Nasointestinal tube indwelling qualification authentication and examination system

Abstract

The invention discloses a nasointestinal tube indwelling qualification authentication and examination system. The system comprises a self-adaptive virtual reality training module, a multi-mode artificial intelligence evaluation module, a quantum encryption biological recognition module, a distributed account book recording module and an artificial intelligence auxiliary decision making and risk early warning module. The system provides a vivid training environment through a virtual reality technology, realizes objective evaluation by utilizing artificial intelligence, ensures safety by adopting quantum encryption, ensures data credibility by applying a distributed account book, and assists decision making and risk early warning through artificial intelligence. According to the method, the nasointestinal tube indwelling training quality and the certification efficiency are improved, the objectivity and the safety of evaluation are enhanced, the medical risk is effectively reduced, and an important contribution is made for improving the medical safety and the patient nursing level.

Description

Technical Field

[0001] The present invention relates to the technical field of medical training and qualification certification, and in particular to a nasoenteric tube placement qualification certification and review system. Background Art

[0002] Nasogastric intubation is a common medical procedure used to provide nutritional support or gastrointestinal decompression for patients who are unable to eat normally. This procedure requires professional medical skills, so systematic training and qualification certification of medical staff is essential. The Nasogastric Intubation Qualification Certification and Review System aims to ensure that medical staff have the necessary knowledge and skills through standardized training, assessment and supervision processes, thereby improving patient safety and treatment outcomes.

[0003] Currently, the training and qualification certification of nasoenteric tube placement mainly relies on the traditional method of combining theoretical learning and practical operation. This method usually includes classroom teaching, simulated operation training and actual case practice. However, this traditional method has some limitations: the difference between the training environment and the real medical scenario, the subjectivity of the evaluation criteria, the lack of real-time supervision and feedback, and the difficulty of large-scale, standardized qualification certification and review. In addition, the existing training and certification systems often lack personalized learning path design, and it is difficult to adapt to the learning needs and progress of different trainees.

[0004] Therefore, the main technical problem facing the current nasoenteric tube placement qualification certification and review system is how to build a comprehensive, objective, efficient and safe training and evaluation platform. This platform needs to be able to provide a real simulation environment, achieve accurate skill assessment, ensure the safety and reliability of the certification process, and at the same time be able to adapt to the personalized learning needs of different trainees. Solving this technical problem will help improve the operating skills of medical staff, reduce medical risks, and ultimately improve the quality and safety of patient treatment. Summary of the invention

[0005] In order to solve the problems of the prior art, an embodiment of the present invention provides a nasogastric tube placement qualification certification and review system. The technical solution is as follows:

[0006] On the one hand, a nasoenteric tube placement qualification certification and review system is provided, including an adaptive virtual reality training module, a multimodal artificial intelligence evaluation module, a quantum encryption biometric module, a distributed ledger recording module and an artificial intelligence-assisted decision-making and risk warning module; the adaptive virtual reality training module includes a scene construction unit, an interaction design unit and an adaptive learning algorithm unit; the multimodal artificial intelligence evaluation module includes a visual analysis unit, a speech analysis unit and a motion analysis unit; the quantum encryption biometric module includes a quantum key distribution unit, a multimodal biometric unit and an anti-deception mechanism unit; the distributed ledger recording module includes a directed acyclic graph structure optimization unit, a smart contract unit and a data management unit; the artificial intelligence-assisted decision-making and risk warning module includes a causal reasoning engine unit, a federated learning framework unit and a real-time monitoring unit; the modules are interconnected through a secure communication network to jointly realize the full-process intelligent certification and review of nasoenteric tube placement qualifications.

[0007] Furthermore, the scene construction unit of the adaptive virtual reality training module adopts high-precision three-dimensional scanning technology to capture the real medical environment, and uses programmatic generation technology to dynamically create diversified ward scenes; the interactive design unit integrates hand tracking and tactile feedback technology; and the adaptive learning algorithm unit adopts the Upper Confidence Bound algorithm to realize intelligent training content adjustment.

[0008] Furthermore, the visual analysis unit of the multimodal artificial intelligence evaluation module adopts a deep learning model for real-time object detection and posture estimation; the speech analysis unit uses a speech recognition model and a natural language processing model fine-tuned in the medical field; and the motion analysis unit adopts skeleton point tracking and Dynamic Time Warping algorithm for motion similarity calculation.

[0009] Furthermore, the quantum key distribution unit of the quantum encryption biometric module adopts a continuous variable quantum key distribution protocol; the multimodal biometric unit uses a deep learning model to fuse multiple biometric features; and the anti-spoofing mechanism unit adopts deep pixel-level depth map reconstruction technology and thermal imaging liveness detection technology.

