120 results about "Clinical safety" patented technology

A computer-implemented system for personalized health optimization constructs a confidence-weighted personal health knowledge graph (PHKG) from heterogeneous data, including wearable sensors, medical devices, lab results, medication logs, and conversational inputs. A multi-stage causal-inference stack identifies modifiable drivers of outcomes using layered methods (e.g., MI, GAM, Neural Granger, DAG-GNN), and simulates candidate interventions. A recommendation engine ranks lifestyle or pharmacologic actions using a benefit-to-friction score, selecting a personalized intervention aligned with user readiness and clinical safety constraints. Interventions may include a minimum effective dose (MED), optimal level, adaptive low-dose, or behavioral challenge. Optional modules include reinforcement learning for timing adaptation and privacy-preserving on-device inference. The system operates across domains including metabolic, cardiovascular, renal, sleep, stress, and medication response, enabling cross-condition synergy evaluation. The architecture is modular, supports runtime plug-in targets, and adapts in real time with or without continuous clinical oversight, depending on deployment.

The invention relates to the technical field of medical equipment, and discloses a multi-head transfusion intelligent switching control system, which comprises the following modules interconnected with a control bus through a data bus: a multi-source information binding module; a multi-modal sensing data fusion module; an intelligent safety control module; an edge-cloud collaborative decision module; a human factor engineering interaction module; a system full life cycle management module; and an infusion pipeline switching execution mechanism. Through the multi-mode sensing fusion and real-time data processing technology, the whole-course automatic monitoring of the infusion progress, the flow speed and the remaining medicine amount is achieved. The nurse station central billboard and the mobile terminal synchronously display infusion states of all beds, sound-light alarm is triggered in time when abnormal conditions occur, manual patrol video frequency is remarkably reduced, an edge computing node is applied to carry out localized data analysis, and it is ensured that alarm response delay is lower than a clinical safety threshold value. And the problem of low efficiency caused by manual regular patrol traditionally is effectively solved.

The invention relates to the technical field of biomedical engineering, and discloses an acute respiratory distress syndrome mechanical ventilation adaptive adjustment control system, which comprises a data acquisition module, a signal preprocessing module, a physiological parameter calculation module, a prediction module, a decision and control logic module and the like. Wherein the prediction module predicts a dynamic lung compliance change trend by using a long short-term memory neural network model, and the decision and control logic module generates a ventilation parameter adjustment instruction according to a prediction result and a clinical safety rule. By adopting the technical scheme, the system overcomes the hysteresis of traditional feedback regulation, reduces the risk of breathing machine related lung injury, optimizes oxygenation and carbon dioxide removal efficiency, and provides an accurate, safe and individualized mechanical ventilation treatment scheme.

The invention relates to the technical field of traditional Chinese medicine acupuncture and moxibustion and big data processing, in particular to a massive human tissue structure big data accurate identification method and system for traditional Chinese medicine acupuncture and moxibustion, and the system comprises a data fusion module, a knowledge graph module, a baseline model construction module, an effect prediction module, a remote monitoring module and a curative effect evaluation module. Compared with the prior art, a closed loop of'precise modeling-real-time feedback-intelligent optimization-safe evidence storage 'is formed through deep fusion of data driving and an intelligent algorithm, the acupuncture point positioning precision is improved by 30% or above, the risk early warning response time is shortened to be within 200 ms, the curative effect prediction accuracy rate reaches 85%, and the method has the advantages of being high in practicability and easy to popularize. Systematic technical support is provided for standardization and intellectualization of acupuncture treatment, and clinical safety and effectiveness are remarkably improved.

The invention discloses a medication assisting system and device based on symptom-medicine two-way verification, and belongs to the technical field of intelligent medical treatment. The system comprises a data preprocessing module, an entity extraction and alignment module, an atlas retrieval module, a case retrieval module and a bidirectional verification and auxiliary decision module. The system extracts a medical entity through an AC automaton rule engine and a knowledge enhanced BERT model, and realizes mapping with a knowledge graph entity by adopting a two-stage term alignment strategy; performing knowledge graph query by adopting an operation chain generation mechanism driven by cue words to obtain a candidate drug set; meanwhile, similar cases are retrieved based on vector similarity, and an associated drug set is generated; and finally, fusing external medicine knowledge base data, executing a bidirectional verification and completion strategy, and outputting medicine recommendation with clinical safety and coverage range. The invention further provides a device structure for realizing the functions of the system, and the accuracy and interpretability of clinical medication aid decision making are effectively improved.

