shunted, first-aid response is slow due to priority judgment errors is solved.

Methods for training and using a machine learning model for predicting a likelihood of an event occurring in a patient having a malignant tumour are provided. The method of using the machine learning model includes inputting a pathology image of a portion of a malignant tumour of a patient and one or more categorical risk factors associated with the malignant tumour or the patient. The method also includes extracting, from the pathology image, one or more features relating to one or more properties of the malignant tumour. The method further comprises determining a predicted likelihood of an event occurring in the patient based on the extracted features and the one or more categorical risk factors. The method additionally includes outputting the predicted likelihood.

The invention provides a medication guidance generation system, and relates to the technical field of medical informatization. Comprising a knowledge base construction module used for constructing a medication guidance knowledge base; the graph term construction module is used for constructing a knowledge graph and a standard term set according to the medication guidance knowledge base; the index construction module is used for splitting the medication guidance knowledge base and constructing a vector index and a full-text index; the personal information processing module is used for acquiring personal information of a patient and preprocessing the personal information to obtain preprocessed personal information; the mixed detection module is used for performing retrieval by utilizing a knowledge graph, a vector index and a full-text index according to the preprocessed personal information to obtain a retrieval result set and obtain an intersection result according to the retrieval result set; and the generation module is used for generating medication guidance corresponding to the target patient according to the intersection result. The problems that traditional medication guidance mainly depends on experience and professional knowledge of medical staff, and is low in efficiency, prone to errors and difficult to personalize are solved.

The invention provides a deep learning-based automatic sketching method and system for high-risk clinical target regions and endangered organs in cervical cancer brachytherapy, and is applied to the technical field of data processing. According to the application, CT image information and MR image information of a cervical cancer patient are processed based on a positioning network, and 3D position information and form information of a source applicator and image feature sequence information are generated; performing classification processing on the CT image information of the cervical cancer patient based on a positioning network, and generating a priori knowledge label; processing the priori knowledge label based on the segmentation network, and generating a source applicator positioning target factor; the source applicator positioning target factor, the 3D position information and form information of the source applicator and the image feature sequence information are processed based on the segmentation network, and high-risk clinical target region and organ segmentation parameter information is generated; and processing the high-risk clinical target region and organ segmentation parameter information to generate an automatic sketching result of the high-risk clinical target region and the organ at risk for cervical cancer close-range treatment.

The invention provides a liver function prediction method and device, electronic equipment and a medium, and the method comprises the steps: obtaining original clinical data of a plurality of pneumonia patients, and grouping an original clinical database according to the liver injury degree; performing significance evaluation on characteristic variables in the data, and finding out a first characteristic variable related to the liver injury degree; extracting a first feature variable in the data, and constructing a first data set; respectively training and evaluating the plurality of machine learning models, and selecting the machine learning model with the optimal evaluation result as a liver function prediction model; carrying out interpretability analysis on the liver function prediction model; extracting a second feature variable in each piece of original clinical data to construct a second data set; training a liver function prediction model based on the second data set to obtain a trained liver function prediction model; and predicting the liver function of the target pneumonia patient based on the trained liver function prediction model. The method can be used for rapidly predicting the liver function of the pneumonia patient.

The invention relates to a diabetic symptomatic ICAS prediction method and system based on machine learning. The method comprises the following steps: collecting an HRMRI image sample of a target patient; performing high-risk plaque feature analysis on the HRMRI image sample to obtain high-risk plaque features; performing image omics feature extraction and screening on the HRMRI image samples to obtain image omics features; based on an XGBoost algorithm, training the high-risk plaque features and the radiomics features to obtain a prediction model; and performing symptomatic ICAS attack prediction on a to-be-predicted patient through the prediction model to obtain a prediction result. The accuracy and the stability of the clinical model in predicting the new symptomatic intracranial atherosclerotic disease of the diabetic patient can be improved, and the clinical model adapts to individual specificity.

