472 results about "Clinical tests" patented technology

The invention provides a block chain-based clinical test data integrity system, and relates to the technical field of data processing, and the system comprises the following components: a multi-source heterogeneous data acquisition and preprocessing module, a multi-level encryption and under-chain data storage module, an alliance block chain network, and an alliance block chain network maintenance and verification shared distributed account book. The distributed account book only stores all digitally signed clinical test data fingerprints, metadata and operation logs, does not store original data, and is linked with the intelligent contract automatic management module, so that the clinical test data cannot be tampered in the whole life cycle from the source to archiving. Hash anchoring, digital signature and decentralized verification mechanisms of the block chain technically eradicate the possibility of unauthorized tampering of data, and ensure scientificity and reliability of clinical test results.

The invention relates to the technical field of computers, and discloses an integrated information system for clinical test archives and a data processing method thereof, and the method comprises the steps: constructing a standardized data model covering a whole process; multi-source data dynamic structured intake and integrity verification are carried out; performing multi-dimensional semantic association analysis based on a Bayesian network and a correlation coefficient; block chain type version tracing and difference comparison are carried out; performing role-sensitivity-operation-context four-dimensional access control; and task-driven collaborative sharing and auditing log records. The system comprises a unified modeling module, a data intake module, a semantic analysis module, a version tracing module, an access control module and a collaborative auditing module. The problems of data islands, semantic segmentation, poor dynamic adaptability, weak safety protection and the like in the prior art are solved, intelligent management, semantic interconnection, safety controllability and efficient collaboration of the whole life cycle of clinical test files are achieved, and data quality, audit compliance and multi-role collaboration efficiency are remarkably improved.

A computer-implemented method is provided for generating a trained TTS-GAN which may be used to generate synthetic longitudinal data for use in survival analysis, a clinical trial or clinical research. The TTS-GAN is configured to generate synthetic time-series data based on synthetic context data generated using a machine-learning model by virtue of being trained using training data comprising real context data and added noise data. A technique for executing survival analysis is also provided, which relies on the synthetic longitudinal data.

The invention provides a tumor patient clinical test matching system and method based on a large language model and an OCR technology, and is applied to the field of medical data processing. The method comprises the following steps: analyzing clinical data and test information, processing an unstructured text, and generating structured clinical feature data through context association analysis; key data is extracted and subjected to double verification correction, and structured data supplementary information is generated; enhancing the structured clinical feature data and supplementary information based on a multi-modal processing assembly line module, extracting an image quantitative index, analyzing an immunohistochemical result, and generating a comprehensive matching score; through a rule engine and semantic similarity calculation, item-by-item comparison of patient features and entry and exhaust conditions is realized, and a preliminary matching result is generated; edge case misjudgment is corrected through context-aware multi-round reasoning, sorting is adjusted in combination with clinical test priority weights, and an optimized clinical test matching list is generated; and generating a clinical test matching report based on the data.

The invention provides a recruitment patient determination method and device for a clinical test, a computer program product and electronic equipment, and relates to the technical field of medical big data, and the method comprises the steps: obtaining medical data of a plurality of patients, and converting the medical data into structured data; performing natural language analysis on the group entering standard information of the clinical test according to a large language model to generate a structured screening condition; performing data filtering on the structured data based on the structured screening condition through intelligent agents corresponding to a plurality of accommodating and arranging conditions of a clinical test to determine a recall result, matching the recall result based on the plurality of accommodating and arranging conditions, and determining an accommodating and arranging score of the medical data of each patient according to a plurality of matching results; and determining a target recruitment patient corresponding to the clinical test based on the recruitment score. According to the invention, the patient recruitment efficiency and accuracy can be improved.

The invention discloses a clinical test document quality detection and processing method and system and computer equipment. The method comprises the following steps: acquiring a to-be-processed clinical test document; performing format conversion and optimization processing on the to-be-processed clinical test document to obtain a preprocessing result; automatically classifying the preprocessing result to obtain a classification result; extracting document key information according to the classification result, and generating a file name to obtain an extraction result; checking the integrity, compliance and archivability of the corresponding document according to the extraction result, and performing automatic processing according to the detection result to obtain an automatic processing result; and storing and archiving the automatic processing result. By implementing the method provided by the invention, efficient and accurate document quality detection and automatic processing can be realized, so that the document processing quality and efficiency are improved, and the problems of low efficiency, poor consistency and high error rate in the prior art are solved.