[0010] Furthermore, the directed acyclic graph structure optimization unit of the distributed ledger recording module is based on the IOTATangle 2.0 protocol and adopts the Markov ChainMonte Carlo random walk algorithm for node selection; the smart contract unit uses the WebAssembly virtual machine to support multi-language smart contract development; and the data management unit uses a star topology structure to store data.

[0011] Furthermore, the causal reasoning engine unit of the artificial intelligence-assisted decision-making and risk warning module adopts the PC algorithm for structural learning; the federated learning framework unit uses the FATE framework to implement model training under privacy protection; and the real-time monitoring unit adopts the optical flow estimation algorithm to analyze hand motion trajectories and abnormal behavior detection based on behavior trees.

[0012] Furthermore, the scene construction unit uses the Unity engine for three-dimensional data processing and rendering; the interactive design unit uses Leap Motion Controller for hand tracking and HaptX Gloves for tactile feedback.

[0013] Furthermore, the visual analysis unit adopts the YOLOv5s model for real-time object detection and ResNet152 for posture estimation; the speech analysis unit adopts the Wav2Vec 2.0 model for speech recognition and the BERT model for natural language processing; and the motion analysis unit adopts the OpenPose framework for skeleton point tracking.

[0014] Furthermore, the multimodal biometric recognition unit adopts a deep learning model of the ResNet-50 architecture to fuse fingerprint, iris, facial features, electrocardiogram and electroencephalogram data; the anti-spoofing mechanism unit adopts the U-Net++ architecture for depth map reconstruction and the FLIR Lepton 3.5 thermal imaging sensor for liveness detection.

[0015] Furthermore, the node selection of the directed acyclic graph structure optimization unit adopts weight-based transfer probability calculation; and the data management unit adopts high-performance SSD storage as the central node.

[0016] The technical solution provided by the embodiment of the present invention has the following beneficial effects:

[0017] The present invention integrates advanced virtual reality, artificial intelligence, quantum encryption and distributed ledger technology to build a comprehensive, intelligent and safe nasoenteric tube placement qualification certification and review system. The system provides a highly realistic virtual training environment, greatly improving the training effect and efficiency. Multimodal artificial intelligence assessment ensures the objectivity and comprehensiveness of skill evaluation. The application of quantum encryption technology and distributed ledger significantly enhances the security of the system and the credibility of data. The adaptive learning algorithm realizes personalized training to meet the needs of different trainees. Real-time monitoring and risk warning functions effectively reduce the risk of medical operations. In addition, the modular design of the system facilitates future expansion and upgrading, and can continue to adapt to the development of medical technology.

[0018] In general, the present invention significantly improves the quality and efficiency of nasogastric tube placement qualification certification, and makes an important contribution to improving medical safety and patient care levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A module schematic diagram of a nasoenteric tube placement qualification certification and review system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0022] The present invention provides a nasoenteric tube placement qualification certification and review system, which consists of five core modules: an adaptive virtual reality training module, a multimodal artificial intelligence evaluation module, a quantum encryption biometric module, a distributed ledger recording module, and an artificial intelligence-assisted decision-making and risk warning module. These modules are interconnected through a secure communication network to form a complete closed-loop system, thereby realizing the full-process intelligent certification and review of nasoenteric tube placement qualifications. In this system, the adaptive virtual reality training module provides an immersive training environment for medical staff, the multimodal artificial intelligence evaluation module evaluates the training and actual operation process in real time, the quantum encryption biometric module ensures the security of system access and the reliability of identity authentication, the distributed ledger recording module securely stores all training, evaluation and certification data, and the artificial intelligence-assisted decision-making and risk warning module provides decision support and risk warnings based on the collected data. The collaborative work of these five modules constitutes a complete, secure, and intelligent nasoenteric tube placement qualification certification and review system. The overall architecture of the system is as follows Figure 1 shown.

[0023] The adaptive virtual reality training module is the basis of this system, which includes a scene construction unit, an interaction design unit, and an adaptive learning algorithm unit. The scene construction unit uses the Artec Leo high-precision 3D scanner to capture the real medical environment with a resolution of 0.2mm. The collected 3D data is processed and rendered through the Unity 2022.3LTS engine. At the same time, the unit uses procedural generation technology to dynamically create diverse ward scenes to provide a more realistic and varied training environment. The interaction design unit integrates the Leap Motion Controller to achieve hand tracking with an accuracy of 0.1mm, and uses the HaptXGloves DK2 tactile feedback gloves to provide 16 touch points and finger resistance feedback to enhance the realism of the operation. The adaptive learning algorithm unit realizes intelligent training content adjustment, the core of which is the Upper Confidence Bound (UCB) algorithm, the formula is as follows:

[0024]

[0025] Among them, a t represents the action (training content) selected at time step t, is the estimated value of action a, c is the exploration parameter that controls the balance between exploration and exploitation, t is the current time step, and N t (a) is the number of times action a is selected. This algorithm can effectively balance known high-value content and under-explored content, thereby optimizing the learning experience.

