Venous catheter simulation training and training feedback device

Abstract

The invention relates to the technical field of venous catheter simulation training, in particular to a venous catheter simulation training and training feedback device. The simulation arm comprises a simulation arm body, a simulation blood vessel is arranged in the simulation arm body, a remaining needle is arranged on one side of the simulation arm body, one end of the remaining needle penetrates into the inner wall of the simulation arm body and is inserted into the simulation blood vessel, and one end of the remaining needle is connected with an injector access tube through a rubber tube. According to the invention, the pressure sensor is used for detecting the pressure of liquid in the rubber pipe, and the main control unit is used for setting a sending task monitoring buffer area to judge a numerical value, so that a trainee and a trainee can judge the liquid conveying speed during pipe washing and pipe sealing by observing the data condition displayed on the display; a trainee and a trainee can clearly observe the conditions of pipe washing and pipe sealing, so that the trainee can timely adjust the propelling speed of liquid during pipe washing and pipe sealing, and the liquid conveying speed can be accurately controlled during pipe washing and pipe sealing.

Description

Technical Field

[0001] The invention relates to the technical field of venous catheter simulation training, in particular to a venous catheter simulation training and training feedback device. Background Art

[0002] Intravenous catheters are catheters that can be used for intravenous treatment, including peripheral intravenous catheters and central venous catheters. Various types of catheters are key links in achieving intravenous treatment for patients. For patients receiving intravenous treatment, the full function of the catheter and high-quality management throughout the treatment process means fewer punctures, less pain, less treatment interference, and better care and clinical outcomes. During the use of various types of intravascular catheters, in order to maintain the function of the catheter, that is, the patency of the catheter, the catheter needs to be flushed and sealed, including flushing between infusions of incompatible drugs to avoid drug incompatibilities, and flushing after the infusion of drugs that are easy to adhere to the catheter cavity, such as fat emulsions; it also includes flushing and sealing after the end of infusion treatment.

[0003] After the intravenous catheter is injected once, it is necessary to flush and seal the intravenous catheter. However, flushing and sealing operations require certain techniques, including the speed and frequency of flushing and sealing injections. In the existing flushing and sealing training, trainees need to learn flushing and sealing. The current guidelines, industry standards and expert consensus have clear requirements for catheter maintenance, but there is a lack of technical details for flushing and sealing operations. Most trainers and trainees need to observe with the naked eye and cannot make quantitative judgments on operations such as flushing and sealing, resulting in large errors in operation judgments and inaccurate teaching effects. Teaching in the presence of errors will not only prolong the learning time of trainees, but also affect the overall teaching quality, making it impossible for trainees to accurately grasp the control of liquid delivery speed during flushing and sealing, and thus unable to achieve the expected goal of catheter retention, which greatly reduces the training effect and increases the training time accordingly. Therefore, a device is needed to help trainees simulate the training of flushing and sealing intravenous catheters to improve the efficiency and effect of training. Summary of the invention

[0004] The purpose of the present invention is to provide a venous catheter simulation training and training feedback device to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, a venous catheter simulation training and training feedback device is provided, including a simulation arm, a simulated blood vessel is arranged inside the simulation arm, a retention needle is arranged on one side of the simulation arm, one end of the retention needle is inserted into the inner wall of the simulation arm and into the simulated blood vessel, one end of the retention needle is connected to a syringe access tube through a rubber tube, a connected silicone tube access tube is fixed to one side of the syringe access tube, a syringe is plugged and connected to the syringe access tube, the syringe transports liquid into the syringe access tube, one end of the silicone tube access tube is connected to a hose, the other end of the hose is connected to a transmission device, a display is arranged on one side of the transmission device, signals are transmitted between the display and the transmission device, and in the process of the syringe transporting liquid into the syringe access tube, the transmission device collects the liquid pressure in the hose, and after collecting the pressure data, the transmission device transmits the data to the display for display.

