37 results about "Clinical phenotype" patented technology

The invention discloses a method and a system for assisting in inputting clinical information of rare diseases and analyzing gene mutation based on phenotypes. The method comprises the following steps: firstly, acquiring clinical information in voice, text and image forms of a patient through a multi-source data acquisition module, converting the clinical information into characters, and performing entity recognition and standardization processing to generate structured medical record data; secondly, extracting clinical phenotypes from the structured data; furthermore, a candidate gene list is obtained according to the gene-disease relationship, comprehensive scoring and sorting are carried out, and a concerned gene list is output. According to the method, efficient structured input and standardization of clinical information are realized, the accuracy and automation level of phenotype-gene matching are remarkably improved, the gene variation interpretation period is effectively shortened, and intelligent support is provided for precise diagnosis of genetic diseases.

The invention discloses construction and application of a Primrose syndrome non-human animal model, and belongs to the technical field of animal models and disease research. According to the invention, through a CRISPR / Cas9 technology, a codon CAC for coding 596th histidine in a No.14 exon of a mouse Zbtb20 gene is subjected to site-directed mutagenesis into CGC for coding arginine, and a hybrid mouse model for simulating pathogenic point mutation (p.H596R) of human Primrose syndrome is constructed. The model can stably reproduce key clinical phenotypes of the Primrose syndrome, including overgrowth after adult, serum IGF-1 rise, memory dysfunction and anxiety behaviors, and shows abnormal hippocampal neuronal development and synaptic transfer related pathways and the like. The invention provides an important experimental tool for deeply revealing the pathological mechanism of Primrose syndrome, developing drug screening and treatment intervention research and the like.

The application belongs to the technical field of biological detection, and discloses a screening method of respiratory tract infection related biomarkers based on macro-transcriptomics. The application performs macro-transcriptome sequencing on respiratory tract infection samples with different clinical phenotypes, performs data quality control, alignment, transcript quantification, retains pathogen and host information, and then identifies genes stably expressed or significantly changed in different groups by combining differential expression analysis and co-expression analysis, obtains potential biomarkers, and obtains the biomarkers by taking the intersection genes of three machine learning algorithms of LASSO algorithm, random forest model and SVM model. The application provides a screening method of biomarkers for rapid and accurate identification of respiratory tract infection.

The invention relates to the technical field of bioinformatics and medical data processing, and discloses a polygene molecular diagnosis model and a construction method and application thereof.The construction method comprises the steps that a data acquisition module constructs a genome, clinical phenotype and environmental exposure data matrix, a preprocessing module executes regression filling and standardizes continuous variables, and a data processing module performs data processing; the feature screening module executes double-layer screening by using LASSO and a random forest model to output a core feature subset, the model building module builds a logic regression architecture to calculate a baseline logarithm probability, and the dynamic updating module outputs a real-time risk probability in combination with follow-up visit environment data, a time adjustment coefficient and the baseline logarithm probability. And the interactive output module outputs a risk layering label and a feature contribution degree. According to the method, redundancy is eliminated through double-layer screening, the time dimension is introduced to adjust the real-time correction probability, visual attribution is realized in combination with the SHAP algorithm, and the dynamic monitoring capability and interpretability are improved.

The invention relates to the technical field of biomedicine, discloses application of an HGF receptor inhibitor in preparation of a medicine for treating / preventing myasthenia gravis, provides a novel treatment medicine for the myasthenia gravis, and innovatively discovers that the HGF receptor inhibitor can be used for treating / preventing the myasthenia gravis by regulating body inflammation and relieving the progress, clinical phenotype and pathological phenotype of MG diseases. The myasthenia gravis disease is treated.

The present invention provides a method, apparatus, terminal, and medium for screening gene mutation points based on clinical phenotypes. The method includes: identifying candidate genes based on gene detection results, clinical text to be analyzed, and a multi-source database; calculating the basic weights of a first pathway; extracting candidate mutation sites within the genomic regions of the candidate genes and obtaining external evidence corresponding to the candidate mutation sites; performing phenotypic extraction on the clinical text to be analyzed to obtain a structured phenotype; inputting the structured phenotype, contextual information of the candidate mutation sites, and external evidence into a trained mutation site screening model to obtain intermediate semantic analysis results; determining the basic weights of a second pathway based on the intermediate semantic analysis results; obtaining a comprehensive weight for each candidate mutation site based on the basic weights of the first and second pathways; and screening the candidate mutation sites based on the comprehensive weights to obtain screening results. This application can perform site-level screening, improving the accuracy of gene mutation point screening.

