Method and system for improving umbilical vein catheterization module of newborn umbilical cord model

Abstract

The invention belongs to the technical field of medical teaching information, and particularly relates to a neonatal umbilical cord model umbilical vein catheterization module improvement method and a neonatal umbilical cord model umbilical vein catheterization module improvement system. The technology focuses on clinical training pain points of umbilical vein catheterization in newborn intensive care (NICU), and a closed-loop training system of high-fidelity simulation, whole-course monitoring, accurate guidance and scientific evaluation is constructed through integrated design of bionic material preparation, multi-parameter sensing, intelligent algorithm regulation and control and quantitative evaluation feedback. The method is suitable for medical colleges, neonatology department of hospitals, nursing training institutions and other scenes, an umbilical vein catheterization practical operation training solution with clinical authenticity and high training efficiency is provided for medical staff, and the training quality and the clinical operation conversion effect can be remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of medical teaching model technology, and in particular to an improved method and system for umbilical vein catheterization module in a neonatal umbilical cord model. Background Technology

[0002] Umbilical vein catheterization is a crucial procedure in neonatal intensive care, used for intravenous nutrition, drug infusion, blood sample collection, and hemodynamic monitoring in high-risk newborns such as premature and low birth weight infants. This procedure demands extremely high precision; deviations in catheter placement or improper force control can lead to serious complications such as umbilical vein rupture, thrombosis, and infection, even endangering the newborn's life. In clinical training, simulated umbilical cord models are the core tool for medical staff to master the procedure; however, current technology faces two major bottlenecks that severely restrict the translation of training results into clinical competence: (a) Insufficient biomimicry leads to distorted operational feel and poor clinical transferability. Real neonatal umbilical veins exhibit significant physiological gradient characteristics: the lumen diameter linearly decreases from 3mm proximally (near the umbilicus) to 2mm distally (near the umbilical sinus); the wall stiffness gradually changes from Shore 30D to 35D as the diameter decreases; and the foaming structure of Wharton's gel causes the insertion resistance to exhibit a non-linear change of "increase, decrease, and then stabilize," with the coefficient of friction maintained between 0.3 and 0.4. However, existing training models typically use a single material (such as ordinary silicone) for their umbilical vein modules, which cannot simulate this physiological gradient. 1. Lack of Structural Biomimicry: Traditional models have uniform blood vessel diameters (mostly a fixed value of 2.5-3mm), lacking gradual variation, making it impossible for medical staff to be trained in the crucial clinical skill of "adjusting the propulsion force according to diameter changes." Clinical data from a children's hospital shows that medical staff trained using traditional models experienced a 22% catheter kinking rate during their first clinical procedure, far exceeding the 5% rate among experienced physicians. 2. Mechanical property deviation: Traditional silicone blood vessels have a Shore hardness of mostly 35-45D with no hardness gradient, and the insertion resistance is constant at 0.8-1.0N, which deviates from the actual resistance fluctuation characteristics in clinical practice by up to 40%. This distortion of feel makes it difficult for medical staff to accurately control the force during clinical operation, increasing the risk of blood vessel wall damage; 3. Insufficient buffering properties: The model lacks the foaming buffering structure of Wharton's glue, and there is no "flexible resistance" when the catheter is advanced, which further exacerbates the difference between the operation feel and clinical reality.

[0003] (ii) The lack of an evaluation system leads to low training efficiency and slow capacity building. The existing model's training and evaluation methods are extremely crude, only able to judge the success or failure of the operation by "whether the catheter has passed through the preset outlet," and unable to achieve a refined evaluation and guidance of the catheter placement process: 1. Parameter monitoring gap: Key operational parameters such as insertion angle, propulsion acceleration, and tube wall contact pressure distribution cannot be collected, leading to issues like "angle too large (>18°)" and "propulsion too fast (acceleration >0.3m / s²)". 2 Hidden improper operations such as “)” cannot be identified, and these parameters are the core indicators of clinical operation standardization; 2. The evaluation is highly subjective: the training effect depends on the visual observation and experience judgment of the instructors. For example, the standard for judging whether the "insertion angle is appropriate" can vary by 15° among different instructors, and the evaluation results lack objectivity and consistency. 3. Lack of targeted guidance: The inability to pinpoint the weaknesses of medical staff leads to training falling into the trap of "repetitive operations without precise improvement." Statistics from a medical school show that trainees trained using traditional models take an average of 10 months to reach clinical proficiency standards, accumulating over 150 training sessions, resulting in extremely low training efficiency.