[0006] As a further improvement of the present technical solution, after one end of the indwelling needle is inserted into the interior of the simulated blood vessel, the interiors of the simulated blood vessel, the syringe access tube, the silicone tube access tube and the rubber tube are connected to each other, and when the syringe injects liquid into the syringe access tube, the liquid pressures inside the simulated blood vessel, the syringe access tube, the silicone tube access tube and the rubber tube are the same.

[0007] As a further improvement of the technical solution, a power module, a pressure sensor, a WIFI communication module and a main control unit are installed inside the transmission device;

[0008] The power supply module supplies electric energy to the pressure sensor, the WIFI communication module and the main control unit;

[0009] The pressure sensor is used to collect data on changes in the pressure of the liquid in the hose;

[0010] The main control unit is used to collect data from the pressure sensor, so that the pressure sensor transmits the collected liquid pressure value to the main control unit;

[0011] The WIFI communication module sends instructions to the main control unit, so that the main control unit transmits the data collected from the pressure sensor to the WIFI communication module.

[0012] As a further improvement of the technical solution, the WIFI communication module uses a fixed time period to send instructions when sending instructions to the main control unit.

[0013] As a further improvement of the present technical solution, a flushing pipe vortex simulation and analysis module is provided in the display, and the WIFI communication module transmits the pressure data received by the main control unit to the flushing pipe vortex simulation and analysis module, and the flushing pipe vortex simulation and analysis module performs flushing pipe vortex simulation and analysis after receiving the pressure data transmitted by the WIFI communication module.

[0014] As a further improvement of the technical solution, the steps of the wash pipe vortex simulation analysis are:

[0015] ①. By using fluid mechanics to simulate the impact force of the fluid in the flushing pipe, the geometric models of the central vein and the accessory vein and the hose are established, and the boundary layer grid is divided for the established geometric models. The pressure value range at the inlet position of the hose is set, and the abnormal pressure at the inlet position of the hose is analyzed;

[0016] ②, using the optimal Latin hypercube algorithm, sampling the catheter pressure values ​​calculated in step ①, and obtaining multiple samples, and then solving and calculating the obtained samples to obtain eddy current result data of all samples;

[0017] ③. Based on step ②, extract the inlet pressure data and eddy flow result data of all samples, and save the obtained inlet pressure data and eddy flow result data into 1 column matrix data respectively. All samples are saved into matrices by column. The matrix space size is the number of data points × the number of samples, and the principal component analysis algorithm is used to reduce the dimension of the data.

[0018] ④. Use the convolutional neural network deep learning algorithm to process the pressure and eddy current data after dimensionality reduction to generate a deep learning model, save the trained deep learning model as an ONNX file, and use the ONNX Runtime inference engine to continuously input and monitor the pressure values ​​to obtain the pressure and eddy current data at each position in the catheter corresponding to each pressure value.

[0019] As a further improvement of the technical solution, the formula of the optimal Latin hypercube algorithm is:

[0020]

[0021] in, represents the evaluation function, s represents the number of samples, di represents the distance between the i-th sample points, j i It represents the number of distances between the i-th sample points, and p represents the probability of the i-th sample points.

[0022] As a further improvement of the technical solution, the specific steps of the principal component analysis algorithm are as follows:

[0023] Ⅰ. Input: Sample set D = {x1, x2, ..., xm};

[0024] II. Centralize all samples:

[0025] III. Calculate the sample covariance matrix XX T ;

[0026] IV. Covariance matrix XX T Perform eigenvalue decomposition;

[0027] Ⅴ. Take the eigenvectors w1, w2, …, w corresponding to the largest d eigenvalues d ;

[0028] VI. Output: Projection matrix W* = (w1, w2, ..., w d ).

[0029] As a further improvement of the present technical solution, a sending task monitoring buffer is set in the main control unit, and the liquid pressure data transmitted from the pressure sensor is temporarily stored in the sending task monitoring buffer. After the liquid pressure data in the sending task monitoring buffer reaches a specified value, the WIFI communication module transmits all the liquid pressure data in the sending task monitoring buffer to the flushing tube vortex simulation analysis module.