The invention provides a peripheral artery disease patient rehabilitation management system based on a whole process, and relates to the technical field of peripheral artery disease patient rehabilitation management. The system comprises a patient screening and risk grading module, a rehabilitation evaluation module, an exercise prescription generation and dynamic adjustment module, an intelligent propaganda and education content pushing module and a quality control closed-loop management module. PAD patients are accurately screened through a medical data interface and an NLP technology, and the risk levels of the patients are dynamically evaluated in combination with an intelligent risk grading system; generating a personalized exercise prescription according to the evaluation report, complication and biological monitoring data, and guaranteeing the exercise safety through a dynamic threshold model and collaborative early warning; dynamic matching and timeliness pushing of propaganda and education contents are realized by using an improved KL divergence algorithm; and full-process quality closed-loop management is realized through the quality control closed-loop management module. The precision and safety of rehabilitation management of the PAD patient can be improved, medical resource configuration is optimized, and rehabilitation practice driven by full-process data is assisted.

The invention discloses a joint rehabilitation effect evaluation method and system, electronic equipment and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring video data of a target patient executing a preset rehabilitation action; identifying a joint position coordinate point from the video data; a joint movement track is constructed according to the time sequence of the joint position coordinate points, track parameters of the joint movement track are calculated, and the track parameters comprise the movement angle, the track curvature and the symmetry degree; acquiring a speed change sequence and an acceleration change sequence of joint movement in the movement track, and determining a joint rehabilitation index of the target patient based on the track parameter, the speed change sequence and the acceleration change sequence; and comparing the joint rehabilitation index with a reference index corresponding to the preset rehabilitation action, and determining the joint rehabilitation progress of the target patient. By implementing the technical scheme provided by the invention, the effect of improving the evaluation accuracy of the joint rehabilitation effect is achieved.

The invention discloses a hip replacement postoperative gait dynamics prediction method based on discrete optimal control, and belongs to the field of clinical medical auxiliary prediction. The implementation method comprises the steps that kinematics data are collected through a preoperative gait motion capture experiment, muscle activation signals are collected through a surface myoelectricity system, and on this basis, a neuromuscular control strategy of a patient is obtained through a neural network; predicting the gait motion characteristics of the postoperative patient based on discrete mechanics and an optimal control method, and predicting the postoperative muscle activation degree by using the neuromuscular control strategy; by comparing the muscle activation degrees before and after the operation, the influence of the replacement operation on the muscle force change of the hip joint of the patient is investigated. According to the method, discrete optimal control and the neural network are combined, the learning characteristics of human nerve-muscle control are considered while the physical characteristics of the system are not deviated, personalized muscle control strategies better conforming to different patients can be found, and personalized prediction of postoperative gait dynamic characteristics is achieved.

The invention discloses a critical patient acute lung injury assessment system and method based on machine learning, and relates to the field of acute lung injury assessment, and the method comprises the steps: obtaining chest multi-modal image data of a critical patient, and carrying out the standardization processing to generate a standard data set; dividing the lung area into structural blocks and extracting structural indexes, and selecting compensation missing data through cross-modal space mapping or candidate blocks; identifying a modal conflict area and a structure mutation area to construct a scoring key area; after disturbance screening effective areas are applied, a mean value or weighted fusion strategy is selected according to a consistency index; calculating a lung injury score in combination with the dynamic weight and performing iterative optimization; according to the method, the recognition accuracy of the acute lung injury key area can be improved, and the accurate quantitative evaluation of the lung injury degree is realized.

The invention relates to the field of medical health and artificial intelligence, and discloses a cancer patient low-molecular heparin individualized dose dynamic optimization method based on reinforcement learning, and the method comprises the steps: S1, collecting multi-dimensional clinical data of a cancer patient, carrying out the preprocessing, carrying out the enhancement of the preprocessed data, and generating time sequence synthesis data; s2, based on the multi-dimensional clinical data and the time sequence synthesis data, constructing a reinforcement learning environment, including defining a state space, an action space and a reward function; s3, constructing an environment adaptive deep reinforcement learning EA-DRL model as a dose optimization model; performing model training and optimization in the reinforcement learning environment until convergence and obtaining an optimal dose decision strategy; and S4, inputting real-time dynamic clinical data of a patient to be decided into the trained dosage optimization model, and outputting individualized low molecular heparin LMWH administration dosage recommendation. Individualized precise administration is realized, and the treatment effect and the clinical medication safety are improved.