The invention relates to the technical field of medical information, and particularly discloses an AI-based medical record intelligent analysis and pre-filling system, which quantifies the certainty degree of clinical expression by constructing a medical entity uncertainty characteristic spectrum, and analyzes the semantic influence of quantified modifiers in combination with the context influence, so that the accuracy of medical record analysis and pre-filling is improved. A multi-objective optimization algorithm is adopted to cooperatively balance clinical safety, data integrity and filling efficiency objectives; and finally, performing ontology alignment and logic verification based on the medical knowledge graph, and implementing a differential pre-filling strategy according to a determinacy level. According to the method, the uncertainty semantics in the medical text can be accurately analyzed, the accuracy and clinical reliability of medical record data processing are improved, and meanwhile, self-adaptive optimization of intelligent pre-filling is realized.

The invention provides a time-space consistency testing method and device for cross-time-zone medical cooperation and a readable storage medium, and belongs to the technical field of medical information. The system aims at solving data conflicts, decision delay and clinical risks caused by space-time dislocation in cross-time zone medical cooperation. The system comprises a space-time scene configurator, a space-time label generator, a conflict resolution algorithm library and a clinical influence evaluator. Wherein the space-time label generator adds a four-dimensional coordinate label for medical operation and solves the problem of time zone conversion boundary; the conflict resolution algorithm library dynamically processes concurrent operation conflicts based on priorities, and combines automatic coverage with an artificial arbitration mechanism; a clinical impact evaluator quantifies the time lag risk. Through the system and the method, the conflict rate of the electronic medical records is reduced by 91%, the emergency operation response delay is shortened by 96%, the clinical decision error rate is reduced by 92%, the robustness of the cross-time-zone medical cooperation system is effectively verified, and the clinical safety is improved.

The invention discloses a medical vaporizer verification system, and relates to the technical field of medical instrument detection. Comprising an environment perception acquisition module, an environment data preprocessing and structuring module, a condensation risk feature extraction and risk value quantification module, a deep learning driven condensation risk intelligent determination module and a particle dynamic identification and condensate droplet elimination module. Multi-dimensional physical parameters of environmental conditions are obtained in real time through a multi-type high-sensitivity sensor array arranged in a detection cabin. According to the invention, environmental changes are monitored in real time through a multi-sensor array, condensation risk features are extracted, and an intervention mechanism is triggered in advance by using deep learning intelligent pre-judgment; ultrasonic excitation and a laser interferometer are combined to collect particle vibration characteristics, and machine learning is matched to quickly distinguish drug particles and condensate drops, so that errors are effectively eliminated, the verification accuracy of an atomizer and the intelligent level of the system are improved, and the detection reliability and clinical safety are ensured.

The invention discloses a medical equipment state monitoring and early warning method and system based on a full life cycle, and relates to the technical field of medical equipment state monitoring and early warning. Comprising a full-life-cycle data acquisition module, a multi-dimensional data preprocessing module, a full-life-cycle state evaluation module, a hierarchical early warning decision module, a full-life-cycle management module and an edge-cloud collaborative storage module. The full-life-cycle data acquisition module is used for acquiring multi-dimensional data of the medical equipment in the whole stage from purchase acceptance, operation and use, operation and maintenance to scrap evaluation, and outputting original data in the whole stage. Differentiation quantitative indexes are set for different life cycle stages, an evaluation model is constructed by adopting an analytic hierarchy process and dynamic weight adjustment, and an early warning threshold value and a four-level hierarchical response mechanism are dynamically adjusted in combination with a gradient lifting tree algorithm. The problems that a traditional evaluation mode is poor in adaptability, early warning misinformation lags behind, response pertinence is insufficient, and clinical safety guarantee is weak are solved.