[0026] The multimodal artificial intelligence evaluation module is the core evaluation component of the system, which includes a visual analysis unit, a speech analysis unit, and a motion analysis unit. The visual analysis unit uses the YOLOv5s model for real-time object detection with a processing speed of 30FPS, and uses ResNet152 for posture estimation with an accuracy of 95%. The speech analysis unit uses the Wav2Vec 2.0 model for speech recognition with an error rate of less than 5%, and implements a BERT-based Chinese medical field fine-tuning model to accurately understand medical terminology. The motion analysis unit uses the OpenPose framework for skeleton point tracking and supports real-time detection of 25 key points. The motion similarity calculation uses the Dynamic Time Warping (DTW) algorithm, and its core recursive formula is as follows:

[0027]

[0028] In this formula, DTW(i,j) represents the DTW distance between the first i elements of sequence X and the first j elements of sequence Y, d(x i ,y j ) is an element x i andj The distance metric between i is the i-th element in the student's action sequence, y j is the jth element in the canonical action sequence. This algorithm can effectively compare action sequences of different lengths and provide accurate similarity assessment.

[0029] The quantum encryption biometric module ensures the security of the system. It includes a quantum key distribution unit, a multimodal biometric unit and an anti-spoofing mechanism unit. The quantum key distribution unit implements a continuous variable quantum key distribution (CV-QKD) protocol, using Gaussian modulated coherent states, with a transmission distance of 100km. The multimodal biometric unit uses a deep learning model based on the ResNet-50 architecture to fuse fingerprints, irises, facial features, electrocardiogram (ECG) and electroencephalogram (EEG) data to provide multi-dimensional identity authentication. The anti-spoofing mechanism unit uses deep pixel-level depth map reconstruction technology, based on the U-Net++ architecture to prevent flat photo attacks, and implements a liveness detection system based on the FLIRRepton3.5 thermal imaging sensor to effectively prevent various deception methods.

[0030] The distributed ledger record module ensures the reliable storage and management of data. It includes a directed acyclic graph structure optimization unit, a smart contract unit, and a data management unit. The directed acyclic graph structure optimization unit is based on the improved IOTATangle2.0 protocol and uses the MCMC (MarkovChainMonte Carlo) random walk algorithm for node selection. The transfer probability formula is as follows:

[0031]

[0032] In this formula, P(i→j) represents the probability of transferring from node i to node j, and W ij is the weight of node i to node j (usually based on the number of transaction confirmations and age), is the weight sum of all outgoing edges of node i. This algorithm selects nodes by random walk, which helps to achieve fast and decentralized consensus. The smart contract unit uses the WebAssembly (Wasm) virtual machine, supports multi-language smart contract development, and improves execution efficiency by 30%. The data management unit uses a star topology to store relevant research and case data, and the central node uses high-performance SSD storage to ensure efficient access and management of data.

[0033] The AI-assisted decision-making and risk warning module is the intelligent decision-making center of the system, which includes a causal reasoning engine unit, a federated learning framework unit, and a real-time monitoring unit. The causal reasoning engine unit uses the PC algorithm for structural learning, automatically discovers the causal relationship between variables, and implements causal effect estimation based on double machine learning, providing reliable theoretical support for decision-making. The federated learning framework unit adopts the FATE (Federated AI Technology Enabler) framework, supports horizontal and vertical federated learning, and uses homomorphic encryption technology to protect the model parameter transmission process, realizing model optimization under the premise of protecting privacy. The real-time monitoring unit uses the Farneback optical flow estimation algorithm to analyze the hand movement trajectory with sub-pixel accuracy, and implements abnormal behavior detection based on the behavior tree to provide real-time risk warnings.

[0034] This nasoenteric tube placement qualification certification and review system has built a comprehensive, intelligent and secure medical training and certification platform by integrating advanced virtual reality, artificial intelligence, quantum encryption, distributed ledger and real-time monitoring technology. The main innovations and advantages of the system are reflected in the highly personalized adaptive learning system, all-round objective multimodal AI evaluation, theoretically absolutely safe quantum encryption technology, DAG structure distributed ledger that supports high concurrent operations, and reliable risk assessment based on causal reasoning. Through the synergy of these innovative technologies, this system can comprehensively improve the quality of medical training, certification reliability and operational safety, and provide better medical protection for patients. At the same time, the modular design of the system also facilitates future expansion and upgrading, and can be continuously optimized and improved according to the development of medical technology and new needs.