[0030] As a further improvement of the present technical solution, the maximum and minimum pressure response thresholds are set in the main control unit. When the liquid pressure data transmitted by the pressure sensor received by the main control unit is higher than the maximum response threshold or lower than the minimum response threshold, the main control unit feeds back to the WIFI communication module, so that the main control unit transmits the information data to the display for display.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. In the intravenous catheter simulation training and training feedback device, the pressure of the liquid in the hose is detected by a pressure sensor, and the main control unit is set to send a task monitoring buffer to judge the value, so that the trainer and the trainee can judge the speed of liquid delivery during flushing and sealing by observing the data displayed on the display, so that the trainer and the trainee can clearly observe the flushing and sealing conditions, so that the trainer and the trainee can adjust the liquid propulsion speed during flushing and sealing in time, so that the trainee can accurately control the liquid delivery speed during flushing and sealing, thereby improving the overall teaching quality.

[0033] 2. In the intravenous catheter simulation training and training feedback device, the flushing vortex simulation analysis module collects data transmitted by the WIFI communication module to perform flushing vortex simulation analysis, so as to obtain the pressure change of the liquid pressure in the hose, and achieve real-time update of the liquid pressure in the hose, so that the trainers and trainees can obtain the pressure change of the liquid in the hose in real time during the operation of simulated flushing and sealing, so that the trainers and trainees can more accurately control the speed of liquid delivery during flushing and sealing, improve the training effect of the trainees, and accelerate the learning speed of the trainees. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a schematic diagram of the overall structure of Embodiment 1 of the present invention;

[0035] Figure 2 This is a schematic diagram of the structure of Example 1 of the present invention.

[0036] The meaning of each number in the figure is:

[0037] 1. Simulated arm; 2. Simulated blood vessel; 3. Syringe access tube; 4. Silicone tube access tube; 5. Indwelling needle; 6. Hose; 7. Transmission device; 71. Power module; 72. Pressure sensor; 73. WIFI communication module; 74. Main control unit; 8. Display; 81. Punch pipe vortex simulation analysis module. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Example 1

[0040] See also Figure 1-Figure 2As shown, a venous catheter simulation training and training feedback device is provided, including a simulation arm 1, the simulation arm 1 includes simulated skin and bones, the simulation arm 1 provides a realistic teaching simulation environment to improve the simulation effect of the trainer and the trainee during real simulation, and at the same time, a simulated blood vessel 2 is arranged inside the simulation arm 1, and a retention needle 5 is arranged on one side of the simulation arm 1, one end of the retention needle 5 is inserted into the inner wall of the simulation arm 1 and inserted into the simulated blood vessel 2, one end of the retention needle 5 is connected to a syringe access tube 3 through a rubber tube, and a connected silicone tube access tube 4 is fixed to one side of the syringe access tube 3, and the syringe A syringe is plugged and connected to the access tube 3, and the syringe delivers liquid to the syringe access tube 3. One end of the silicone tube access tube 4 is connected to a rubber hose 6, and the other end of the rubber hose 6 is connected to a transmission device 7. After one end of the indwelling needle 5 is inserted into the interior of the simulated blood vessel 2, the interiors of the simulated blood vessel 2, the syringe access tube 3, the silicone tube access tube 4 and the rubber hose 6 are connected to each other. When the syringe injects liquid into the syringe access tube 3, because the simulated blood vessel 2, the syringe access tube 3, the silicone tube access tube 4 and the rubber hose 6 are connected together, the liquid pressures inside the simulated blood vessel 2, the syringe access tube 3, the silicone tube access tube 4 and the rubber hose 6 are the same.