The invention provides a thyroid cancer auxiliary diagnosis and metastasis risk prediction method based on deep learning, and relates to the technical field of artificial intelligence auxiliary medical treatment, and the method comprises the steps: extracting ultrasonic image multi-scale features through a self-adaptive neural architecture search network, combining clinical examination data, fusing diagnosis and treatment knowledge through a neural symbol inference device, and carrying out the prediction of the metastasis risk. Generating a knowledge enhancement feature map; constructing a feature propagation field by using a dynamic neural field network, solving a dynamic evolution equation, and generating a spatial-temporal feature field representing the dynamic change of focus features; constructing a tumor diffusion kinetic model by using an implicit neural representation network and a nerve ordinary differential equation network, calculating a transition probability based on an optimal transmission algorithm, solving an optimal control equation, and outputting a metastasis risk prediction result of each organ; the thyroid cancer diagnosis accuracy and metastasis risk prediction reliability can be effectively improved, and doctors can be assisted in accurate diagnosis and treatment.

The invention discloses a tumor disease assessment method based on big data, and particularly relates to the technical field of tumor disease assessment. Medical data and living habit data of a patient are collected through multiple data interfaces, integration is carried out through preprocessing and a multi-modal data fusion technology, tumor microenvironment features and drug metabolism features are extracted, a comprehensive feature vector is generated, the effectiveness of a chemotherapy regimen is predicted by using a machine learning model, and when the effectiveness of the chemotherapy regimen is low, the chemotherapy regimen is identified. Newest clinical tests and drug data are introduced regularly, model parameters are dynamically optimized in combination with the chemotherapy regimen improvement effect, it is ensured that the model reflects the newest clinical progress in real time, the problems of model updating lag and drug innovation mismatching are effectively solved, the accuracy and effectiveness of a treatment regimen are improved, and the treatment cost is reduced. And meanwhile, side effects caused by blind attempts are reduced, and powerful support is provided for personalized precise medical treatment.

The invention provides a self-adaptive clinical trial design method based on machine learning, relates to the technical field of clinical trials, and adopts a method integrating reinforcement learning, causal inference and data enhancement technologies to predict an optimal treatment scheme and adjust a distribution proportion in real time through a dynamic optimization module, and limits high-risk treatment selection in combination with dynamic ethical constraints. The problems that a traditional clinical test preset rule lacks real-time performance and neglects patient safety are solved, a data collection and enhancement module generates virtual patient data conforming to a causal relationship, real and virtual data are combined to form a unified data set, a high-quality data set is constructed, the data size is effectively expanded, the statistical power of a small sample scene is improved, and the accuracy of a clinical test result is improved. The termination rule module of comprehensive statistics, ethics and resources shortens the test period through weighted evaluation, significantly reduces the risk of improper allocation of high-risk groups, enhances the benefit of patients, ethics compliance and test result reliability, and expands the application range in rare disease research and personalized medical treatment.

The invention relates to the technical field of data processing, in particular to an intelligent analysis method and system for clinical test data, and the method comprises the steps: obtaining a data sequence of each physiological index of a patient, and calculating the deviation degree through quantifying the difference between a target data point and a corresponding physiological index normal range, calculating an abnormal index based on the deviation degree and the distribution characteristics of the corresponding data in the data sequence of the corresponding physiological indexes, and obtaining a fitting curve of each physiological index by fitting the abnormal index, so as to screen out time periods in which all the fitting curves are in a rising trend; and taking the physiological index pairs of which the correlation is greater than a preset value in the time period meeting the screening condition as target index pairs, constructing a causal graph by using a PC algorithm based on the target index pairs, and identifying the causal relationship of each physiological index. According to the method, the complexity of constructing the causal graph by the PC algorithm can be reduced while the important causal relationship among the physiological indexes is reserved.