The invention discloses a highly inert Gd-DOTA-based contrast agent with a hydrophobic side chain as well as a preparation method and application of the highly inert Gd-DOTA-based contrast agent, and two novel Gd-DOTA-based contrast agents Gd-L1 and Gd-L2 are successfully developed by respectively connecting hydrophobic 3-phenyl propylamine and 3, 3-diphenyl propylamine to Gd-DOTABA. The two novel Gd-DOTA-based contrast agents remarkably improve the chemical inertness of the complex, the dissociation half-life period of the complex in a 1 M HCl solution at 37 DEG C is about 5 times that of Gd-DOTA, and a powerful guarantee is provided for clinical safety; the MRI (Magnetic Resonance Imaging) performance of the liver in a mouse shows that Gd-L1 is equivalent to commercially available Gd-BOPTA and is suitable for imaging of a liver and gall system; after the Gd-L2 is combined with the human serum albumin, the longitudinal relaxation rate is obviously increased (r1 = 14.8 mM <-1 > s <-1 >, 1.4 T, 37 DEG C), the blood elimination half-life period in a rabbit model is about 18 minutes, and the Gd-L2 is suitable for vascular MRI (Magnetic Resonance Imaging). The two contrast agents avoid the spleen residual problem, the organ accumulation possibility is reduced, and a safer and more efficient MRI diagnostic tool is provided for clinic.

The invention discloses a model uncertainty-driven man-machine collaborative risk grading interpretation method, system and device and a computer readable storage medium. According to the method, under federated learning deployment, calibration confidence, bucket-level calibration deviation and multi-model inconsistency are simultaneously calculated for a single sample, and a comprehensive uncertainty score is formed to perform risk grading: when the comprehensive uncertainty score exceeds a threshold value or the calibration confidence is insufficient, manual re-checking is automatically triggered; otherwise, directly outputting the AI result. Artificially confirmed samples enter a feedback sample library for subsequent federation retraining, temperature parameters and barrel counting are periodically updated, and a continuous learning closed loop is constructed. According to the scheme, in medical scenes such as lung CT nodule detection and lung cancer I-stage recurrence risk prediction, the diagnosis efficiency and clinical safety are effectively considered, and the long-term stability and credibility of the model are improved.

The invention relates to the technical field of medical temperature control, and discloses an ice blanket machine dynamic temperature control method based on body temperature feedback. The method comprises the following steps: acquiring body surface temperature sensing data and core temperature simulation data of a target object through body temperature multi-source sensing, and constructing an original body temperature sensing data set; generating a physiological state feature vector according to the data, and performing physiological state evaluation; based on the feature vector, generating a multi-modal body temperature state tensor through multi-source sensing fusion modeling; then, an optimized temperature control strategy is generated according to the multi-modal body temperature state tensor by adopting a reinforcement learning method for dynamic binding of a clinical constraint rule component; performing integrity verification and clinical safety rule conflict detection on the optimization strategy to generate a safety control instruction sequence; and finally, the ice blanket machine is driven to execute temperature regulation and control operation according to the sequence. According to the method, the accuracy and safety of temperature regulation and control of the ice blanket machine are improved, and complex clinical scene requirements are met.

The invention provides a robot treatment bed positioning error correction method. The method comprises three steps of coordinate system establishment and calibration, positioning error detection and calculation and error correction. A first reflection target ball is installed at an angular point of a treatment bed, a high-precision optical positioning system is used for obtaining coordinates of a radiotherapy isocenter point, and a conversion relation between a base coordinate system and an optical positioning system coordinate system is established. Controlling the treatment bed to move to four non-coplanar feature points, collecting actual coordinates, comparing the actual coordinates with theoretical coordinates, calculating an error matrix of a base coordinate system, solving a target pose matrix, driving the robot treatment bed to adjust the tail end pose, and realizing accurate alignment of a treatment center and an isocenter. The method can detect and automatically compensate systematic pose deviation caused by mechanical wear in real time, has the advantages of high precision, high automation degree, correction from an error source and the like, and effectively guarantees long-term positioning precision and clinical safety of a radiotherapy system.

The present invention provides the use of Feilike in the preparation of products for the prevention and treatment of liver cancer, belonging to the technical field of liver cancer prevention and treatment. This invention proposes for the first time the use of Feilike in the preparation of products for the prevention and treatment of liver cancer, specifically inhibiting the vitality and proliferation of liver cancer cells, promoting apoptosis of liver cancer cells, and altering the morphology of liver cancer cells, thereby achieving the effective treatment of liver cancer. Furthermore, Feilike is a traditional Chinese medicine already in clinical use that can be directly applied to the human body without the need for clinical safety assessments. It has excellent application prospects and can rapidly increase the market share of this product.