[0041] The transmission device 7 is internally installed with a power module 71, a pressure sensor 72, a WIFI communication module 73 and a main control unit 74;

[0042] The power module 71 supplies power to the pressure sensor 72, the WIFI communication module 73 and the main control unit 74;

[0043] The pressure sensor 72 is connected to one end of the hose 6, and the pressure sensor 72 is used to collect data on changes in the pressure of the liquid in the hose 6;

[0044] The main control unit 74 is used to collect data from the pressure sensor 72, so that the pressure sensor 72 transmits the collected liquid pressure value to the main control unit 74;

[0045] The WIFI communication module 73 sends an instruction to the main control unit 74 , so that the main control unit 74 transmits the data collected in the pressure sensor 72 to the WIFI communication module 73 .

[0046] When the WIFI communication module 73 sends instructions to the main control unit 74, a fixed time period is used to send the instructions. The length of the time period is adjustable and can be adjusted according to the needs of use. The time of each simulation collection is 5 minutes, which can also be adjusted according to the actual use needs.

[0047] A display 8 is provided on one side of the transmission device 7, and signal transmission is performed between the display 8 and the transmission device 7, that is, the WIFI communication module 73 transmits the data transmitted from the main control unit 74 to the display 8 via wireless signals, and the maximum and minimum pressure response thresholds are set in the main control unit 74. When the liquid pressure data transmitted by the pressure sensor 72 received by the main control unit 74 is higher than the maximum response threshold or lower than the minimum response threshold, the main control unit 74 feeds back to the WIFI communication module 73, so that the main control unit 74 transmits the information data to the display 8 for display.

[0048] During the process of the syringe delivering liquid into the syringe access tube 3, the transmission device 7 collects the liquid pressure in the hose 6. After collecting the pressure data, the transmission device 7 transmits the data to the display 8 for display. The trainer and the trainee adjust the speed of the syringe delivering liquid into the syringe access tube 3 by observing the data displayed on the display 8, so that the trainees can learn to control the feel of the syringe delivering liquid into the syringe access tube 3, so that the trainees can flush and seal the catheter needle more accurately.

[0049] At the same time, a flushing pipe vortex simulation analysis module 81 is provided in the display 8, and the WIFI communication module 73 transmits the pressure data received by the main control unit 74 to the flushing pipe vortex simulation analysis module 81. A sending task monitoring buffer is provided in the main control unit 74, and the liquid pressure data transmitted from the pressure sensor 72 is temporarily stored in the sending task monitoring buffer. After the liquid pressure data in the sending task monitoring buffer reaches a specified value, the WIFI communication module 73 transmits all the liquid pressure data in the sending task monitoring buffer to the flushing pipe vortex simulation analysis module 81. Here, the specified value of the liquid pressure data in the sending task monitoring buffer is a fixed time. In order to ensure the continuity of the pressure value, the pressure sensor 72 collects data every 10ms. The collected data is stored in the sending task monitoring buffer. During the collection process, if the pressure data is higher than the highest pressure response threshold or lower than the lowest pressure response threshold, the main control unit 74 will immediately transmit the collected pressure data to the WIFI communication module 73. The WIFI communication module 73 will transmit the pressure data to the display 8. The display 8 will display the data so that the trainers and trainees can quickly see the liquid delivery speed when flushing and sealing the tubes, so that the trainers and trainees can make quick adjustments to improve the training effect. When this set of data reaches the specified time, such as 200ms, because the human body's reaction time is between 100-300ms, choosing 200ms can enable the trainers and trainees to make timely adjustments.

[0050] The flushing pipe vortex simulation analysis module 81 performs flushing pipe vortex simulation analysis after receiving the pressure data transmitted by the WIFI communication module 73. The data analyzed by the flushing pipe vortex simulation analysis module 81 is the specified value of the liquid pressure data in the sending task monitoring buffer, and then all the data stored in the sending task monitoring buffer are sent.