The invention relates to the technical field of hepatitis diagnosis and treatment management, and discloses a block chain-based hepatitis patient full-period management method. The method comprises the following steps: creating a patient identity chain in a block chain network, and storing patient registration data and initial diagnosis information; full-cycle diagnosis and treatment data of a patient are obtained and divided into clinical examination, iconography, medication records and life sign monitoring data. Performing multi-dimensional cleaning on the clinical examination data to generate a standardized data set; inputting the iconography data into a pre-trained hepatitis feature extraction model, and outputting a liver injury feature set; verifying the integrity of the medication record, and generating a non-tampering medication time sequence chain; comparing the life sign monitoring data with preset health reference parameters to generate a sign deviation coefficient set; according to the method, data safety and traceability are guaranteed through the block chain, diagnosis and treatment data quality is improved through multi-dimensional data processing, patient privacy is protected, and reliable support is provided for full-period management of hepatitis patients.

The invention discloses a multi-role configuration and effective judgment method based on semantic arbitration, and belongs to the technical field of artificial intelligence, and the method comprises the steps: setting a multi-organization role and an arbitration role; receiving and vectorizing a test description text, distributing the test description text as a first clinical test context vector to multiple organizations, randomly assigning a first speaking party and setting an empty discussion pool; the first speaking party generates speaking content based on the clinical test context vector, vectorizes the speaking content and writes the speaking content into a discussion pool, calculates the similarity between a speaking content vector and the clinical test context vector, and calculates an arbitration score in combination with multi-organization attributes; the arbitration role executes three-state judgment according to the arbitration score and the parameters, an arbitration decision is generated, and an effective speaking content vector is determined; fusing, updating and writing the effective vector and the clinical test context vector into a discussion pool; discussion is terminated according to preset conditions, and based on the final vector and the discussion pool, effective judgment is output and a log is recorded; according to the method, a multi-organization discussion scene can be effectively simulated, and the clinical test discussion efficiency and accuracy are improved.

The invention belongs to the technical field of radial artery compression hemostats, and particularly relates to a radial artery hemostasis method based on real-time pulse analysis and a hemostat control system. A slow and continuously changing force is applied to a radial artery puncture opening through the pressure applying module, meanwhile, the pulse detection module transmits a pulse forced feedback signal of the puncture opening to the main control module in real time, and the main control module analyzes and processes the received feedback signal in real time and judges the current hemostatic state of the radial artery. And on this basis, the pressure applying module is controlled to adjust the pressure. The method is characterized in that the relation between pulse forced feedback signals and the forced state of the radial artery is obtained through clinical tests according to the difference of the pulse feedback signals of the radial artery in different pressed states, and therefore the pressure is stabilized in the state that the blood flow of the radial artery is stable and a puncture opening does not bleeding. According to the scheme, the individual difference between different patients can be overcome, the compression hemostasis strength is adjusted in a self-adaptive mode, and the occurrence rate of related complications such as radial artery occlusion is effectively reduced.

The invention discloses a drug clinical trial contract management system, which comprises a contract text structured knowledge base for storing contract structures, field terms and hospital basic data and supporting standardized construction. The contract processing engine module is used for receiving test parameters and driving contract intelligent matching, budget automation and text dynamic splicing; the intelligent auditing module is used for performing automatic comparison, rule checking, difference detection, compliance examination and clause conflict early warning, so that manual errors and workload are remarkably reduced; the electronic signing module is used for providing multi-mode identity verification and block chain evidence storage, and ensuring the signing effectiveness and non-tampering property; a database is recorded and tracked, full-life-cycle management of contracts is achieved, state changes and version history are recorded, and tracing and auditing are facilitated. According to the invention, all the modules cooperate, the contract generation efficiency and accuracy are significantly improved, the customization and compliance are enhanced, budget transparency is realized, safe and efficient signing is provided, and the operation cost and legal risks are effectively reduced.

The invention provides a tuberculosis early screening method based on deep learning, and belongs to the technical field of tuberculosis screening. The method comprises the following steps: collecting multi-modal medical image data, RNA sequencing data, electronic medical record text data and clinical examination data of a tuberculosis patient, preprocessing the data, and extracting characteristics of each modal data; and by using an IRENE model, the extracted features are fused through a bidirectional multi-modal attention mechanism, and overall characterization is learned. And based on the fusion features, adopting a federal learning technology to train an ensemble learning classification model, and outputting a screening result. A decision support model based on reinforcement learning is further constructed, a decision strategy is optimized in combination with a screening result, electronic medical record text data, clinical examination data and historical diagnosis and treatment information, and finally a binary classification result for judging whether the patient suffers from tuberculosis or not is given. According to the method, multi-modal data are integrated, the advantages of deep learning in the aspects of feature extraction and fusion are fully played, and early screening of tuberculosis is realized.

