Simulation system for hemostasis model of obstetrics and gynecology department based on augmented reality

Abstract

The invention discloses a simulation system of a gynecological hemostatic model based on augmented reality, which relates to the field of gynecological hemostatic training and comprises a data acquisition module, a model construction module, an augmented reality display module and an interactive feedback module. The data acquisition module acquires physiological and medical record data of a patient through multiple sensors, and accuracy is guaranteed through fusion and preprocessing; the model construction module is used for constructing hemostasis models with different difficulties by using a finite element method based on the acquired data, and simulating the mechanical characteristics of blood vessels; the augmented reality display module fuses the model and the real scene, and ensures accurate display by using an image registration algorithm; the interactive feedback module monitors operation in real time, calculates pressing force according to a formula, feeds back operation effects in various forms, adjusts the state of the model in real time, and provides real simulation for hemostasis training in the obstetrics and gynecology department. According to the invention, bleeding scenes are highly restored, personalized training is supported, evaluation is scientific and objective, cases are rich, remote cooperation can be realized, training efficiency and quality are improved, operation safety is guaranteed, and medical education development is promoted.

Description

Technical Field

[0001] The invention relates to the technical field of obstetrics and gynecology hemostasis training, and in particular to a simulation system of an obstetrics and gynecology hemostasis model based on augmented reality. Background Art

[0002] In obstetrics and gynecology surgeries, hemostasis is an extremely critical and challenging step. Effective hemostasis is directly related to the patient's life safety and postoperative recovery. Traditional obstetrics and gynecology hemostasis training mainly relies on actual clinical surgery observation and operation, but this method has many limitations.

[0003] In the actual clinical environment, surgical opportunities are limited, especially for complex gynecological and obstetric bleeding, and it is difficult for young doctors to have enough practice opportunities to master various hemostasis techniques. Moreover, practicing in real surgery will bring certain risks to patients, and hospitals and patients are often cautious about this, which makes the process of novice doctors accumulating experience slow and difficult.

[0004] Most of the existing obstetrics and gynecology hemostasis models are static models that lack dynamic simulation of real bleeding scenarios. These models cannot accurately present the characteristics of different types of bleeding (such as arterial bleeding, venous bleeding, and capillary bleeding), including differences in bleeding speed, pressure, and blood color. When doctors use static models for training, it is difficult to obtain the sense of urgency and operational feedback in real surgery, resulting in the inability to quickly make accurate judgments and effective hemostasis operations when faced with sudden bleeding in actual surgery.

[0005] Traditional training methods lack an effective evaluation mechanism, making it difficult to conduct a comprehensive and objective evaluation of doctors' hemostasis skills. During the training process, trainees' performance can only be evaluated through subjective judgment by the instructor, lacking quantitative indicators and a scientific evaluation system. This makes it difficult for trainees to clearly understand their strengths and weaknesses in hemostasis operations, which is not conducive to targeted skill improvement.

[0006] In addition, in the traditional training model, collaborative training between doctors is difficult. Gynecological and obstetric surgery usually requires close cooperation between team members, such as doctors and nurses. However, in actual training, due to the limitations of venue, time and resources, it is difficult to organize multiple people to conduct collaborative training at the same time, which affects the cultivation of team collaboration ability.

[0007] With the development of science and technology, augmented reality (AR) technology has gradually been applied to the field of medical education. AR technology can combine virtual information with real scenes to provide users with an immersive experience. However, there are relatively few simulation systems based on augmented reality in obstetrics and gynecology hemostasis training. Existing systems may have problems such as inaccurate model construction, inaccurate and timely interactive feedback, lack of a rich case library and personalized difficulty adjustment, and cannot fully meet the needs of obstetrics and gynecology hemostasis training.

[0008] Therefore, it is of great practical significance to develop a simulation system and method for the hemostasis model of obstetrics and gynecology based on augmented reality. The system and method can provide more realistic and rich training scenarios, improve doctors' hemostasis skills and teamwork ability, and provide scientific and objective evaluation of doctors' training effects, which will help promote the development and progress of obstetrics and gynecology medical education. Summary of the invention

[0009] The present invention proposes a simulation system of an obstetrics and gynecology hemostasis model based on augmented reality to solve the problems mentioned in the above-mentioned prior art.

[0010] In order to achieve the above object, the present invention adopts the following technical solution: a simulation system of a gynecological and obstetric hemostasis model based on augmented reality, comprising:

[0011] Data acquisition module: equipped with pressure sensor, flow sensor, photoelectric sensor, heart rate sensor equipment, using multi-sensor fusion technology to perform weighted fusion on the collected data from different sensors, and through the formula Calculate the collected data, where X fused is the fused data, X i is the data collected by the i-th sensor, w i is the weight of the ith sensor, and For blood pressure data, the oscillometric method is used to measure the pressure fluctuation signal collected by the pressure sensor through fast Fourier transform analysis, and the formula is used Calculate the average blood pressure value, BP mean is the mean blood pressure, P sys is systolic blood pressure, P dia is diastolic blood pressure;

[0012] Model building module: using the collected data, combined with obstetrics and gynecology medical knowledge and experience, to build obstetrics and gynecology hemostasis models with different bleeding characteristics and difficulty levels, using the finite element analysis method to model the vascular structure, dividing the blood vessels into multiple finite element units, and using the formula σ = Eε to simulate the mechanical properties of blood vessels under different pressures, σ is stress, E is elastic modulus, and ε is strain;

[0013] Augmented reality display module: The constructed obstetrics and gynecology hemostasis model is integrated with the real operation scene. The virtual hemostasis model is presented in the operator's field of view through augmented reality equipment, specifically AR glasses, so that the operator can intuitively see the bleeding site and surrounding tissue structure. The image registration algorithm based on feature matching is used. Calculate the distance between feature points, (x i1 ,y i1 ) and (x i2 ,y i2 ) are the coordinates of the corresponding feature points in the real scene and the virtual model respectively;

[0014] Interactive feedback module: real-time monitoring of the operator's operating actions, specifically the pressing position, force, and time, and providing the operator with corresponding feedback information based on the requirements of the hemostasis model and the actual situation. Pressure sensors and displacement sensors are used to monitor the pressing force and position, and the pressing force is calculated using the formula F=kx, where F is the pressing force, k is the elastic coefficient, and x is the displacement.