The invention relates to the technical field of orthodontics and medical image processing, in particular to an artificial intelligence-based automatic positioning method and system for a maxillary bone expansion device. The positioning method comprises the following steps: taking a cone beam computed tomography image and intraoral scanning data of a patient; segmenting a key anatomical structure based on the scanned image, wherein the key anatomical structure comprises an incisor foramen and a transverse palate slit; automatically identifying a plurality of cranio-maxillofacial mark points based on the scanned image, and establishing a maxilla three-dimensional coordinate system; based on the three-dimensional coordinate system, the intraoral scanning data and the key anatomical structure, positioning adjustment is carried out on the arch expander to determine the final positioning posture, and positioning adjustment comprises the steps that the safe distance between the arch expander and the surface of the palate mucosa is screened, and contact with the key anatomical structure is avoided; and outputting the three-dimensional parameters of the final positioning posture. According to the invention, the automatic, intelligent and safe placement of the arch expanding device is realized, so that the design efficiency is improved, the operation difficulty is reduced, and the clinical safety and the arch expanding effect are guaranteed.

The invention relates to the technical field of biological medicine, and discloses a nanoparticle vaccine based on an HER2 target spot and application of the nanoparticle vaccine, and the nanoparticle vaccine based on the HER2 target spot comprises an HER-2 gene related antigen sequence HER2 and hepatitis B core virus-like particle nanoparticles HBc VLPs; the amino acid sequences of the gene are respectively shown as SEQ ID No. 1 and SEQ ID No. 2. According to the invention, hepatitis B core virus-like particles are modified by using B cell epitopes of HER2 targets, and the nanoparticle tumor vaccine suitable for intramuscular injection is prepared. The vaccine aims at reducing clinical safety risks and reducing immune tolerance and toxic and side effects caused by long-term use of monoclonal antibody drugs. Through an active immunization mode, an organism can continuously generate an anti-HER2 specific antibody, and clinical guidance is provided for research and development of therapeutic general tumor vaccines.

This invention belongs to the technical field of medicinal chemistry and provides a milrinone-tartaric acid cocrystal. Using Cu-Kα radiation, its X-ray diffraction pattern (expressed as 2θ) exhibits characteristic peaks at at least 10.0±0.2°, 13.2±0.2°, 20.4±0.2°, 24.7±0.2°, 26.2±0.2°, 26.7±0.2°, and 34.3±0.2°. The cocrystal of this invention has high solubility and good stability. Using this cocrystal to prepare formulations avoids the use of large amounts of excipients and auxiliaries in existing technologies, reducing both production costs and clinical safety risks. The preparation method of the milrinone-tartaric acid cocrystal provided by this invention is simple to operate, the crystallization process is easy to control, and it has good reproducibility.

The application discloses a kind of based on active learning and man-machine collaborative optimization's massaging robot control method, it is related to artificial intelligence, medical rehabilitation robot and complex contact type operation control cross technical field, this method relies on multimodal perception system, core computing hub and remote man-machine collaborative interface control platform, in turn complete the initial massaging strategy model construction of multi-source heterogeneous expert data, the uncertainty measurement based on bayesian neural network, man-machine collaborative key frame active learning trigger, reinforcement learning reward function design of fusion patient biological feedback and incremental learning of impedance control parameter and force-position hybrid output;The application greatly improves the clinical safety of massaging robot, exponentially reduces data acquisition cost, realizes individual physiological closed-loop rigid-flexible massaging, also overcome the catastrophic forgetting problem of neural network, endow system lifelong learning ability.

This invention relates to the interdisciplinary field of image recognition and intelligent medical technology, specifically disclosing a method for orthopedic rehabilitation movement recognition and assessment based on image recognition. This method acquires patient motion images using a non-invasive multi-view visual sensor, extracts the three-dimensional coordinates of key skeletal points, and constructs a parametric biomechanical digital twin model integrating joint range of motion, muscle force lines, and ligament tension mechanisms. The key point data is input into a graph neural network for posture recognition, and the digital twin model is simultaneously invoked for physical feasibility verification and posture correction. Finally, the spatiotemporal trajectory similarity is calculated using a standard rehabilitation movement template, generating a comprehensive assessment result including movement completion, safety level, and injury risk. This invention, through the above technical solution, improves the robustness, physiological rationality, and clinical safety of movement recognition.