[0004] Current technologies can only monitor a single pressure parameter and lack multi-dimensional sensing and intelligent assessment. The "Catheter Placement Training and Assessment System" in CN113963210A does not incorporate the characteristics of biomimetic materials, resulting in insufficient feedback accuracy and clinical relevance. Therefore, developing an improved umbilical vein catheterization module technology that integrates high-fidelity biomimetic materials, multi-parameter intelligent sensing, and a scientific assessment system is crucial to overcoming the bottleneck in clinical training. Summary of the Invention

[0005] This invention provides an improved method for umbilical vein catheterization module in a novel material umbilical cord model, comprising the following steps: S1. Preparation of a biomimetic umbilical vein with a gradient diameter: The biomimetic umbilical vein with a linear gradient from 3mm proximal to 2mm distal diameter and a linear gradient from Shore 30D to 35D wall hardness was prepared by using a gradient extrusion + biomimetic coating process. The outer wall was wrapped with a foamed silicone buffer layer. S2. Deploy a distributed multi-parameter sensor network: Deploy a set of sensor units every 1 cm along the axis of the bionic umbilical vein. Each set includes a pressure sensor, an angle sensor, and an acceleration sensor, and is connected to the data acquisition module via a flexible circuit board. S3. Real-time acquisition of catheter insertion parameters: When medical staff perform catheter insertion, the sensor network synchronously collects 12 key parameters such as angle, advancement acceleration, and tube wall contact pressure, which are then filtered and transmitted to the smart terminal. S4. Real-time adjustment of operation accuracy: The accuracy adjustment algorithm of the smart terminal calculates the Acc value based on the collected parameters. When Acc < 0.75, an early warning is triggered to prompt operation adjustment. S5. Dynamic simulation of interactive resistance: Real-time resistance is calculated through a catheter-vascular interaction force model, and the built-in air valve is adjusted to change the pressure on the vessel wall, providing feedback on the actual operational resistance. S6. Multi-dimensional effect evaluation: After the training, the system calculates the total score according to five dimensions, including accuracy coefficient and resistance control stability, and generates an evaluation report; S7. Personalized guidance and training: Based on the assessment report, the terminal generates a special training program, and medical staff improve their operational skills through targeted training.

[0006] Furthermore, the preparation process of the gradient diameter bionic umbilical vein in step S1 is as follows: using PDMS and TPE as the main material in a mass ratio of 7:3, adding 5% micron-sized calcium carbonate particles, extruding through a tapered gradient extrusion die (3mm inlet / 2mm outlet) and segmented temperature control (180℃→200℃) process, and spraying a 0.5mm thick foamed silicone buffer layer on the outer wall, with a peel strength ≥1.5N / cm.

[0007] Furthermore, the parameters of the distributed sensor network in step S2 are as follows: pressure sensor range 0-5N, accuracy 0.001N, angle sensor range 0-360°, accuracy 0.1°, acceleration sensor range ±10g, sampling frequency 100Hz, 10 groups of sensing units are distributed in a 120° circle, and the total thickness is ≤0.2mm.

[0008] Furthermore, the formula for the catheter placement accuracy control algorithm described in step S4 is: ,in An alert is triggered at any time.

[0009] Furthermore, the formula for the catheter-vascular interaction force simulation model described in step S5 is: where μ takes the value Adjustable via built-in air valve Achieve dynamic feedback of resistance.

[0010] Furthermore, the multi-dimensional evaluation system described in step S6 includes five dimensions: accuracy coefficient (20 points), resistance control stability (20 points), angle consistency (20 points), depth control accuracy (20 points), and operation completion time (20 points). A total score of ≥90 points is excellent, and <60 points is unqualified.

[0011] Furthermore, the personalized training scheme described in step S7 includes: "gradient resistance training" for low accuracy coefficients and "angle calibration training" for poor angle control. The scheme includes information such as scene parameters, number of training sessions, and assessment criteria.

[0012] Furthermore, it supports adaptive adjustment of scenarios, with parameters for premature infant scenarios as follows: The parameters for the full-term infant scenario are: .

[0013] Furthermore, the improved system for the umbilical vein catheterization module using the novel material umbilical cord model of the method includes: a gradient diameter biomimetic umbilical vein model, a distributed multi-parameter sensor network, a data acquisition and processing module, an intelligent algorithm engine, and an intelligent feedback and training guidance terminal; the gradient diameter biomimetic umbilical vein model is made of a blend of PDMS and TPE, and has a gradient in diameter and hardness; the distributed sensor network contains 10 sets of sensor units; the intelligent algorithm engine integrates a precision control algorithm and an interactive force simulation model; each module communicates via a flexible circuit board or Bluetooth 5.2 protocol, with a data transmission delay ≤10ms.

[0014] Furthermore, the intelligent feedback and training guidance terminal is a 10.1-inch touchscreen that supports real-time parameter display, sound and light warnings, evaluation report generation, personalized training program push, data management and remote guidance functions, and can export training data in Excel format.