The invention relates to the technical field of pathological section image recognition, in particular to a system for achieving precise prognosis prediction of a gastric cancer patient based on deep learning, and aims at achieving precise prognosis prediction of the gastric cancer cell tumor patient on the pathological section level. The implementation package of the system comprises an information acquisition module, an image processing module, a wavelet parallel Mama model module, a result prediction module and an interpretability module, so that the efficiency of image feature extraction and the accuracy of model prediction are greatly improved. The core target of the invention is to solve the problems of insufficient accuracy, poorer result stability, limited model generalization ability, lack of interpretability and the like in the existing gastric cancer prognosis prediction method. Through strict verification and testing, the wavelet parallel Mama model of the research shows excellent performance in the aspect of predicting the total lifetime of gastric cancer patients in multiple centers, provides reliable support for clinical decision-making, and manifests the huge potential of the wavelet parallel Mama model in application in the field of precision medical treatment.

The invention aims to provide a multi-modal cancer prognosis evaluation method based on a graph. The method comprises the following steps: S1, pre-processing multi-modal data, segmenting a pathological tissue part into patches, extracting patch features, and performing surrounding type composition on the patches according to cell migration continuity; the omics data is subjected to GSEA enrichment classification, wherein five cancer development processes are included; s2, single-modal features are obtained, and modal corresponding features are obtained by using a modal enhanced and shared graph mask auto-encoder; s3, cross-modal interaction is carried out, and multi-modal data distribution mapping alignment is realized by using a cross-modal alignment module; s4, performing multi-modal fusion, and fusing the cross-modal interaction feature and each single-modal feature by using a Hyperdge-Mixer module to obtain a modal fusion feature; and S5, risk prediction: inputting the final multi-modal representation into a risk prediction module, and finally obtaining a risk prediction result. The cancer prognosis is realized by effectively fusing pathology, genes and clinical data, an end-to-end prognosis analysis framework is realized by utilizing a deep learning technology, the method has good performance and relatively high interpretability, and the clinical application practicability is enhanced.

The invention provides a chronic disease risk early warning system and method based on artificial intelligence. The method comprises the following steps: acquiring historical monitoring information of a diabetic patient; determining development trends of different disease course stages through historical monitoring information, and performing trend evolution based on all the development trends to obtain evolution characteristics of each disease course stage; determining the risk contribution degree of each health index to the chronic disease risk according to the linear correlation among different health indexes, and determining a risk monitoring model of the target patient through all the risk contribution degrees; performing confidence adjustment on the chronic disease risk in the risk monitoring model according to each evolution feature, and further obtaining a confidence risk value of the current disease course stage of the target patient; and carrying out risk prompting on the target patient based on the confidence risk value. By adopting the scheme of the invention, real-time modeling can be carried out on the dynamic change of the course of disease of the patient under the background of massive health data.

The invention discloses a CKD special disease database construction method and system, and relates to the technical field of database construction, and the method comprises the steps: obtaining and predicating a multi-source heterogeneous atomic fact; constructing and fusing three sets of ontology models; verifying and deducing facts in a layered manner according to a multi-layer priority rule base; calling a constraint completion engine to generate inference facts for missing or conflicting fields; streaming follow-up visit, test and intervention logs into events and triggering graded alarms; an anti-fact intervention scene is simulated for the high-risk patient, and pilot groups are screened; dynamically evaluating and adjusting the rule priority; and based on a causal chain and an event mode, clustering patients and constructing a generality map, and recording a whole-process operation chain. Semantic management is realized through fusion of ontology, anti-fact intervention simulation is performed on high-risk patients by means of multi-layer rule verification, and database semantic consistency, reasoning depth, decision support and traceability are improved through causal atlas driven clustering.