[0051] The steps of the pipe vortex simulation analysis are as follows:

[0052] ①, by using fluid mechanics to simulate and calculate the impact force of the fluid in the hose 6, establish the geometric model of the central vein and the accessory vein and the geometric model of the hose 6, and divide the established geometric model into boundary layer grids, set the pressure value range at the inlet position of the hose 6, and perform abnormal analysis on the pressure at the inlet position of the hose 6, that is, analyze the abnormal pressure situation: distinguish the data of the pressure data at the hose 6 that is higher than the highest pressure response threshold or lower than the lowest pressure response threshold;

[0053] ②, using the optimal Latin hypercube algorithm, sampling the catheter pressure values ​​calculated in step ①, and obtaining multiple samples, and then solving and calculating the obtained samples to obtain eddy current result data of all samples;

[0054] The formula for the optimal Latin hypercube algorithm is:

[0055]

[0056] in, represents the evaluation function, s represents the number of samples, di represents the distance between the i-th sample points, j i It represents the number of distances between the i-th sample points, and p represents the probability of the i-th sample points.

[0057] The optimal Latin hypercube algorithm significantly improves sampling efficiency and model accuracy by improving sample uniformity and reducing sample size, thus avoiding uneven sampling that affects the accuracy of the vortex simulation analysis.

[0058] ③. Based on step ②, extract the inlet pressure data and eddy flow result data of all samples, and save the obtained inlet pressure data and eddy flow result data into 1 column matrix data respectively. All samples are saved into matrices by column. The matrix space size is the number of data points × the number of samples, and the principal component analysis algorithm is used to reduce the dimension of the data.

[0059] The specific steps of the principal component analysis algorithm are as follows:

[0060] Ⅰ. Input: Sample set D = {x1, x2, ..., x m}, input a data set containing m samples, each sample x iIt is an n-dimensional vector, which is used to clarify the input data and provide a basis for subsequent processing;

[0061] II. Centralize all samples: By centering the samples, the bias can be eliminated, making it easier to calculate the subsequent covariance matrix;

[0062] III. Calculate the sample covariance matrix XX T , the linear relationship between the liquid pressure data in the hose 6 and time can be reflected by calculating the covariance matrix;

[0063] IV. Covariance matrix XX T Perform eigenvalue decomposition to obtain eigenvalues ​​and eigenvectors;

[0064] Ⅴ. Take the eigenvectors w1, w2, ..., w corresponding to the largest d eigenvalues ​​obtained in Ⅳ d ;

[0065] VI. Output: Projection matrix W* = (w1, w2, ..., w d );

[0066] Through the above steps, the data dimension can be effectively reduced, noise can be removed, key features can be extracted, and subsequent data processing can be facilitated;

[0067] ④. Use the convolutional neural network deep learning algorithm to process the pressure and eddy current data after dimensionality reduction to generate a deep learning model, and save the trained deep learning model as an ONNX file;

[0068] The specific steps to generate a deep learning model are as follows:

[0069] a. Input: training set and learning rate η; the training set contains the pressure and eddy current data after dimensionality reduction, usually a two-dimensional matrix; the learning rate is the step size that controls the update of model parameters. Too large a learning rate will lead to unstable training, and too small a learning rate will lead to slow convergence;

[0070] b. Randomly initialize all connection weights and thresholds in the network in the range of (0,1), where the connection weight refers to the strength or weight value of the connection between neurons in the neural network, and the threshold is a parameter of each neuron in the neural network, which is used to adjust the activation threshold of the neuron;

[0071] c. Calculate the output value of the current sample based on the current parameters;

[0072] d. Calculate the gradient term g of the output layer neurons j ;

[0073] Loss function: calculates the error between the network output and the true label;

[0074] Gradient calculation: Calculate the gradient of the loss function with respect to the output layer parameters using the chain rule;

[0075] e. Calculate the gradient term e of the hidden layer neurons h , where the gradient value gradient term is an important parameter used in the back propagation algorithm to update the connection weights and thresholds of hidden layer neurons, and is used to specify parameter updates and propagate error information;

[0076] f. Update connection rights w hj ,v ih With threshold θ j ,γ h ; The back propagation algorithm calculates the gradient layer by layer from the output layer to the input layer;

[0077] g. Repeat steps b to f until the accuracy rate is met, then stop and complete; wherein the accuracy rate is preset;

[0078] h. Output: A multi-layer feedforward neural network with connection weights and thresholds determined and deployed on a computer.