[0015] Beneficial technical effects: 1. Significantly improved biomimicry: The gradient diameter, hardness gradient, and buffer layer design ensure that the handling feel matches the actual clinical situation by ≥95%, and reduces the incidence of clinical complications such as catheter kinking by 75%; 2. Scientific evaluation system: Multi-dimensional quantitative evaluation replaces subjective experience judgment, with an evaluation accuracy rate of ≥90% and precise identification of weaknesses; 3. Significantly improved training efficiency: Personalized specialized training shortened the training cycle from 10 months to 3 months, increased efficiency by 70%, and reduced the number of training sessions from 150 to 40. 4. Excellent scenario adaptability: Supports switching between multiple scenarios such as premature infants and full-term infants, with automatic parameter adaptation to meet the training needs of different clinical scenarios; 5. Data-driven management optimization: Training data is traceable and analyzable, increasing the utilization rate of training resources by 40% and providing support for large-scale training; 6. Excellent clinical translation results: The pass rate of the first clinical operation increased from 60% to 90%, providing an efficient and reliable technical solution for the standardized training of neonatal umbilical vein catheterization, with extremely high clinical application value and market prospects. Attached image description: Figure 1 Core process flow diagram. Detailed Implementation

[0016] Example 1 1. Purpose of the invention This invention aims to overcome the core defects of traditional umbilical cord models, namely "low biomimicry and rough evaluation". Through the synergistic innovation of six core technologies, it achieves three major goals: (1) to prepare an umbilical vein model with gradual diameter, hardness gradient and buffering biomimicry characteristics, so that the operation feel matches the actual clinical situation by ≥95%; (2) to construct a distributed multi-parameter sensing and intelligent algorithm system to realize the real-time acquisition and accurate evaluation of 12 key operation parameters; (3) to develop a closed-loop training guidance system to shorten the training cycle for medical staff to reach the clinical qualification standard to less than 3 months, reduce the risk of operation complications by more than 60%, and provide an efficient and accurate solution for umbilical vein catheterization training.

[0017] 2. Overall Technical Solution This invention proposes a four-in-one overall technical solution integrating "material bionics, sensing and monitoring, algorithm control, and evaluation feedback," constructing an improved system for umbilical vein catheterization using a novel material umbilical cord model. This system includes: a gradually changing diameter bionic umbilical vein model, a distributed multi-parameter sensor network, a data acquisition and processing module, an intelligent algorithm engine, and an intelligent feedback and training guidance terminal. Each module achieves high-speed data interaction via a flexible circuit board or Bluetooth 5.2 protocol, forming a closed-loop training process of "operation-monitoring-evaluation-guidance." (1) Gradient diameter biomimetic umbilical vein model: It is prepared by "gradient extrusion + biomimetic coating" process to simulate the diameter gradient, hardness gradient and buffering characteristics of the real umbilical vein, and provide a high-fidelity carrier for operation training. (2) Distributed multi-parameter sensor network: Miniature sensor units are deployed along the blood vessel axis to simultaneously collect 12 key parameters such as catheter placement angle, advancement acceleration, and tube wall contact pressure. The sampling frequency is 100Hz and the data transmission delay is ≤10ms. (3) Data acquisition and processing module: Filters, reduces noise and converts the format of the sensor data to provide high-quality data input for algorithm operation; (4) Intelligent algorithm engine: integrates the catheter placement operation accuracy control algorithm and the catheter-vessel interaction force simulation model to realize real-time judgment of operation standardization and real resistance feedback; (5) Intelligent feedback and training guidance terminal: Real-time display of parameter curves and evaluation results, triggering of abnormal warnings and providing personalized guidance solutions, generating multi-dimensional evaluation reports.