The invention relates to the technical field of cardiopulmonary exercise tests, in particular to a cardiovascular patient risk assessment system and method based on a cardiopulmonary exercise test, and the system comprises a physiological data acquisition module, a blood oxygen fluctuation analysis module, an exercise load adjustment module, a cardiopulmonary recovery assessment module and a risk grading module. By monitoring and synchronizing multi-dimensional data in a cardiopulmonary exercise test in real time, the accuracy and credibility of the data are improved, close association between physiological indexes and exercise loads is strengthened, and a doctor can more accurately evaluate the blood oxygen stability and find key physiological changes in time through detailed blood oxygen fluctuation analysis; by integrating heart rate, expiration frequency and stride frequency data, dynamic exercise load adjustment is provided, optimized exercise suggestions for individuals are achieved, then prevention and evaluation strategies are refined, real-time monitoring on the recovery ability of a patient is increased by adding cardiopulmonary recovery evaluation, and intervention is more transparent.

The invention relates to the technical field of medical health management, and discloses an endocrine patient health management system based on big data. According to the system, a multi-source data acquisition module is adopted to acquire heterogeneous health data including physiological monitoring indexes, medication record data, medical image reports and the like, and living habit logs can also be included. The physiological feature noise reduction module is used for extracting steady-state physiological features, the multi-modal fusion module is used for generating a focus dynamic map, and the medication feedback analysis module is used for generating a drug response mode evolution sequence. The data are input into a multi-dimensional decision model, and a health management scheme is output by a personalized scheme generation module. And the living habit analysis module analyzes the living habit logs to generate influence factors, and participates in making a health management scheme. The system realizes multi-source data fusion analysis, can provide a personalized health management scheme for an endocrine patient, improves the disease diagnosis accuracy and treatment effect, and improves the life quality of the patient.

The invention discloses a critical patient pressure sore intelligent early warning and automatic turning-over system based on pressure sensing, and relates to the technical field of intelligent medical monitoring. Comprising a data acquisition and fusion module used for acquiring pressure-blood oxygen time sequence data of a patient after alignment processing; the risk assessment module is used for calculating the blood oxygen decline rate and generating a dynamic quantitative score by combining the blood oxygen decline rate with the pressure intensity value; the dynamic adjustment and optimization module is used for obtaining historical intervention data of the patient, performing personalized dynamic adjustment and optimization and generating a multi-level risk threshold value; the threshold judgment module is used for comparing the dynamic quantitative score with a multi-level risk threshold and generating an intervention instruction; the strategy generation module is used for generating self-adaptive turning parameters according to the score and historical data; the execution feedback module is used for executing body position adjustment and collecting the blood oxygen recovery rate to update historical data. According to the invention, dynamic and quantitative evaluation and personalized automatic intervention of the pressure sore risk are realized, and the accuracy and safety of critical care are effectively improved.

The invention discloses a constipation patient defaecation device and mobile terminal integrated system based on endoscope visualization and air bag assistance, and relates to the technical field of medical instruments, the constipation patient defaecation device comprises a display terminal, a connecting line, an operating handle and a comprehensive pipe, and the two ends of the operating handle are connected with the connecting line and the comprehensive pipe respectively. According to the non-invasive, high-safety and portable constipation patient defecation device, through the integrated design of endoscope real-time visual positioning, air bag auxiliary mechanical defecation and mobile terminal data interaction, the invasive operation pain is reduced, the defecation time is shortened, the complication risk is reduced, and the constipation patient defecation efficiency is improved. The pressure sensor monitors the internal pressure of the air bag in real time and feeds back the internal pressure to the dynamic pressure upper limit module and the mobile terminal in real time, and double protection of a hardware pressure release valve and software pressure limiting is triggered when the pressure exceeds the limit. The problems of high intestinal injury risk, low treatment efficiency, limited application scene and the like caused by operation blindness, function singleness or high invasiveness in the prior art are solved.