1017 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 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 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 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 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 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 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 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 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.

The invention relates to the technical field of psychosomatic health management, in particular to a psychological immune system evaluation system and intervention method for cancer patients. And the data acquisition module is used for acquiring and fusing the immune biochemical indexes of the patient, the continuous physiological time sequence signals generated by the wearable device, the digital psychological assessment data and the clinical treatment stage marks, and outputting a structured multi-dimensional time sequence data set through time alignment and feature extraction operation. Through multi-modal data fusion and integrated machine learning analysis, the psychosomatic health risk of the patient can be predicted based on the continuous physiological and psychological data change trend of the patient, and corresponding intervention measures can be triggered before the patient has obvious clinical symptoms by outputting the quantized risk coefficient and the multi-stage early warning signal, so that the accuracy of the patient is improved. Therefore, an intervention mode is promoted to be converted from traditional intervention after evaluation to predictive and preventive intervention.

The invention provides a digital twinborn medical education training method and system. The method comprises the following steps: generating case information and scoring rules of AI digital patients based on clinical real cases; constructing a physical training environment which comprises a training model, an environment construction object and an operation sensing device; creating a digital training environment mapped with the physical training environment in real time, wherein the digital training environment comprises an AI digital dummy and an interactive operation process; responding to operation input of the trainee in the physical training environment or the digital training environment; through a server synchronization engine, real-time bidirectional interaction between entity operation of the physical training environment and virtual operation of the digital training environment is realized; and dynamically collecting cross-environment operation data, and generating a clinical ability evaluation report based on case information and scoring rules. The method has the beneficial effects that a medical education evaluation-diagnosis-treatment full operation chain is deeply fused, the clinical practice ability is remarkably improved, and medical risks are effectively prevented and controlled.

The invention relates to a nephrotic patient health management method and system based on a neural network prediction model, and the method comprises the steps: obtaining the multi-dimensional health data of a target nephrotic patient at each stage, constructing a time sequence feature matrix to extract the change trend of the whole course of disease, and dividing the course of disease into a disease alleviation stage and a disease deterioration stage; analyzing the health data of the two stages to obtain an improvement factor and a deterioration factor; outputting a renal function state prediction result by using a neural network prediction model fusing an attention mechanism and a knowledge graph, and generating an initial health management scheme by combining the result, a preset health management rule base and patient preferences; and optimizing the initial scheme based on the improvement factor, the deterioration factor and a reinforcement learning algorithm to obtain an optimized health management scheme. Complete-cycle closed-loop management of data acquisition, trend analysis, factor identification, prediction and early warning, scheme generation and dynamic optimization of nephropathy patients is realized, and the accuracy and individuation level of nephropathy health management are improved.

The embodiment of the invention provides a medical system working method and device based on a large model and a knowledge graph. The method is applied to information medical field. The method comprises the following steps: searching contents related to patient chief complaint information from a constructed medical knowledge graph by utilizing a large model, analyzing the patient chief complaint information based on the searched related contents, identifying a patient disease, and giving a corresponding doctor-seeing department; the distance between the hospital and the patient position is smaller than a preset range, and the hospital comprises a doctor-seeing department, and number source data of the target hospital are obtained in real time; according to the number source data and the evaluation factors of the doctors in the doctor-seeing departments in the hospitals, a doctor recommendation result is generated through a large model, and constraint examination is conducted on the doctor recommendation result; and after the constraint examination is qualified, performing multiple rounds of inquiry on the patient according to the patient chief complaint information by utilizing the large model, generating a pre-inquiry report and sending the pre-inquiry report to the patient or a doctor in a recommendation result. Therefore, the doctor recommendation accuracy and the doctor seeing efficiency are improved, and the doctor seeing pressure of a hospital is relieved.