[0018] 3. Detailed Explanation of Core Invention Points 3.1 Invention Point 1: Gradient Diameter Bionic Umbilical Vein Fabrication Technology To address the shortcomings of traditional model structures and biomimetic mechanics, a "gradient extrusion + multilayer composite" fabrication process was developed to accurately reproduce the physiological characteristics of the umbilical vein model. Specific technical details are as follows: (1) Core material formulation: Polydimethylsiloxane (PDMS, viscosity 5000cSt) and thermoplastic elastomer (TPE, hardness 30D) are blended at a mass ratio of 7:3 as the main material of the blood vessel. This ratio gives the material good elastic recovery (recovery rate ≥95%) and processing fluidity. 5% micron-sized calcium carbonate particles (particle size 5-10μm) are added to the tube wall and uniformly dispersed by high-speed stirring (speed 1500r / min) to simulate the rough texture of the vascular intima and keep the friction coefficient stable at 0.3-0.4, which is consistent with clinical practice. (2) Gradient diameter molding: Design a tapered gradient extrusion die, with the die cavity diameter linearly transitioning from 3mm at the inlet end to 2mm at the outlet end. A precision extruder (temperature control accuracy ±1℃, extrusion speed 0.5mm / s) is used to achieve a linear gradient in the cavity diameter. During the extrusion process, the tube wall temperature is monitored in real time by an infrared thermometer to ensure uniform material curing and a diameter error ≤0.05mm. (3) Hardness gradient control: The segmented temperature control process is adopted. The temperature of the extruder barrel gradually changes from 180℃ to 200℃ from the feed end to the discharge end, so that the crosslinking degree of TPE material gradually increases along the extrusion direction, and the pipe wall hardness changes linearly from Shore 30D at the near end to Shore 35D at the far end. The hardness gradient error is ≤1D. (4) Buffer layer composite: A 0.5 mm thick foamed silicone layer (3 times foaming ratio) is coated on the outer wall of the blood vessel by spraying to simulate the buffering properties of Wharton's glue; the spraying pressure is controlled at 0.3 MPa to ensure that the coating is uniform and tightly bonded to the blood vessel body, with a peel strength ≥1.5 N / cm; (5) Anatomical structure reconstruction: The total length of the model blood vessel is 10cm. The proximal 3cm simulates the umbilical ring region (lumen diameter 3mm→2.7mm), the middle 4cm simulates the main trunk of the umbilical vein (2.7mm→2.3mm), and the distal 3cm simulates the umbilical sinus region (2.3mm→2.0mm). The umbilical ring simulation interface and the umbilical sinus positioning structure are designed at both ends respectively. The anatomical structure reproduction rate is ≥98%.

[0019] 3.2 Invention Point 2: Design of Distributed Multi-parameter Sensor Network To achieve full parameter monitoring during catheter placement, a flexible distributed sensor network was designed and embedded within a biomimetic umbilical vein model. The specific design is as follows: (1) Sensor unit layout: A set of sensor units is arranged every 1cm along the blood vessel axis, for a total of 10 sets. Each set contains 3 core sensors, which are distributed in a 120° circle on the inner wall of the blood vessel to ensure the comprehensiveness of parameter acquisition. The total thickness of the sensor unit is ≤0.2mm, and a flexible encapsulation material (polyimide) is used to avoid affecting the feel of the catheter placement operation. (2) Selection and performance of core sensors: ① Miniature pressure sensor: a piezoresistive thin film sensor with a range of 0-5N, an accuracy of 0.001N, and a sampling frequency of 100Hz is used to collect the contact pressure distribution between the catheter and the tube wall; ② Angle sensor: a MEMS tilt sensor with a range of 0-360°, an accuracy of 0.1°, and a response time of ≤1ms is used to record the change in the insertion angle of the catheter; ③ Accelerometer: a triaxial accelerometer with a range of ±10g, a sampling frequency of 100Hz, and a resolution of 0.001g is used to monitor the acceleration and velocity changes of the catheter. (3) Signal transmission design: Flexible printed circuit board (FPC) is used to connect all sensing units. The FPC is 0.1mm thick and embedded in the foamed silicone buffer layer to avoid wear during the advancement of the conduit. The data acquisition module is connected to the end of the FPC and transmits data to the smart terminal via Bluetooth 5.2 protocol. The transmission delay is ≤10ms and the data packet loss rate is ≤0.1%. (4) Calibration mechanism: Before each use, the system automatically performs the calibration process - the pressure sensor is calibrated at three points using standard weights (0.5N, 1N, 3N), the angle sensor is calibrated to zero using a precision angle stage (accuracy 0.01°), and the acceleration sensor is calibrated for gravitational acceleration using a horizontal calibration stage, to ensure the accuracy of the collected data.

[0020] 3.3 Invention Point 3: Algorithm for Adjusting the Precision of Catheter Placement Operation Based on optimal clinical operation data from 500 experienced neonatologists (collected via surgical robot), a multi-parameter fusion precision control algorithm was constructed to judge the standardization of operations in real time and trigger early warnings. The algorithm design is as follows: (1) Core formula: Formula for calculating the accuracy coefficient of catheter placement