A business method for use in classifying patient samples. The method includes steps of collecting case samples representing a clinical phenotypic state and control samples representing patients without said clinical phenotypic state. Preferably the system uses a mass spectrometry platform system to identify patterns of polypeptides in said case samples and in the control samples without regard to the specific identity of at least some of said polypeptides. Based on identified representative patterns of the state, the business method provides for the marketing of diagnostic products using representative patterns. The present invention relates to systems and methods for identifying new markers, diagnosing patients with a biological state of interest, and marketing / commercializing such diagnostics. The present invention relates to systems and methods of greater sensitivity, specificity, and / or cost effectiveness.

The invention provides a Parkinson's disease freezing gait prediction method and device based on eye movement characteristics, equipment and a medium. The method comprises the following steps: acquiring target information corresponding to a target object; the target information comprises target demographic data, target clinical features and target eyeball motion parameters collected at a certain time point, and / or target demographic data, target clinical features and target eyeball motion parameters continuously collected according to a preset frequency within a preset time; and inputting the target information into the target frozen gait prediction model, so that the frozen gait prediction model analyzes and processes the target information to obtain a static frozen gait risk assessment result and / or a dynamic frozen gait risk assessment result corresponding to the target object. According to the scheme, through multi-dimensional integration and collaborative verification of traditional clinical phenotypes and objective indexes of eye movement, non-invasive efficient evaluation of frozen gait risks is achieved, and objective basis and key technical support can be provided for early diagnosis and early treatment of disease subtypes.

This invention discloses a method for establishing a mouse model of outer retinal tubular structures, which is obtained by specifically knocking out the Lss gene in the mouse retina. This invention utilizes CRISPR / Cas9 gene editing technology to construct a mouse model of outer retinal tubular structures by specifically knocking out the Lss gene in the retina. The mouse model of outer retinal tubular structures constructed by this method is similar to the clinical phenotype of this type of retinal disease, providing a relatively ideal experimental animal model for studying the developmental regulation of the mammalian retina and the pathogenesis of retinal degeneration.

This invention provides a method and system for integrated data correlation analysis based on clinical features and molecular indicators, belonging to the field of medical data processing and intelligent prediction technology. The method includes: preprocessing collected clinical feature data and molecular indicator data to obtain a standardized structured dataset; extracting key clinical feature subsets and key molecular indicator subsets from the standardized structured dataset, and fusing them to generate a unified multidimensional fusion feature vector; dynamically calibrating at least three clinical phenotypic benchmarks in the unified multidimensional fusion feature vector using a clinical feature data spectrum, whereby the clinical phenotypic benchmarks are defined based on age feature spectrum nodes, infertility course trajectory critical scaling, and endometrial thickness quantification cutoff values. This invention can effectively complete the integrated correlation analysis of clinical features and molecular indicators and predict pregnancy outcomes, meeting the needs of precision clinical diagnosis and treatment.

The invention discloses a hepatitis B virus combined mutation detection method and device based on high-throughput sequencing. A novel detection and analysis method is established on the basis of targeted amplicon sequencing, the combined mutation of a basic core promoter and a front core region on a single virus DNA molecule can be accurately analyzed, and the relevance of the combined mutation with clinical phenotypes and the influence of the combined mutation on virus functions are further explored on the basis of the combined mutation. The invention provides a new technical means for detecting the combined mutation in the specific region of the hepatitis B virus, makes up for the deficiency of the recognition capability of the traditional detection method on the combined mutation, not only perfects the research on the mutation in the specific region of the hepatitis B virus, but also is beneficial to the individualized diagnosis and treatment of the hepatitis B virus and the prognosis evaluation of diseases.

The disclosure provides a pre-pregnancy carrier screening genetic counseling system based on a large language model, applied to the technical field of gene sequencing analysis, comprising a variation acquisition module for screening the user's to-be-sequenced gene data to obtain pathogenic abnormal site information, and converting the pathogenic abnormal site information, disease clinical phenotype, family history and personal past history into a high-dimensional vector; a variation retrieval module for dynamically retrieving the high-dimensional vector based on a semantic vector index database and recalling Top-K medical data in the semantic vector index database; an inference analysis module for evaluating the reproductive genetic variation risk according to the Top-K related medical data and the high-dimensional vector to obtain an evaluation result; and a multi-round dialogue module, through which the user can obtain the evaluation result through multi-round dialogue. The advantage of the disclosure is that the reproductive risk, intervention suggestions and other contents can be automatically generated, and the disclosure can also interact with the patient through dialogue, thereby reducing the clinical genetic counseling burden.