The present application relates to a kind of intelligent chest negative pressure drainage device, including electronic negative pressure device and chest closed drainage bottle;Electronic negative pressure device is equipped with negative pressure component;Negative pressure component includes: negative pressure pipe;Negative pressure connector;Negative pressure pump;And check valve;Wherein, electronic negative pressure device power consumption realizes active negative pressure mode;Electronic negative pressure device power consumption realizes chest closed drainage mode when power is exhausted.Through the combination of mechanical structure and electronic control, with " active negative pressure " and " chest closed drainage " two working modes, realize active negative pressure mode when power is normal;When power interruption or system failure, automatically, seamless switch to water seal drainage mode without power, switching process is based on mechanical characteristics, without manual intervention, greatly improve clinical safety, completely solve the failure risk of electronic system when power off.Combination water seal liquid physical barrier and check valve structure, form double anti-reflux protection, can effectively prevent gas or liquid reflux.

The application relates to the technical field of medical information, and particularly discloses an AI-based medical record intelligent analysis and pre-reporting system, which quantifies the certainty degree of clinical expression by constructing a medical entity uncertainty feature spectrum, quantifies the semantic influence of a modifier by combining context influence analysis, and balances the clinical safety, data integrity and reporting efficiency targets by adopting a multi-objective optimization algorithm; finally, ontology alignment and logical verification are performed based on a medical knowledge graph, and a differentiated pre-reporting strategy is implemented according to the certainty level; the application can accurately analyze the uncertainty semantics in medical texts, improve the accuracy and clinical reliability of medical record data processing, and realize adaptive optimization of intelligent pre-reporting.

A three-dimensional (3D) printing method for manufacturing metal embedded medical implants and devices is provided. The newly developed 3D printing technology and surface modification method will contribute to wider application of 3D printed patient-customized products in many medical fields, thereby exhibiting excellent clinical safety.

The present invention provides a developable hydrogel, comprising a first component and a second component; the first component is a multi-arm polyethylene glycol terminated with a maleimide group; the second component is an iodine-containing contrast agent modified with a thiol group. The components of the hydrogel of the present invention can be formulated as a neutral solution, react mildly under a physiological environment, have high safety, and the formed hydrogel has good biocompatibility. The hydrogel of the present invention does not cause hyperosmotic phenomena in vivo, and the hydrophilic iodine molecular units after degradation are easily metabolized and excreted from the body. The two components of the hydrogel of the present invention are used at a low concentration, which greatly reduces the raw material concentration and the usage amount, which can not only reduce costs, but also be more friendly to the human body and have better clinical safety. The hydrogel of the present invention can achieve precise adjustment of a wide gel time window of 2-120s, and the development value can be quantitatively regulated at 80-400Hu, adapted to the application requirements of different tissues, and the degradation cycle of the gel in vivo can be adjusted from 1 week to 24 weeks.

This invention discloses the application of the Miao medicine Liao's Huafengdan in the preparation of drugs for the prevention and treatment of multiple myeloma, belonging to the field of pharmaceutical application technology. In vitro detection using RPMI8226 and MM1.S multiple myeloma cells revealed that Liao's Huafengdan has a significant inhibitory effect on multiple myeloma. Flow cytometry analysis further showed its effect on cell apoptosis, indicating that Liao's Huafengdan significantly affects apoptosis in multiple myeloma cells, with higher drug concentrations leading to more pronounced apoptosis. Therefore, it can be used as a preparation for the prevention and treatment of multiple myeloma, and can be formulated into pharmaceutically permissible oral liquids, injections, tablets, capsules, and drop pills, providing a new application for Liao's Huafengdan. Moreover, Liao's Huafengdan is already a marketed drug and can be directly applied to humans without further clinical safety assessments, which can rapidly increase the market share of this product.

The invention discloses an optimization management method for cardinality drugs in a cardiology department ward, and relates to the technical field of medical treatment, and the method comprises the steps: collecting the physiological data of a patient in real time, extracting PQRST wave band abnormal features and blood pressure crisis threshold features through a nerve symbol system, and generating a medication decision; based on the medication decision and the basic illness state data of the patient, a dynamic demand graph with ward beds as nodes and medication demands as attributes is constructed, node state changes are analyzed through a space-time diagram neural network, and a cross-bed collaborative cardinal number medicine adjustment instruction is output; updating the cardinal number stock of the target medicine cabinet according to the cardinal number medicine adjusting instruction, and verifying the authenticity of the cardiovascular high-risk medicine through a medicine identifier during medicine taking; monitoring a physiological index of a patient after administration, and when the physiological index breaks through a clinical safety threshold, starting an emergency cardinal number drug and marking stock change; and when the physiological indexes deviate from the expected curative effect range, adjusting the administration scheme and synchronously updating the medication demand attributes of the nodes of the dynamic demand graph.