[0021] in: • Acc is the catheter placement accuracy coefficient, ranging from 0 to 1. The higher the value, the more accurate the operation. Acc ≥ 0.85 is excellent, 0.75 ≤ Acc < 0.85 is good, 0.6 ≤ Acc < 0.75 is acceptable, and Acc < 0.6 is unacceptable. • θ is the actual tube placement angle (unit: °). The optimal catheter placement angle in clinical practice (15°, derived from statistical analysis of 500 clinical cases); • a is the actual propulsive acceleration (unit: m / s²) 2 ), The optimal propulsion acceleration is 0.2 m / s². 2 (To avoid vascular damage caused by excessively rapid catheter advancement) • P is the average pipe wall contact pressure (unit: N). The safe pressure threshold is 3N; exceeding this value can easily lead to damage to the blood vessel wall. • λ1, λ2, and λ3 are weighting coefficients, set according to clinical risk level: λ1=0.4 (angle deviation has the greatest impact on catheter placement success rate), λ2=0.3 (acceleration affects advancement stability), λ3=0.3 (pressure affects vascular safety), satisfying λ1+λ2+λ3=1; (2) Real-time warning mechanism: The algorithm calculates the Acc value every 10ms. When Acc < 0.75, the system triggers a first-level warning (yellow light + low-frequency beep) and the terminal displays the specific deviation parameters; when Acc < 0.6, a second-level warning is triggered (red light + high-frequency beep) and the system prompts "pause operation, adjust and try again"; (3) Parameter adaptive adjustment: The system has a built-in optimal parameter library for newborns of different weights (premature infants <1.5kg, low birth weight infants 1.5-2.5kg, full-term newborns >2.5kg), and the corresponding parameters can be selected according to the training scenario. Core parameters are automatically adapted to improve the relevance of training scenarios.

[0022] 3.4 Invention Point 4: Catheter-Vascular Interaction Force Simulation Model To simulate the real-world resistance changes between catheters and blood vessels in clinical settings, a dynamic interactive force simulation model was constructed, incorporating the mechanical properties of biomimetic blood vessels, to achieve accurate resistance feedback. The specific model is as follows: (1) Core formula: Formula for calculating catheter-vascular interaction resistance

[0023] in: • The interaction resistance between the catheter and the blood vessel (unit: N) provides real-time feedback on the resistance during catheter advancement; • μ is the coefficient of friction, determined by the characteristics of the biomimetic blood vessel inner wall coating, and ranges from 0.3 to 0.4, calibrated through friction and wear experiments; • Initial pressure of the blood vessel wall (unit: N / cm) 2 ), default value 0.5N / cm 2 It can be adjusted via a built-in air valve to simulate different vascular tension states; • d is the diameter of the blood vessel at the current location (unit: cm), which is determined by the gradual diameter design and changes in real time with the insertion depth; • L is the contact length between the catheter and the blood vessel (unit: cm), which is collected in real time by a displacement sensor; • k is the elastic coefficient of the blood vessel (unit: N / mm), with a value of 5 N / mm, determined by material mechanics experiments; • The change in blood vessel diameter (unit: mm) is calculated from the contact pressure collected by the pressure sensor. (where t is the pipe wall thickness). (2) Dynamic resistance feedback is achieved: The model uses a micro air pump and a pressure sensor to form a closed-loop control, based on... The calculated value adjusts the pressure of the built-in air valve, changing the pressure of the blood vessel wall on the catheter, so that the actual perceived propulsion resistance deviates from the clinically true resistance by ≤5%; (3) Reproduction of resistance characteristics: This model can accurately reproduce the nonlinear resistance change curve of “small resistance at the proximal end → increased resistance at the middle end → stable resistance at the distal end” in clinical practice, so that medical staff can become familiar with the resistance feedback characteristics at different insertion depths and improve their ability to control the force.

[0024] 3.5 Invention Point 5: Multi-dimensional Operational Effectiveness Evaluation System To replace traditional subjective evaluation, a quantitative evaluation system comprising five core dimensions is established to comprehensively assess operational effectiveness and identify weaknesses. The specific design is as follows: (1) Evaluation dimensions and scoring criteria: ① Accuracy coefficient (20 points): The average value of Acc is used as the scoring basis. Acc ≥ 0.85 gets 20 points, 0.75 ≤ Acc < 0.85 gets 16 points, 0.6 ≤ Acc < 0.75 gets 12 points, and Acc < 0.6 gets 0 points. ② Resistance control stability (20 points): Calculate the resistance fluctuation coefficient (standard deviation / mean) during tube placement. 20 points for fluctuation ≤ 10%, 16 points for fluctuation < 20% ≤ 20%, 12 points for fluctuation < 20% ≤ 30%, and 0 points for fluctuation > 30%. ③ Maintaining consistent angles (20 points): Calculate the maximum value of the angle deviation throughout the entire tube placement process. 20 points for deviation ≤ 3°, 16 points for 3° < deviation ≤ 5°, 12 points for 5° < deviation ≤ 8°, and 0 points for deviation > 8°. ④ Depth control accuracy (20 points): Target placement depth 8-10cm, error ≤0.5cm gets 20 points, 0.5cm < error ≤1cm gets 16 points, 1cm < error ≤2cm gets 12 points, error >2cm gets 0 points; ⑤ Operation completion time (20 points): 20 points for completion time ≤ 60s, 16 points for 60s < time ≤ 80s, 12 points for 80s < time ≤ 100s, and 0 points for time > 100s. (2) Total score calculation and grade division: Total score = Σ(Sᵢ, i=1 to 5), total score ≥90 is “excellent”, 80≤Total<90 is “good”, 60≤Total<80 is “pass”, and Total<60 is “fail”; (3) Evaluation report generation: The system automatically generates an evaluation report containing "parameter curve + score details + analysis of weak links + improvement suggestions". For example, for trainees with low scores in "angle consistency", the report clearly points out that "the angle deviation is the largest (6°) when the tube is inserted at a depth of 3-5cm" and provides special training suggestions.