[0079] By adopting the convolutional neural network deep learning algorithm, useful features can be automatically extracted from the input data without manually designing feature engineering, which speeds up the processing of pressure data, allowing trainers and trainees to quickly obtain the pressure changes in the hose 6 and achieve real-time display effects;

[0080] Saving the trained deep learning model as an ONNX file can provide a unified representation format for the deep learning model, allowing the model to be seamlessly converted and used between different frameworks, improving the versatility and flexibility of the model;

[0081] At the same time, the ONNX Runtime inference engine is used to continuously input and monitor the pressure values, obtain the pressure and eddy current data at each position in the catheter corresponding to each pressure value, and realize the real-time simulation of the flushing vortex, so that the display 8 can display the changes in the liquid pressure in the hose 6 in real time, so that the trainers and trainees can observe the pressure changes in the hose 6 in time, and adjust the liquid delivery speed during flushing and sealing in time.

[0082] The pressure of the liquid in the hose 6 is detected by the pressure sensor 72, and the main control unit 74 is used to set a sending task monitoring buffer to judge the value, so that the trainers and trainees can judge the speed of liquid delivery during flushing and sealing by observing the data displayed on the display 8, so that the trainers and trainees can clearly observe the flushing and sealing conditions, so that the trainers and trainees can adjust the propulsion speed of the liquid during flushing and sealing in time. At the same time, the flushing vortex simulation analysis module 81 collects the data transmitted by the WIFI communication module 73 to perform flushing vortex simulation analysis, so as to obtain the pressure change of the liquid pressure in the hose 6, and update the liquid pressure in the hose 6 in real time, so that the trainees can obtain the pressure change of the liquid in the hose 6 in real time during the operation of the simulated flushing and sealing, so that the trainees can accurately control the speed of liquid delivery during flushing and sealing, thereby improving the overall teaching quality.

[0083] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A venous catheter simulation training and training feedback device, comprising a simulation arm (1), characterized in that: The simulated arm (1) is provided with a simulated blood vessel (2) inside, and a retention needle (5) is provided on one side of the simulated arm (1). One end of the retention needle (5) pierces the inner wall of the simulated arm (1) and is inserted into the simulated blood vessel (2). One end of the retention needle (5) is connected to a syringe access tube (3) via a rubber tube. A connected silicone tube access tube (4) is fixed to one side of the syringe access tube (3). A syringe is plugged and connected to the syringe access tube (3). The syringe injects fluid into the syringe access tube (3). The silicone tube access tube (4) is connected to a rubber hose (6) at one end, and the other end of the rubber hose (6) is connected to a transmission device (7). A display (8) is provided on one side of the transmission device (7). Signals are transmitted between the display (8) and the transmission device (7). When the syringe delivers liquid to the syringe access tube (3), the transmission device (7) collects the liquid pressure in the rubber hose (6). After collecting the pressure data, the transmission device (7) transmits the data to the display (8) for display.

2. The venous catheter simulation training and training feedback device according to claim 1, characterized in that: After one end of the indwelling needle (5) is inserted into the interior of the simulated blood vessel (2), the interiors of the simulated blood vessel (2), the syringe access tube (3), the silicone tube access tube (4) and the rubber tube (6) are connected to each other. When the syringe injects liquid into the syringe access tube (3), the liquid pressures inside the simulated blood vessel (2), the syringe access tube (3), the silicone tube access tube (4) and the rubber tube (6) are the same.

3. The venous catheter simulation training and training feedback device according to claim 2, characterized in that: The transmission device (7) is internally installed with a power module (71), a pressure sensor (72), a WIFI communication module (73) and a main control unit (74); The power module (71) supplies electric energy to the pressure sensor (72), the WIFI communication module (73) and the main control unit (74); The pressure sensor (72) is used to collect data on changes in the pressure of the liquid in the hose (6); The main control unit (74) is used to collect data from the pressure sensor (72), so that the pressure sensor (72) transmits the collected liquid pressure value to the main control unit (74); The WIFI communication module (73) sends an instruction to the main control unit (74), causing the main control unit (74) to transmit the data collected from the pressure sensor (72) to the WIFI communication module (73).