The present invention provides a method and system for automatically and seamlessly processing clinical trial source data into electronic data capture (EDC) systems. In one embodiment, a file structure is defined for an electronic binder system (eBinder); source data is uploaded to the eBinder; the source data is encrypted, Patient Identifiable Information in the source data is masked; the source data is converted into machine readable plain text in the JavaScript Object Notation (JSON) format using Natural Language Processing (NPL) technologies; the JSON data is converted into tabulated machine readable data in the HyperText Markup Language (HTML) format using NPL technologies; the HTML data is converted into machine understandable data using NPL technologies; the machine understandable data is populated into EDC datasets using NPL technologies; the source data and converted data are displayed side-by-side for source data verification; and a platform is provided for regulatory data verification or auditing.

The invention provides a compound nystatin ointment and a preparation method thereof, and relates to the technical field of medicine preparation, the compound nystatin ointment comprises active ingredients of nystatin, neomycin sulfate, triamcinolone acetonide and permethrin, and the formula comprises liquid paraffin, white vaseline, wool fat, hexadecanol, PEG-30 dipolyhydroxystearate and glyceryl monoisostearate. According to the compound nystatin ointment preparation prescription process disclosed by the invention, the phenomenon of ointment matrix layering in the use process of commercially available products is mainly solved. According to a pharmaceutical research test, the stability of the compound nystatin ointment is superior to that of a commercially available original development agent, the ointment body character and the stability of active ingredients can be kept under the conditions of high temperature, high humidity, low temperature, freeze thawing and illumination, the maximum single impurity content and the total impurity content in the product are not obviously increased, and the quality is controllable. And meanwhile, an in-vitro transdermal test (IVPT) accidentally finds that the retention amount of related active ingredients is higher than that of the original development agent, and the drug effect is obviously improved. Through clinical test research, the compound nystatin ointment disclosed by the invention can be used for effectively treating external otitis caused by bacteria sensitive to neomycin, fungi sensitive to nystatin and ear mites sensitive to permethrin of dogs and cats.

The invention discloses a clinical test case report form automatic generation method and system, and relates to the technical field of medical instrument tests.The method comprises the steps that a target clinical test scheme document is analyzed, and medical instrument data is extracted; matching a corresponding target case report sub-table from a structured template database based on the medical instrument data; the structured template database comprises a hierarchical mapping relationship between the medical instrument type and the case report sub-table; and combining the target case report sub-tables to generate a case report table document. According to the method, the target test scheme is subjected to structured analysis, then the structured template database is automatically matched according to the analyzed medical instrument data to obtain the target case report sub-tables in the standardized format, and finally the case report table document is generated based on the combination of the target case report sub-tables. Therefore, the design time of the medical instrument clinical test case report form is remarkably shortened, and the design efficiency is improved. Meanwhile, human errors are reduced, and the reliability of the case report form is guaranteed.

The invention discloses a sample size calculation method and system for quantitative consistency evaluation. The method comprises the following steps: firstly, determining MDL and a clinical consistency boundary value, then collecting pre-test data, and determining a required sample size through iterative calculation. Then, verification samples are extracted from the overall data for consistency analysis, and the robustness of a calculation result of the verification sample size is simulated through Bootstrap; according to the method, MDL and least square regression are fused for the first time, a sample size formula for quantitative consistency evaluation is constructed, the reliability and effectiveness of clinical test data are ensured by providing a new sample size calculation method, more accurate sample size estimation is provided for clinical research, and therefore the quality of clinical decisions is improved.

The invention relates to the technical field of clinical laboratory digital intelligence, and particularly discloses an intelligent explanation method for an inspection result based on an AI algorithm mart, and the process comprises the steps: constructing N disease data sets, and training N AI disease prediction models based on the N disease data sets to obtain N AI disease prediction models. A plurality of disease prediction models form an AI market and are applied to a real scene, a plurality of disease prediction models meeting conditions are triggered based on a target patient inspection result to generate a prediction result, a decision-making path is automatically adjusted by using a decision-making tree algorithm, the prediction results of the plurality of models are fused to obtain an optimal explanation, and the model performance is continuously monitored through a sliding window. And when the performance of the model is reduced, re-training or parameter adjustment is triggered in time so as to ensure the accuracy and stability of a prediction result. According to the method, through multi-model collaboration, dynamic decision and full-process AI algorithm digital closed-loop management, the comprehensive interpretation capability of clinical examination results can be improved, and the examination value is further improved.