[0035] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A nasogastric tube placement qualification certification and review system, characterized in that: It includes an adaptive virtual reality training module, a multimodal artificial intelligence evaluation module, a quantum encryption biometric module, a distributed ledger recording module and an artificial intelligence-assisted decision-making and risk warning module; the adaptive virtual reality training module includes a scene construction unit, an interaction design unit and an adaptive learning algorithm unit; the multimodal artificial intelligence evaluation module includes a visual analysis unit, a speech analysis unit and a motion analysis unit; the quantum encryption biometric module includes a quantum key distribution unit, a multimodal biometric unit and an anti-deception mechanism unit; the distributed ledger recording module includes a directed acyclic graph structure optimization unit, a smart contract unit and a data management unit; the artificial intelligence-assisted decision-making and risk warning module includes a causal reasoning engine unit, a federated learning framework unit and a real-time monitoring unit; the modules are interconnected through a secure communication network to jointly realize the full-process intelligent authentication and review of nasogastric tube placement qualifications.

2. The nasogastric tube placement qualification certification and review system according to claim 1 is characterized in that: The scene construction unit of the adaptive virtual reality training module uses high-precision three-dimensional scanning technology to capture the real medical environment, and uses programmatic generation technology to dynamically create diverse ward scenes; the interactive design unit integrates hand tracking and tactile feedback technology; the adaptive learning algorithm unit uses the Upper Confidence Bound algorithm to achieve intelligent training content adjustment.

3. The nasogastric tube placement qualification certification and review system according to claim 1 is characterized in that: The visual analysis unit of the multimodal artificial intelligence evaluation module adopts a deep learning model for real-time object detection and posture estimation; the speech analysis unit uses a speech recognition model and a natural language processing model fine-tuned in the medical field; and the motion analysis unit adopts skeleton point tracking and Dynamic Time Warping algorithm for motion similarity calculation.

4. The nasogastric tube placement qualification certification and review system according to claim 1 is characterized in that: The quantum key distribution unit of the quantum encryption biometric module adopts a continuous variable quantum key distribution protocol; the multimodal biometric unit uses a deep learning model to fuse multiple biometric features; The anti-spoofing mechanism unit adopts deep pixel-level depth map reconstruction technology and thermal imaging liveness detection technology.

5. The nasogastric tube placement qualification certification and review system according to claim 1 is characterized in that: The directed acyclic graph structure optimization unit of the distributed ledger recording module is based on the IOTA Tangle 2.0 protocol and adopts the MarkovChainMonte Carlo random walk algorithm for node selection; the smart contract unit uses the WebAssembly virtual machine to support multi-language smart contract development; the data management unit uses a star topology structure to store data.

6. The nasogastric tube placement qualification certification and review system according to claim 1, characterized in that: The causal reasoning engine unit of the artificial intelligence-assisted decision-making and risk warning module adopts the PC algorithm for structural learning; the federated learning framework unit uses the FATE framework to implement model training under privacy protection; the real-time monitoring unit adopts the optical flow estimation algorithm to analyze the hand motion trajectory and abnormal behavior detection based on the behavior tree.

7. The nasogastric tube placement qualification certification and review system according to claim 2, characterized in that: The scene construction unit uses the Unity engine for three-dimensional data processing and rendering; the interactive design unit uses the Leap Motion Controller for hand tracking and the HaptX Gloves for tactile feedback.

8. The nasogastric tube placement qualification certification and review system according to claim 3 is characterized in that: The visual analysis unit uses the YOLOv5s model for real-time object detection and ResNet152 for posture estimation; the speech analysis unit uses the Wav2Vec 2.0 model for speech recognition and the BERT model for natural language processing; the motion analysis unit uses the OpenPose framework for skeleton point tracking.

9. The nasogastric tube placement qualification certification and review system according to claim 4, characterized in that: The multimodal biometric recognition unit uses a deep learning model with a ResNet-50 architecture to fuse fingerprint, iris, facial features, electrocardiogram and electroencephalogram data; the anti-spoofing mechanism unit uses a U-Net++ architecture for depth map reconstruction and a FLIR Lepton 3.5 thermal imaging sensor for liveness detection.

10. The nasogastric tube placement qualification certification and review system according to claim 5, characterized in that: The node selection of the directed acyclic graph structure optimization unit adopts weight-based transfer probability calculation; the data management unit adopts high-performance SSD storage as the central node.

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