The invention relates to a hierarchical diagnosis and treatment resource allocation system based on an AI priority pre-auditing mechanism, and belongs to the technical field of medical resource allocation, and the system comprises a user input module which is used for receiving initial information of a patient based on an AI applet, and the initial information at least comprises a symptom description, a medical history record, examination data and a diagnosis and treatment stage; the AI intelligent pre-inquiry module is used for performing multiple rounds of questions and answers on the patient according to the initial information to extract an illness state index, inputting the illness state index into a preset risk layering model, and determining an illness state severity / urgency index of the patient; the resource distribution module comprises a special number source pool reserved for experts, and the resource distribution module is used for opening expert numbers for the patients whose illness condition severity / emergency index is greater than an illness condition threshold value; and the referral guidance module is used for recommending a replacement doctor seeing scheme for the patient who does not accord with the open expert number. According to the invention, the technical problem of insufficient fairness and efficiency of an illness state emergency degree registration distribution mechanism caused by structural mismatching of expert resources is solved.

The invention relates to the technical field of medical care informatics and precision medical treatment, and discloses a hemodialysis patient precision medication system based on multiple omics and big data, and the system comprises a multi-source data collection module which collects the multi-source heterogeneous data of a patient and carries out the standardization and integration processing; the drug metabolism analysis module is used for analyzing drug metabolism paths and individual differences through multi-omics data, constructing a drug metabolism polymorphic model and outputting gene metabolism capability grades and biomarker risk tags; the initial medication decision-making module is used for constructing an initial medication decision-making model based on a federal optimization framework of a momentum enhancement and robust aggregation mechanism, and outputting a personalized initial medication scheme for erythropoietin and a vein iron agent; and the drug dosage adjustment module is used for dynamically optimizing the time node and dosage adjustment opportunity of drug administration through an asynchronous federal reinforcement learning framework in combination with a prospective parameter correction strategy. According to the invention, the intelligent medication system with strong robustness and fine decision is constructed.

The invention discloses an old-age tumor patient early-stage weakness risk prediction system based on an improved population algorithm, and the system comprises a data collection module which constructs an old-age tumor patient data set; the data preprocessing module is used for generating a fused elderly tumor patient feature matrix; the feature processing module forms an initial feature set; the feature screening module inputs the initial feature set into a discrete empire butterfly optimization algorithm to generate a key feature subset; the multi-scale Markov chain modeling module is used for constructing a multi-scale Markov chain model based on the key feature subset to form a risk state sequence; and the dynamic risk prediction module is used for judging whether the early-stage weakening risk of the old tumor patient reaches an early-warning standard or not and generating corresponding risk early-warning information. A layered prediction mechanism is established, so that the model can perform comprehensive analysis from short-term health fluctuation and medium-term change trend to long-term risk evolution.

Systems and methods for impingement analysis are disclosed herein. A method includes receiving a three-dimensional model of a human anatomy comprising a spinopelvic joint; receiving input related to a spinopelvic condition of a patient; determining at least one of a sacral slope or a lumbar lordosis of the patient based on the input; classifying the spinopelvic condition of the patient based on at least one of the sacral slope or the lumbar lordosis in a plurality of positions; modifying the three-dimensional model based on the spinopelvic condition and a placement of an implant; performing at least one simulation of one or more activities with the modified three-dimensional model; and displaying an impingement analysis on a display device based on the at least one simulation.

The invention discloses a postoperative monitoring system and method for a metabolic weight loss surgery patient, and the system comprises a multi-source data collection module which is used for obtaining physiological parameters, behavior data and environment data of the patient in real time, the physiological parameters comprise body weight, blood sugar and body composition indexes, and the behavior data comprise exercise amount and diet records; the environment data comprises temperature, humidity and geographic position; the central processing platform is connected with the multi-source data acquisition module and comprises a data fusion unit for performing space-time alignment and dimension reduction processing on the multi-source heterogeneous data by adopting a feature level fusion algorithm; the dynamic risk assessment unit is used for constructing a patient personalized metabolism model based on the fused data, and dynamically generating nutrition loss and complication recurrence risk indexes through a machine learning algorithm; and the intelligent early warning module triggers a graded early warning signal according to the risk index, generates a personalized intervention scheme and pushes the personalized intervention scheme to the doctor-patient terminal.