The invention provides a nutrition intervention method and system for a patient with sarcopenia, and relates to the technical field of health data management. The method comprises the following steps: acquiring multi-modal data of a patient, wherein the multi-modal data comprises basic information of the patient, body composition data, clinical phenotype data, nutrition evaluation data, biochemical index data, muscle function data and exercise preference data; determining the nutritional status, risk factors and intervention direction of the patient based on the multi-modal data; based on the intervention direction, generating an individualized nutrition scheme according to a quantity-mass-opportunity balance mechanism and a nutrition intervention mechanism; wherein the nutrition intervention mechanism comprises an intake mechanism, a nutrition quality mechanism and a feeding opportunity mechanism, the intake mechanism is used for determining total energy intake and a nutrition formula, the nutrition formula comprises the variety, the weight and the cooking mode of food materials, and the nutrition quality mechanism is used for determining nutrition preparations needing to be fortified and supplemented. The feeding opportunity mechanism is used for determining daily meal time distribution and cooperation timing with the exercise scheme.

The invention relates to a big data-based METTL14 action mechanism analysis method and system, and provides a multi-modal omics data-oriented causal structure modeling and intelligent prediction method for the problem of causal inference of a regulation relation of METTL14 and a target gene (such as PTEN) thereof in gastric cancer. The method comprises the following steps: firstly, acquiring a transcriptome, an m6A methylation modification group and clinical phenotype multi-modal original data from a public database, marking and pre-processing the data, unifying the data to a potential space through high-dimensional feature mapping, and constructing and optimizing cross-modal causal embedding representation and a graph structure; the causal link mining is realized by combining the variational self-encoding and the graph convolutional network, and the signal-to-noise ratios of different modes are dynamically weighted, so that the structural analysis precision is improved. Anomaly detection and adaptive correction are further introduced to ensure the credibility of the causal chain. Integrating external biological experiment data to carry out model fine tuning, and supporting dynamic introduction of new features. And finally, outputting an interpretable regulation and control prediction result. According to the scheme, the reliability and generalization ability of causal inference under multi-modal data are improved, and mechanism support is provided for targeted intervention.

This invention provides a computer-aided analysis system based on high-throughput sequencing data and knowledge graphs, including a data access agent, a quality control analysis agent, a species annotation agent, a knowledge graph reasoning agent, a report generation agent, and an agent scheduling center. The data access agent acquires sequencing data and clinical phenotypic data; the quality control analysis agent performs quality control using the Illumina NovaSeq platform, V3-V4 primers, the SILVA v144 database, Q30 ≥ 90%, and sequences ≥ 50 bp; the species annotation agent performs species classification annotation based on a reference database; the knowledge graph reasoning agent performs association analysis based on a microbiome-disease-intervention knowledge graph, using ET_MGNN and RTGN models, including 264 search keywords covering dental caries, periodontitis, oral cancer, Alzheimer's disease, diabetes, and heart disease; the report generation agent generates personalized analysis reports and intervention suggestions; and the agent scheduling center manages and dynamically schedules all agents, forming a complete closed loop of "perception → analysis → diagnosis → intervention → execution → feedback".

This application relates to the fields of bioinformatics and medical big data technology, specifically disclosing a digestive tumor risk assessment system based on multi-omics data. The system includes: a multi-source heterogeneous data acquisition terminal, a data standardization preprocessing module, a broad-spectrum digestive system risk assessment engine, an organ-specific feature decoupling module, a multimodal cross-omics fusion reasoning module, and a clinical decision support and early warning terminal. This solution simultaneously acquires ctDNA methylation, serum indicators, and clinical phenotype data, uses the assessment engine to evaluate overall risk, and extracts tissue-specific features through methylation map comparison. Combined with a dynamic weight allocation model, it outputs the targeted risk probabilities for the esophagus, stomach, and colorectal region. This application improves screening sensitivity through multi-dimensional data fusion, solves the problems of reliance on a single data source and inability to locate lesions, and achieves accurate organ-specific analysis and early warning.