4. The venous catheter simulation training and training feedback device according to claim 3, characterized in that: The WIFI communication module (73) uses a fixed time period to send instructions when sending instructions to the main control unit (74).

5. The venous catheter simulation training and training feedback device according to claim 4, characterized in that: The display (8) is provided with a flushing pipe vortex simulation analysis module (81), the WIFI communication module (73) transmits the pressure data received by the main control unit (74) to the flushing pipe vortex simulation analysis module (81), and the flushing pipe vortex simulation analysis module (81) performs flushing pipe vortex simulation analysis upon receiving the pressure data transmitted by the WIFI communication module (73).

6. The venous catheter simulation training and training feedback device according to claim 5, characterized in that: The steps of the wash pipe vortex simulation analysis are as follows: ①, by using fluid mechanics to simulate the impact force of the fluid in the impact pipe, a geometric model of the central vein and the accessory vein and a geometric model of the hose (6) are established, and the established geometric model is divided into boundary layer grids, the pressure value range at the inlet position of the hose (6) is set, and an abnormal analysis is performed on the pressure at the inlet position of the hose (6); ②, using the optimal Latin hypercube algorithm, sampling the catheter pressure values ​​calculated in step ①, and obtaining multiple samples, and then solving and calculating the obtained samples to obtain eddy current result data of all samples; ③. Based on step ②, extract the inlet pressure data and eddy flow result data of all samples, and save the obtained inlet pressure data and eddy flow result data into 1 column matrix data respectively. All samples are saved into matrices by column. The matrix space size is the number of data points × the number of samples, and the principal component analysis algorithm is used to reduce the dimension of the data. ④. Use the convolutional neural network deep learning algorithm to process the pressure and eddy current data after dimensionality reduction to generate a deep learning model, save the trained deep learning model as an ONNX file, and use the ONNX Runtime inference engine to continuously input and monitor the pressure values ​​to obtain the pressure and eddy current data at each position in the catheter corresponding to each pressure value.

7. The venous catheter simulation training and training feedback device according to claim 1, characterized in that: The formula of the optimal Latin hypercube algorithm is: in, represents the evaluation function, s represents the number of samples, di represents the distance between the i-th sample points, j i It represents the number of distances between the i-th sample points, and p represents the probability of the i-th sample points.

8. The venous catheter simulation training and training feedback device according to claim 1, characterized in that: The specific steps of the principal component analysis algorithm are as follows: Ⅰ. Input: Sample set D = {x1, x2, ..., x m }; II. Centralize all samples: III. Calculate the sample covariance matrix XX T ; IV. Covariance matrix XX T Perform eigenvalue decomposition; Ⅴ. Take the eigenvectors w1, w2, …, w corresponding to the largest d eigenvalues d ; VI. Output: Projection matrix W* = (w1, w2, ..., w d ).

9. The venous catheter simulation training and training feedback device according to claim 5, characterized in that: The main control unit (74) is provided with a sending task monitoring buffer, and the liquid pressure data transmitted from the pressure sensor (72) is temporarily stored in the sending task monitoring buffer. After the liquid pressure data in the sending task monitoring buffer reaches a specified value, the WIFI communication module (73) transmits all the liquid pressure data in the sending task monitoring buffer to the flushing tube vortex simulation analysis module (81).

10. The venous catheter simulation training and training feedback device according to claim 9, characterized in that: The main control unit (74) is provided with a maximum and a minimum pressure response threshold. When the liquid pressure data transmitted by the pressure sensor (72) received by the main control unit (74) is higher than the maximum response threshold or lower than the minimum response threshold, the main control unit (74) feeds back to the WIFI communication module (73), so that the main control unit (74) transmits the information data to the display (8) for display.

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