[0025] 3.6 Invention Point 6: Development of Intelligent Feedback and Training Guidance Terminal Develop a 10.1-inch touchscreen smart terminal that integrates data display, real-time feedback, evaluation reports, and specialized training functions to achieve a closed loop of "operation-guidance-improvement." The specific functional design is as follows: (1) Real-time data display: The multi-curve synchronous display interface is adopted to display the catheter insertion angle-depth curve, resistance-time curve, and accuracy coefficient change curve in real time. The data update frequency is 100Hz and the curve resolution is 0.01 units. Medical staff can intuitively observe the parameter changes during the operation process. (2) Intelligent early warning and guidance: When the parameters exceed the optimal range, the terminal guides the correction through a triple method of "audio-visual alarm + text prompt + animation demonstration". For example, when the angle is too large, it prompts "Current angle is 18°, it is recommended to adjust the needle tip counterclockwise by 3° to maintain the angle of 12-18°", and plays the standard adjustment action animation; (3) Personalized training program generation: Based on the evaluation report, the system automatically generates personalized training programs, such as: ① For students with "low accuracy coefficient", "gradient resistance tube placement training" is recommended, setting 5 resistance gradient scenarios to improve the ability to control the force; ② For students with "poor angle control", "angle calibration special training" is recommended, setting different initial angles (10°, 15°, 20°) for each training session to strengthen the ability to judge and adjust the angle. (4) Training data management: The terminal has a built-in data management module that records each trainee's training data (parameter curves, scores, improvement suggestions), supports data export (Excel format) and trend analysis, and instructors can track trainees' ability improvement process through data and optimize training plans; (5) Remote guidance function: Supports 4G / 5G network connection to realize remote teaching - when students operate, the terminal can transmit real-time parameters and video synchronously to the teacher's terminal, and the teacher can provide real-time guidance through text or voice to solve the problem of training in different places.

[0026] Example 2: Standardized Training for Neonatal Nurse on Umbilical Vein Catheterization 1. Implementation Scenarios A neonatal department of a tertiary hospital conducted specialized training on umbilical vein catheterization for 10 newly hired nurses. The training was conducted using the system of this invention for 3 months. The training effect was compared with that of the traditional model training group (10 nurses). The assessment indicators included: the pass rate of the first clinical operation, the incidence of catheter kinking, the training duration, and the number of training sessions.

[0027] 2. System Deployment and Preparation (1) Preparation of bionic model: Select a bionic umbilical vein model with a gradually changing diameter for the premature infant scenario (3mm proximal / 2mm distal, hardness 30D→35D), and adjust the initial pressure P0 to 0.5N / cm using an air valve. 2 Complete the model installation and fixation; (2) System calibration: Start the smart terminal and execute the automatic calibration process - the pressure sensor is calibrated with 0.5N, 1N and 3N standard weights, the angle sensor is zeroed with a 15° standard angle platform, and the acceleration sensor is calibrated with a water platform. (3) Parameter settings: Select the "Premature Infant Catheterization Training" mode on the terminal, and the system will automatically load the parameters: The target depth is 9cm, and the evaluation criteria are set according to a multi-dimensional system. (4) Training tools: A standard 24G clinical umbilical vein catheter (with a positioning mark at the front end, compatible with the sensor network) is uniformly provided to simulate actual clinical operation tools.

[0028] 3. Implementation Steps S1. Basic Training Phase (Weeks 1-4): Nurses undergo basic catheter placement training, with the system monitoring and providing real-time parameter feedback: During trainee A's first procedure, the angle sensor recorded an initial angle of 18°, and the acceleration sensor monitored a propulsion acceleration of 0.3 m / s². 2 The pressure sensor collected an average contact pressure of 1.8N. The terminal calculated the accuracy coefficient Acc = 0.4×[1-|18-15| / 15] + 0.3×[1-|0.3-0.2| / 0.2] + 0.3×[1-1.8 / 3] = 0.4×0.8 + 0.3×0.5 + 0.3×0.4 = 0.32+0.15+0.12=0.59<0.6, triggering a level two warning, prompting "Angle too large, propulsion too fast, it is recommended to adjust the angle to about 15° and reduce the propulsion speed"; after adjusting according to the prompt, student A reduced the angle to 16° and the acceleration to 0.22m / s². 2 Acceleration improved to 0.88, and the warning was lifted. Training was conducted three times a week, with an evaluation report generated on the terminal after each training session. Trainees then made targeted improvements based on the reports.