The application provides a kind of based on deep learning's radiation pneumonia prediction method and system.The method comprises the following steps: S1, obtains DICOM image, radiotherapy data, clinical data and immunotherapy time series data, and carries out pre-processing;S2, constructs multi-channel three-dimensional structure tensor based on DICOM image;S3, obtains multi-channel input tensor X according to planned CT volume, dose volume and three-dimensional structure tensor;obtain time series characteristic vector and clinical phenotype characteristic vector according to radiotherapy data and clinical data;S4, construct fusion prediction model, and process three-dimensional structure tensor, time series characteristic vector and clinical phenotype characteristic vector, obtain the risk probability of radiation pneumonia;S5, based on the dynamic update of input and re-prediction of updated patient data.The application realizes the deep fusion and dynamic update of multi-modal information, can provide high-precision, risk prediction with spatially interpretable, and provides decision support for clinical intervention.

The invention discloses an AI high-flux cardiovascular medicine screening system, and relates to the field of cardiovascular medicine screening. Comprising the following steps: establishing a cardiovascular medicine multi-modal database, integrating compound structure data, biological activity data, genomics data and clinical phenotype data, and performing data cleaning, normalization and feature extraction by adopting a standardized process; the method comprises the following steps: establishing a multi-modal drug characterization learning model based on a deep learning framework, processing molecular two-dimensional structure features by using a graph neural network, processing molecular conformation information by using a three-dimensional convolutional network, processing physical and chemical parameters by using a full-connection network, and carrying out heterogeneous feature fusion and unified vector representation through a cross-modal attention mechanism. According to the method, an intelligent screening system covering the whole drug discovery process is constructed, so that collaborative precise prediction and multi-objective optimization of the activity, toxicity and pharmacokinetic properties of the compounds are realized, and the efficiency and success rate of discovery of the cardiovascular lead compounds are remarkably improved.

The invention provides a biological safety detection big data analysis method and system based on cloud computing, and relates to the field of safety detection.The method comprises the steps that multi-source heterogeneous data such as gene sequences, clinical phenotypes and space-time tags in a cloud platform are integrated, a time decay function and a geographic risk weight matrix are set, and the weighted risk support degree of each feature item is calculated; screening the feature item with the support degree higher than a first threshold value as a high-risk feature item; the tuple set is scanned in a descending order according to the high-risk feature items and the support degrees of the high-risk feature items, a context path coding FP tree is constructed, and besides feature identifiers and the support degrees, nodes of the FP tree further comprise path context vectors recursively updated along a father node path by using a gating loop unit; recursively mining the FP tree to generate candidate high-risk association modes, and calculating the cohesion and sequence validity of each candidate high-risk association mode; the candidate modes meeting the conditions that the weighted risk support degree is higher than a first threshold value, the cohesion degree is higher than a second threshold value and the sequence effectiveness is higher than a third threshold value are taken as biological safety high-risk association rules and output.

The application provides an IBS micro-ecological transplantation intelligent prediction method and system based on multi-omics driving, and relates to the field of biomedical technology. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect target patient metagenome, metabolome, host genome and clinical phenotype group data; inputting the data into a bacterial flora-metabolite joint network analysis model to construct an interaction network and extract features; generating a correlation matrix based on the features and host genome data and calculating an index; combining the clinical phenotype data and the index to generate an index through a dynamic response algorithm; and using a transfer learning framework to jointly model and output a therapeutic effect prediction result. The system comprises data acquisition, network analysis, correlation calculation, dynamic response and joint modeling modules. The application integrates multi-omics data, accurately mines the relationship between flora and host, realizes intelligent prediction of micro-ecological transplantation efficacy, provides strong support for IBS personalized treatment, and has data processing and security guarantee measures.

The invention discloses a congenital glaucoma animal model and a construction method and application thereof, and relates to the technical field of animal models. The construction method comprises the step of enabling the TRPA1 gene of a target animal to be not expressed or to be inhibited in expression through a gene editing technology so as to obtain the TRPA1 gene knockout or low-expression animal model. The model can effectively simulate the clinical phenotype of human congenital glaucoma, is helpful to understand the pathogenesis of glaucoma, and provides a simple, reliable and economic animal model closer to the phenotype of a patient for subsequent disease research.