The invention discloses an emergency treatment high-risk patient real-time grading method based on an improved multi-mode Transform algorithm. The method comprises the following steps: S1, generating a time synchronization multi-mode event sequence; s2, obtaining a corresponding single-mode feature representation tensor; s3, obtaining a risk weighted cross-modal attention matrix; s4, in the improved multi-modal Transform fusion network, generating a fusion feature representation tensor by using a time sequence sliding window cache and incremental updating mechanism, and outputting a risk grading label and a corresponding risk grading confidence coefficient based on the fusion feature representation tensor; s5, inputting the risk grading label and the risk grading confidence into the interpretive sub-network, and generating clinical causal chain prompt information; and S6, synchronizing the risk grading label, the risk grading confidence and the clinical causal chain prompt information. According to the method, the accuracy of high-risk patient identification and the adaptability of the model to a clinical complex scene are remarkably improved, and a test result shows that the real-time identification accuracy of a high-risk case is improved compared with that of a conventional multi-modal model.

A communication device can determine information associated with a medical state of a patient associated with the communication device. The communication device can further determine a priority of data associated with the patient based on the information. The communication device can further communicate data associated with the patient using the priority.

The present invention relates to the field of intelligent medical technology, and in particular to an intelligent outpatient triage method and system based on a large model. An intelligent outpatient triage system based on a large model comprises: a data acquisition module and an outpatient triage module. The present invention determines the triage priority corresponding to the patient by sending the patient's structured data into the outpatient triage model for processing, and performs triage operations on the patient based on the patient's corresponding triage priority, without the need for medical staff to make judgments based on experience; four different judgment criteria are incorporated into the outpatient triage model, and the feature information expression effect and feature information fusion effect under the perspectives of the four different judgment criteria are improved through a multi-standard feature extraction layer, a feature transmission layer, and a difference complementary enhancement layer, so that the outpatient triage model can be adapted to more clinical scenarios and has higher adaptability to complex patient conditions.

The invention discloses a thoracic surgery patient postoperative remote monitoring method and system based on the Internet of Things, and relates to the technical field of medical rehabilitation monitoring. Feature index extraction is carried out by taking body position pressure and respiration collaborative response as a two-factor dynamic evaluation basis, so that high-sensitivity judgment on a thoracic cavity load state is formed; after the abnormal state is recognized, body position adjustment is carried out, acoustic feedback analysis is linked, secondary evaluation after body position adjustment is achieved, intervention effectiveness of body position adjustment is ensured, personalized adjustment suggestions are automatically generated, the whole process of'perception-recognition-execution 'of remote monitoring is opened, the intelligent level and response real-time performance of postoperative recovery management are improved, and the postoperative recovery management efficiency is improved. According to the overall scheme, health risks caused by position misjudgment, intervention lag and monitoring blind areas in a traditional system are effectively avoided.

The invention discloses a rehabilitation state evaluation system for a patient with language psychological disorder based on multi-agent collaboration, belongs to the technical field of artificial intelligence, and aims to solve the technical problem of how to realize comprehensive evaluation of language and psychology and improve the evaluation accuracy and efficiency. The system overcomes the defects of poor dialect adaptation, multi-mode splitting, single evaluation dimension and weak interpretability in the prior art, and meets the clinical requirements of accurate and efficient evaluation on patients with language psychological disorders. The technical scheme is as follows: the system comprises a base model construction unit, a special evaluation agent construction unit and a collaborative scheduling unit, the base model construction unit is used for constructing a base model by taking Transform-XL and Wav2Vec2.0 as a basic framework and fusing a multi-modal attention mechanism so as to realize cross-modal information interaction; and the special evaluation agent construction unit is used for constructing a dialect special evaluation agent, a text special evaluation agent and a chart special evaluation agent based on the base model.