The invention relates to the technical field of medical data analysis, and discloses a clinical examination result analysis method and system based on a medical knowledge graph. The method comprises the following steps: discretizing continuous test data of a patient into data segments with change modes, mapping a time sequence mode of the data segments to corresponding entity nodes of a knowledge graph, and constructing knowledge graph segments carrying dynamic information; and performing heuristic extension by identifying key mode paths in the analysis paths to generate a candidate analysis path set. And performing synchronous iteration comparison on the core analysis path and real-time inspection data, dynamically correcting the confidence coefficient weight of each path, and finally outputting a structured report according to the optimized main analysis link. According to the method, fusion of inspection data time sequence features and the knowledge graph is realized, and the depth of result analysis and the adaptive capacity to illness state evolution are improved through dynamic interaction optimization of paths and data.

The invention discloses a clinical examination A box integrated with biochemical detection equipment, and belongs to the technical field of emergency medical rescue, the clinical examination A box comprises a box body, a box cover and a support structure, the outer surface of the box body close to the upper end is provided with a locking structure; the box cover and the box body are limited through a locking structure; the supporting structure is installed on the lower end face of the box body. Wherein a detection instrument integrating a dry type blood cell analyzer, a dry type biochemical analyzer, a dry type immune biochemical analyzer, a urine analyzer and a thermal printer is placed in the box body, a limiting assembly is arranged in the middle of the upper end face of the box cover, and during clinical examination, the detection instrument is placed on the limiting assembly; besides, the biochemical detection instruments are integrated together and then placed in the box body, and the integrated biochemical detection instruments can be directly taken out and then placed on the box cover for direct use during use, so that the use is relatively convenient, and a good use prospect is achieved.

The invention discloses an intelligent processing method and system for clinical test data, and relates to the field of data processing, and the method comprises the steps: recognizing a high flow disturbance stage, and starting an uploading channel adjustment process; executing flow limiting operation in the high flow disturbance stage; obtaining a load pressure evaluation result; and completing data supplementation. According to the method, under the condition of data uploading peak or resource shortage in the clinical test process, the whole-process regulation and control of the disturbance data, such as current-limiting protection, back compensation and abnormal filing, is realized by effectively identifying the high-flow disturbance stage, dynamically adjusting the data uploading behavior and intelligently distinguishing the processing priorities of the key data and the non-key data. The method has the advantages of being high in adaptability, accurate in resource scheduling and safe and reliable in data, solves the problems of system congestion, uploading failure or important data lag in the data uploading stage, and improves the overall operation stability and the data integrity guarantee capability of a clinical test data processing system.

The invention belongs to the technical field of model optimization, and provides a large language model optimization method and system based on a clinical test. The method comprises the following steps: determining a first attribute of a target drug aiming at a clinical test, and matching based on the first attribute to obtain field data; generating a small sample data set based on the domain data, and performing fine tuning training on the large language model by using the small sample data set; a plurality of second attributes are obtained through matching based on the first attributes, clinical test data are obtained based on the first attributes and the second attributes, multi-modal data fusion is conducted on the clinical test data, and a strengthened training data set is obtained; and performing reinforcement learning optimization on the fine-tuned large language model by using the reinforcement training data set, and performing model evaluation and iteration on the large language model until an optimization ending condition is met. According to the method, the field data and the clinical test data are integrated, the optimization of the large language model is realized by using reinforcement learning, and the model generalization ability of the large language model can be remarkably improved.

The invention provides a method and equipment for analyzing adverse event information of a clinical test. The method comprises the following steps: acquiring audio information of a target object; outputting a corresponding pinyin sequence based on the audio information by using a pre-trained speech recognition model; performing text prediction on the pinyin sequence to obtain an output text; in response to the fact that the knowledge-enhanced large language model is utilized to determine that an adverse event exists in the output text, key information in the output text is extracted; performing named entity identification based on the key information to obtain name information, time information and degree information in the key information; and constructing adverse event information based on the name information, the time information and the degree information. By implementing the method and the device, the analysis processing can be automatically performed according to the description of the target object to obtain the corresponding adverse event information, compared with manual processing, the speed is higher, the method and the device are not interfered by clinical environments or other factors, and analysis tasks can be efficiently completed in batches.