The invention relates to a disposable visible nasointestinal tube, a nasointestinal tube assembly and a control method of the nasointestinal tube assembly. The disposable visible nasointestinal tube comprises a connector used for being connected with a multiplexing device; the catheter is provided with a first channel and a second channel which are through in the far and near end direction, the far end of the second channel is sealed by the light-transmitting part of the catheter, and the near end of the catheter is connected to the connector; the camera module is arranged in the second channel of the catheter and is isolated from the first channel, a camera of the camera module is fixed to the far end of the second channel and is tightly attached to the catheter, and the view field of the camera module covers at least one part of the area of the far side of the catheter through the light transmitting part. The disposable visible nasointestinal tube integrates clinical safety, product reliability and cost controllability; the camera module and the conduit are integrated, so that the structure is compact, and operation is facilitated; the camera module and the catheter are disposable and discarded, the camera module does not need to be disinfected any more, and the device easy to disinfect serves as a reuse device, so that the single-time use cost can be controlled on the whole.

This invention discloses an epidural anesthesia assistance method and system based on pressure curve characteristics, including a data acquisition and preprocessing module, an anatomical layer segmentation module, a multidimensional feature extraction module, a template matching and initial classification module, a Bayesian probabilistic inference and fusion decision-making module, a risk assessment and feedback output module, and an online adaptive update module. By utilizing real-time pressure monitoring, negative pressure recognition, and biometric frequency extraction technologies, it assists in determining the position of the epidural puncture needle, significantly reducing the rate of epidural puncture mispuncture and improving clinical safety. Through a Bayesian online learning update mechanism, the model parameters can be continuously iterated based on clinically labeled data, adapting to different operator habits and sensor drift, achieving long-term stable operation and individualized calibration. This invention is easy to integrate and has low hardware costs, facilitating large-scale clinical deployment.

This invention discloses a front-end interaction and clinical safety control method and system for boron neutron capture therapy, comprising the following steps: constructing a patient-specific treatment model; during treatment, collecting real-time monitoring data streams containing boron concentration data and physiological state signals through biosensors; inputting the monitoring data streams into the patient-specific treatment model to predict future outcomes; based on the prediction results, generating evaluation results in real time through a dynamic optimization algorithm; arbitrating real-time control commands and physiological state signals for safety, and ultimately controlling the treatment execution device; and displaying the real-time monitoring data streams, prediction results, control commands, and safety status through a unified interactive interface. This invention achieves continuous and prospective prediction of boron drug distribution and radiation dose by constructing a treatment model that integrates the patient's unique anatomical and physiological information and dynamically driving the model using real-time collected in vivo data.

The application relates to the technical field of medical big data processing, and relates to a medical data classification and labeling method and system applied to big model mining, which comprises a heterogeneous data phasor measurement unit, which samples multi-modal medical data and calculates a semantic deviation angle and a semantic angular velocity; a transient energy evaluation engine, which is configured with a Lyapunov function calculation core and is used for real-time calculation of semantic kinetic energy, semantic potential energy and instantaneous total energy of the sample; a stability discrimination and shunt controller, which calculates an energy margin and shunts the data to different channels; and an excitation feedback adjustment module, which executes an adaptive excitation control strategy and dynamically adjusts big model parameters in the medical field. The application quantifies data semantic reliability by using transient energy stability theory, accurately identifies critical high-value samples by calculating an energy margin, effectively suppresses model hallucinations by adaptive parameter adjustment while greatly reducing manual labeling costs, and significantly improves the clinical safety and robustness of the medical big model.

The present application relates to the technical field of dental instrument material performance evaluation, in particular to a multi-dimensional performance grading evaluation system and method for dental shell instrument membrane materials. The system includes a hierarchical modeling module, an index collection module, a data processing module, a weight adjustment module and a result output module, constructs three layers of performance structure of basic clinical safety layer, core correction function layer and advanced service efficiency layer, and is associated with dimensional indexes such as clinical quality characteristics, biological safety, orthodontic mechanical characteristics, processing process characteristics and appearance characteristics. The measured data are obtained by standardized testing, normalized processing and dynamic weight adjustment combined with a rule engine or a machine learning model to generate a comprehensive score and grade determination. The present application overcomes the defects of index dispersion, lack of weight and hierarchical division in the prior art, realizes objective and comparable evaluation of the performance of different membrane materials, and improves the scientificity of clinical application and industry research and development.