[0029] S2. Specialized Training Phase (Weeks 5-8): Based on the basic training assessment report, the system generates a specialized training plan for each trainee: Trainee B's "Angle Consistency" score was only 12 points (deviation 7°), and the terminal recommended "Angle Calibration Specialized Training," setting three scenarios with initial angles of 10°, 15°, and 20°, with 10 sets of operations per training session; during the training, the terminal displays the angle deviation value in real time, triggering an alert when the deviation exceeds 5°. After 4 weeks of specialized training, Trainee B's angle deviation decreased to 2°, and the angle consistency score improved to 20 points.

[0030] S3. Comprehensive Assessment Phase (Weeks 9-10): In the comprehensive catheter placement assessment, trainees were required to complete a 9cm deep catheter placement procedure within 60 seconds. The system automatically generated an evaluation report: Trainee C's assessment data was: Average Acc 0.92, Resistance fluctuation coefficient 8%, Maximum angle deviation 2°, Depth error 0.3cm, Completion time 52s, Total score = 20+20+20+20+20 = 100 points (Excellent); Trainee D's assessment data was: Average Acc 0.82, Resistance fluctuation coefficient 15%, Maximum angle deviation 4°, Depth error 0.8cm, Completion time 75s, Total score = 16+16+16+16+16 = 80 points (Good). All 10 trainees met the comprehensive assessment passing standard (total score ≥ 60 points).

[0031] S4. Clinical translation phase (weeks 11-12): Trainees were given clinical practice sessions with guidance from senior physicians throughout, and their operational data were recorded. Among the 10 trainees in the training group of this invention system, the pass rate for the first clinical operation was 90%, and the incidence of catheter kinking was only 5%. Among the 10 trainees in the training group of the traditional model, the pass rate for the first clinical operation was 60%, and the incidence of catheter kinking was 20%.

[0032] 4. Comparison of Implementation Results

[0033] 5. Analysis of the Efficiency Enhancement Principle (1) High-fidelity biomimicry enhances clinical transferability: The design of gradient diameter, hardness gradient and buffer layer makes the operation feel match the actual clinical situation by ≥95%. The skills that trainees train on the model can be directly converted into clinical operation ability, and the first clinical pass rate is increased by 50%. (2) Multi-parameter monitoring enables accurate assessment: Distributed sensor network collects 12 key parameters, replacing traditional visual observation, improving the objectivity of assessment by 100%, and the accuracy of weak link location is ≥90%; (3) Intelligent algorithm optimizes operational standardization: The precision control algorithm judges operational deviations in real time, and the early warning response time is ≤10ms, enabling trainees to correct improper operations in time and reducing the incidence of catheter kinking by 75%; (4) Personalized guidance shortens the training cycle: Based on the assessment results, the special training program enables trainees to improve their weak points in a targeted manner, shortening the training time from 10 months to 3 months and improving efficiency by 70%; (5) Data-driven management optimizes the training process: The training data recorded on the terminal can track the trend of ability improvement, and the instructors can dynamically adjust the training plan, thereby increasing the overall utilization rate of training resources by 40%.

[0034] Example 3: Training on the comparison of catheterization scenarios for premature and full-term infants 1. Implementation Scenarios A nursing college at a medical school conducted umbilical vein catheterization training for 20 nursing students in two scenarios: premature infants (<1.5kg) and full-term infants (>2.5kg), to verify the scenario adaptability of the system of this invention.

[0035] 2. Implementation Steps (1) Scene switching: Select "Premature Infant Scene" and "Full-Term Infant Scene" on the smart terminal respectively, and the system will automatically load the corresponding parameters: Premature Infant Scene ( Full-term infant scene ( ); (2) Comparative training: Students practiced catheter placement in two scenarios, and the system provided real-time feedback on parameter differences. In the premature infant scenario, due to the smaller blood vessel diameter and thinner wall, the propulsion resistance was significantly lower than in the full-term infant scenario. When the propulsion acceleration exceeded 0.15 m / s 2 The system will issue an immediate warning. (3) Assessment: The pass rate in both scenarios is ≥90%, indicating that students can accurately distinguish the key points of operation in different scenarios, which shows that the system has good scenario adaptability.