The invention relates to the technical field of medical information matching, in particular to a clinical test item and case rapid matching system and method, and the system comprises a data acquisition and storage layer which obtains clinical test item information and multi-dimensional electronic health record data of an enrollment subject; the data processing layer is used for preprocessing the data; the preliminary matching layer is used for eliminating enrollment subjects which do not conform to hard conditions of clinical test items based on a rule engine; the deep matching layer is used for calculating the matching probability of the registered subjects and the clinical test items in the preliminary matching result, and generating a candidate subject list according to a probability threshold value; the data mining layer is used for mining a residual subject list from a related medical platform when the number of people in the candidate subject list is insufficient; and the output layer is used for generating a matching report for each successfully matched subject and highlighting the key matching factors. The matching efficiency and accuracy of clinical test items and medical records can be improved.

The invention provides a block chain-based clinical test data traceability and sharing platform. The block chain-based clinical test data traceability and sharing platform comprises the following modules: a life cycle state definition module, an on-chain event generation module, an identity verification module, a state transition logic unit and an event chain construction module. According to the invention, through cooperation of a life cycle state definition module, an on-chain event generation module, a state transition logic unit and an event chain construction module, key behaviors generated by a subject in the stages of screening, informed agreement, group entering, visit, adverse event processing, follow-up visit and the like are solidified into a chain event structure according to a time sequence; each event node comprises a behavior abstract, a preorder node hash and a timestamp, continuous association and accurate positioning of cross-stage operation are achieved, a clinical test is converted into a state transition chain capable of tracking the whole process from a fragment type data uplink, and the problem that a continuous behavior chain cannot be constructed in the prior art is solved.

The invention discloses a clinical test scheme deviation automatic checking method and device and computer equipment. The method comprises the following steps: acquiring PD records and PD classification grading data from a CTMS system and an EDC system to obtain two pieces of data of the same project of the CTMS system and the EDC system; analyzing the two parts of data by using a large language model to obtain two parts of analysis results; performing consistency check on the two data according to the two analysis results, and comparing differences to obtain a detection result; generating a report containing consistency check and difference comparison according to a detection result; and outputting the report. By implementing the method provided by the invention, the checking efficiency, accuracy and system compatibility can be remarkably improved, the manual intervention and learning cost can be reduced, and seamless integration with the existing workflow can be ensured.

The embodiment of the invention discloses a medical equipment control system based on a clinical test scheme. According to one specific embodiment, the system comprises a scheme generation server, a document abstract extraction server, a data analysis server and a medical robot, and the document abstract extraction server is used for generating a query argument tree and a retrieval information result set based on text information and document request information; generating an ideal answer vector set according to the retrieval information result set; querying the ideal answer vector set to generate a retrieval result group; processing the retrieval result group to obtain a literature abstract; the data analysis server is used for performing instruction conversion on the query information to obtain a query instruction and executing the query instruction to obtain an information query result; and the scheme generation server is used for generating a clinical test scheme and sending the clinical test scheme to the medical robot to execute clinical test operation. According to the embodiment, the system response time and the waste of test samples can be reduced.

The invention provides a preparation method and application of fructus cannabis protein peptide with a sugar absorption blocking effect, and belongs to the technical field of functional active peptides, and the preparation method comprises the following steps: crushing fructus cannabis, and degreasing through low-temperature continuous phase change to obtain degreased fructus cannabis meal after degreasing; the degreased hemp seed meal is hydrolyzed through polysaccharase and protease in sequence, low-temperature salting-out is carried out after hydrolysis is finished, drying and sterilization are carried out, and the hemp seed protein peptide is obtained. The fructus cannabis protein peptide obtained by the technical scheme and the preparation method disclosed by the invention has the following effects: 1, the fructus cannabis protein peptide prepared by the preparation method disclosed by the invention can be used for remarkably inhibiting the activity of alpha-amylase and alpha-glycosidase (the maximum inhibition rates can respectively reach 84.2% and 75.1%), so that digestion and absorption of a human body on carbohydrates such as starch, dextrin and disaccharide are blocked; and 2, double-blind clinical test results show that the fructus cannabis protein peptide prepared by the invention can effectively reduce the postprandial blood sugar peak value and peak area, and the effective sugar breaking rate can reach 30-60%.