The invention discloses a biological specimen-based special-type interstitial lung disease association analysis method, which comprises the following steps of: acquiring multi-dimensional omics and pathological characteristic data through a biological specimen omics data mining platform, and realizing characteristic differentiation grouping through a lung interstitial characteristic deep clustering identification algorithm, inputting the grouped data into an interstitial lung disease subtype typing prediction model, completing preliminary typing in combination with clinical phenotypes, capturing a pathological feature dynamic change rule by using a specimen pathological feature time sequence evolution model, integrating clustering, typing and time sequence evolution results through a platform feature fusion module to construct a feature set, and carrying out classification on the feature set; and establishing a parameter mapping relation of each dimension through a correlation analysis module. According to the method, feature input optimization, sequential sequence analysis and multi-dimensional feature efficient fusion are realized, the pertinence and reliability of correlation analysis are comprehensively improved, and technical support is provided for precise diagnosis and treatment of diseases.

The invention discloses a biomarker for auxiliary diagnosis of SLE (systemic lupus erythematosus) and application of the biomarker, and belongs to the technical field of biology. The invention discloses one of an anti-NPM1 antibody, an anti-RPLP2 antibody and an anti-MX1 antibody or a combination of any two or three of the anti-NPM1 antibody, the anti-RPLP2 antibody and the anti-MX1 antibody, which can be used as a biomarker for auxiliary diagnosis of SLE. And the kit can be used for auxiliary monitoring of SLE disease activity and auxiliary diagnosis of SLE (combined anti-phospholipid antibody) with clinical phenotypes of arterial thrombosis, microangiopathy, heart valve enuresis and thrombocytopenia, and has good diagnosis efficiency.

This invention discloses a AIPL1 A gene mutation-related congenital amaurosis animal model, its construction method, and its application. The method includes: S1, constructing a humanized... AIPL1 S2, a homologous recombination vector with a point mutation of c.421 C>T (p.Q141X) knocked in; S3, Cas9 mRNA, gRNA1, gRNA2 and the homologous recombination vector are injected into animal zygotes to perform homologous recombination, resulting in transfected zygotes; S4, the transfected zygotes are transplanted into the uterus of pseudopregnant animals to reproduce offspring, and offspring are selected from the offspring. AIPL1 Homozygous animals with the c.421 C>T (p.Q141X) point mutation were obtained, thus yielding the aforementioned... AIPL1 A gene mutation-related congenital amaurosis animal model. The animal model of this invention exhibits the clinical phenotype of Leber congenital amaurosis type 4 (LCA4) from 12 days after birth; while heterozygous mice do not show a significant phenotype, consistent with... AIPL1 The clinical phenotype and inheritance pattern of LCA caused by the c.421 C>T (p.Q141X) gene mutation demonstrate the effectiveness and reliability of the animal model of this invention, providing a basis for understanding the genetic model of LCA. AIPL1 Animal models provide a reliable basis for studying the disease mechanisms and drug screening of mutation-related LCA.

The invention discloses a congenital cataract mouse model with RagA GTPase point mutation, a construction method and application, and belongs to the technical field of gene editing. The method comprises the following steps: aiming at a gRNA sequence of a mouse RagA GTPase gene, jointly introducing gRNA and Cas9 mRNA into a mouse fertilized egg, and transplanting the fertilized egg into the uterus of a pseudopregnant female mouse to obtain an F0-generation mouse carrying RagA GTPase point mutation; and hybridizing the F0 generation mouse with a wild type mouse, and screening to obtain an F1 generation homozygote mouse carrying RagA GTPase point mutation. According to the invention, a RagA GTPase Leu60Arg point mutation mouse model is constructed by using a CRISPR-Cas9 technology for the first time, and the RagA GTPase Leu60Arg point mutation mouse model can simulate clinical phenotypic characteristics of human congenital cataract and is used for pathogenesis research.