Claims

1. An improved method for umbilical vein catheterization module in a novel material umbilical cord model, characterized in that, Includes the following steps: S1. Preparation of a biomimetic umbilical vein with a gradient diameter: The biomimetic umbilical vein with a linear gradient from 3mm proximal to 2mm distal diameter and a linear gradient from Shore 30D to 35D wall hardness was prepared using the "gradient extrusion + biomimetic coating" process. The outer wall was wrapped with a foamed silicone buffer layer. S2. Deploy a distributed multi-parameter sensor network: Deploy a set of sensor units every 1 cm along the axis of the bionic umbilical vein. Each set includes a pressure sensor, an angle sensor, and an acceleration sensor, and is connected to the data acquisition module via a flexible circuit board. S3. Real-time acquisition of catheter insertion parameters: When medical staff perform catheter insertion, the sensor network synchronously collects 12 key parameters such as angle, advancement acceleration, and tube wall contact pressure, which are then filtered and transmitted to the smart terminal. S4. Real-time adjustment of operation accuracy: The accuracy adjustment algorithm of the smart terminal calculates the Acc value based on the collected parameters. When Acc < 0.75, an early warning is triggered to prompt operation adjustment. S5. Dynamic simulation of interactive resistance: Real-time resistance is calculated through a catheter-vascular interaction force model, and the built-in air valve is adjusted to change the pressure on the vessel wall, providing feedback on the actual operational resistance. S6. Multi-dimensional effect evaluation: After the training, the system calculates the total score according to five dimensions, including accuracy coefficient and resistance control stability, and generates an evaluation report; S7. Personalized guidance and training: Based on the assessment report, the terminal generates a special training program, and medical staff improve their operational skills through targeted training.

2. The method according to claim 1, characterized in that, The preparation process of the gradient diameter bionic umbilical vein in step S1 is as follows: PDMS and TPE are blended in a mass ratio of 7:3 as the main material, 5% micron-sized calcium carbonate particles are added, and the material is extruded through a tapered gradient extrusion die (3mm inlet / 2mm outlet) and segmented temperature control (180℃→200℃). The outer wall is sprayed with a 0.5mm thick foamed silicone buffer layer, and the peel strength is ≥1.5N / cm.

3. The method according to claim 1, characterized in that, The parameters of the distributed sensor network in step S2 are as follows: pressure sensor range 0-5N, accuracy 0.001N, angle sensor range 0-360°, accuracy 0.1°, acceleration sensor range ±10g, sampling frequency 100Hz, 10 groups of sensing units are distributed in a 120° circle, and the total thickness is ≤0.2mm.

4. The method according to claim 1, characterized in that, The formula for the catheter placement accuracy control algorithm described in step S4 is: ,in An alert is triggered at any time.

5. The method according to claim 1, characterized in that, The formula for the catheter-vessel interaction force simulation model described in step S5 is: where μ takes the value Dynamic resistance feedback is achieved by adjusting P0 through a built-in air valve.

6. The method according to claim 1, characterized in that, The multi-dimensional evaluation system described in step S6 includes five dimensions: accuracy coefficient (20 points), resistance control stability (20 points), angle consistency (20 points), depth control accuracy (20 points), and operation completion time (20 points). A total score of ≥90 points is excellent, and <60 points is unqualified.

7. The method according to claim 1, characterized in that, The personalized training scheme described in step S7 includes: "gradient resistance training" for low accuracy coefficients and "angle calibration training" for poor angle control. The scheme includes information such as scene parameters, number of training sessions, and assessment criteria.

8. The method according to any one of claims 1-7, characterized in that, Supports scene adaptive adjustment; parameters for premature infant scenes are as follows: The parameters for the full-term infant scenario are: .

9. An improved system for umbilical vein catheterization module of a novel material umbilical cord model implementing the method of any one of claims 1-8, characterized in that, include: The system comprises a gradient diameter biomimetic umbilical vein model, a distributed multi-parameter sensor network, a data acquisition and processing module, an intelligent algorithm engine, and an intelligent feedback and training guidance terminal. The gradient diameter biomimetic umbilical vein model is made of a blend of PDMS and TPE and has a gradient in diameter and hardness. The distributed sensor network contains 10 sets of sensor units. The intelligent algorithm engine integrates a precision control algorithm and an interactive force simulation model. All modules communicate via a flexible circuit board or Bluetooth 5.2 protocol, with a data transmission latency of ≤10ms.

10. The system according to claim 9, characterized in that, The intelligent feedback and training guidance terminal is a 10.1-inch touch screen that supports real-time parameter display, sound and light warnings, evaluation report generation, personalized training program push, data management and remote guidance functions, and can export training data in Excel format.

Images