The invention relates to the field of medical artificial intelligence, in particular to a multi-modal deep learning fused early-stage intelligent screening system for rheumatic diseases, which comprises an intelligent question and answer module, a knowledge graph module, an auxiliary diagnosis module and a hierarchical diagnosis module, innovatively introduces an algebraic topology theory, and represents medical knowledge as a pure complex structure, so that the medical knowledge can be quickly and accurately screened; weak signals in the early stage of diseases are captured through the continuous homology feature extraction technology, clinical phenotypes, laboratory examination and iconography information are integrated through a spectrum topology fusion network, the system adopts a multi-head topology perception attention mechanism, contributions of different modes are dynamically balanced, early accurate screening and clinical verification display of rheumatism are achieved, and the system has a wide application prospect. The system can identify the early performance of rheumatism six months in advance, the diagnosis accuracy reaches 85%, reliable decision support is provided for early intervention, and the system is particularly suitable for primary medical screening, specialized auxiliary diagnosis and multi-center clinical research.

The invention discloses a pathogenic gene prediction method and device based on phenotypic fingerprints, equipment and a medium. The method is executed by a computer, systematic integration and quantification are carried out on associated information between genes and phenotypes of multiple dimensions, phenotype fingerprints with specific genes are constructed on the group level, and complex effects of the genes on different phenotype dimensions can be captured more comprehensively; a multi-phenotype score value taking genes as the center is calculated through gene phenotype fingerprints, that is, multi-dimensional clinical phenotype information of a target object is converted into quantitative scores taking the genes as the center on the object level, and two types of key output of pathogenic variation carrying risk assessment and candidate gene priority ranking are achieved through observation phenotypes of the target object; and the integrating degree of each candidate gene and the actual phenotype of the target object can be objectively and efficiently evaluated. According to the method, phenotype fingerprints are introduced, a multi-phenotype scoring mechanism is combined, the efficiency and objectivity of complex disease pathogenic gene recognition are remarkably improved, and the method has important clinical application value.

The application discloses a method for identifying a key regulator of tumor immune interaction, comprising the following steps: S1, integrating single-cell sequencing, and constructing a tumor-immune cell interaction identification model of a multi-view attention network to screen a tumor-immune cell interaction pair with the greatest influence on prognosis; S2, according to the tumor-immune cell interaction pair with the greatest influence on prognosis, obtaining gene mutation, copy number variation and gene expression characteristics of the key regulator based on a cell interaction key regulator identification algorithm based on multi-omics feature fusion; S3, constructing a key regulator function exploration and regulation network; and S4, based on the key regulator function exploration and regulation network, designing an immune therapy response correlation of the key regulator of tumor immune interaction. The application integrates large-scale multi-omics data, systematically analyzes the correlation between the key regulator of tumor immune interaction and its functional module and immune response characteristics, clinical phenotypes and immune therapy response, and explores the potential of the key regulator as a prediction marker.

The present application relates to the technical field of bioinformatics, in particular to a biological information analysis system based on large model technology, which comprises a data analysis calibration module, a problem disintegration module, an analysis task arrangement module, a result mapping module and a feedback iteration module.In the present application, dynamic calibration of genome alignment and clinical phenotype timestamp eliminates time sequence misplacement deviation, base complementary pairing combined protein network abnormal screening enhances low-abundance collaborative variation capture, genotype-phenotype discrete distribution quantization unifies multi-modal data benchmark, solves multi-source heterogeneous standardization loss, classifies and integrates pathogenic gene semantic weight, balances statistical threshold and biological function, dynamically optimizes analysis sequence to synchronously cover key mutation area, improves non-coding region function annotation, integrates gene expression clustering and protein network topology in three-dimensional distribution, breaks through two-dimensional space limitation, and reduces genetic heterogeneity false positive rate through closed-loop feedback correction threshold iteration elimination rule.

The application provides a method and system for nutritional intervention of sarcopenia patients, and relates to the technical field of health data management. The method comprises the following steps: collecting multi-modal data of the patient, the multi-modal data comprising basic information of the patient, human body composition data, clinical phenotype data, nutritional assessment data, biochemical index data, muscle function data and exercise preference data; determining the nutritional status, risk factors and intervention direction of the patient based on the multi-modal data; generating an individualized nutritional plan based on the intervention direction, the quantity-quality-time balance mechanism and the nutritional intervention mechanism; wherein the nutritional intervention mechanism comprises an intake mechanism, a nutritional quality mechanism and an eating time mechanism, the intake mechanism is used to determine the total energy intake and a nutritional formula, the nutritional formula comprises the types and quantities of food materials and cooking methods, the nutritional quality mechanism is used to determine the nutritional preparation that needs to be supplemented, and the eating time mechanism is used to determine the daily meal time distribution and the cooperation timing with the exercise plan.