[0015] Furthermore, it also includes:

[0016] Difficulty adjustment module: Users can adjust the difficulty level of the hemostasis model according to their own skill level and training needs. Difficulty adjustment includes changing the bleeding speed, increasing the number of bleeding points, and simulating complex vascular structures. Difficulty adjustment is achieved by modifying the parameters of the hemostasis model. The parameter modification is based on the preset difficulty coefficient table. The relationship between the difficulty coefficient D and the bleeding speed v and the number of bleeding points n is D = a × v + b × n + c, where D is the difficulty coefficient, a, b, c are preset coefficients, v is the bleeding speed, and n is the number of bleeding points;

[0017] Recording and evaluation module: records the operator's operation data and results during the simulation training process, specifically the operation time, hemostasis success rate, and bleeding control situation. The operator's performance is evaluated according to the preset evaluation criteria and an evaluation report is generated. A multi-index comprehensive evaluation method is used, and the evaluation indicators include operation accuracy A, operation efficiency E, and bleeding control rate C. The comprehensive evaluation score S = w 1 ×A+w 2 ×E+w 3 ×C, S is the comprehensive evaluation score, w 1 、w 2 、w 3 is the weight of each indicator, A is the operation accuracy, E is the operation efficiency, C is the bleeding control rate, and the evaluation results are presented in the form of charts and text reports.

[0018] Furthermore, it also includes:

[0019] Case library module: stores gynecological and obstetric bleeding cases, including real cases and simulated cases. Users can select different cases for simulation training according to their needs. The case library module has case update and retrieval functions, updates case data in a timely manner, and retrieves relevant cases based on the keywords entered by the user. It uses text similarity algorithm for case retrieval and uses formula Calculate the similarity between the keyword and the case text, Sim is the similarity between the keyword and the case text, w i1 is the weight of the i-th word in the keyword, w i2 is the weight of the i-th word in the case text;

[0020] Remote collaboration module: supports multiple users to conduct remote collaborative training via the network. During the training process, operators in different locations share operation images and data in real time, communicate and collaborate, and jointly complete the hemostasis operation. It uses real-time video streaming technology and data synchronization algorithm to sync =T send -T receive Calculate the synchronization error, T sync is the synchronization error, T send is the sending time, T receive The receiving time.

[0021] Furthermore, the difficulty adjustment module realizes difficulty adjustment by modifying the parameters of the hemostasis model. The parameter modification is based on a preset difficulty coefficient table. The relationship between the difficulty coefficient D and parameters such as the bleeding speed v and the number of bleeding points n is D=a×v+b×n+c, where D is the difficulty coefficient, a, b, and c are preset coefficients, v is the bleeding speed, and n is the number of bleeding points. According to different difficulty levels, the bleeding speed, number of bleeding points, vascular elastic modulus, and tube diameter parameters are comprehensively adjusted.

[0022] Furthermore, the record evaluation module adopts a multi-index comprehensive evaluation method, and the evaluation indicators include operation accuracy A, operation efficiency E, and bleeding control rate C. The comprehensive evaluation score S=w 1 ×A+w 2 ×E+w 3 ×C, S is the comprehensive evaluation score, w 1 、w 2 、w 3 is the weight of each indicator, A is the operation accuracy, E is the operation efficiency, and C is the bleeding control rate; the operation accuracy is calculated by the deviation between the operator's pressing position and the correct position, using the Euclidean distance formula Calculate the deviation value, d is the deviation distance between the pressing position and the correct position, (x 1 ,y 1 ) is the coordinate of the operator's pressing position, (x 2 ,y 2) is the coordinate of the correct position; the operation efficiency is based on the operation time t actual With standard time t standard The ratio of The bleeding volume control rate is determined by the actual controlled bleeding volume V controlled The initial bleeding volume V initial Measured by the ratio of The evaluation results are presented in the form of charts and text reports, including bar charts and line graphs, which intuitively display the scores of each evaluation indicator.

[0023] Furthermore, the case library module has case update and retrieval functions, timely updates case data, and retrieves cases based on keywords entered by users; uses text similarity algorithm for case retrieval, and uses formula Calculate the similarity between the keyword and the case text, Sim is the similarity between the keyword and the case text, w i1 is the weight of the i-th word in the keyword, w i2 is the weight of the i-th word in the case text.

[0024] Furthermore, it also includes:

[0025] Data collection steps: Multi-sensor fusion technology is used to collect physiological data and medical records of obstetrics and gynecology patients in different bleeding scenarios through pressure sensors, flow sensors, photoelectric sensors, and heart rate sensors. For blood pressure data, the oscillometric method is used to measure and the formula is used to calculate the blood pressure. Calculate the average blood pressure value, BP mean is the mean blood pressure, P sys is systolic blood pressure, P dia is the diastolic pressure; the collected data from different sensors are weighted and fused, and the formula Calculate data, X fused is the fused data, X i is the data collected by the i-th sensor, w i is the weight of the ith sensor, and

[0026] Model construction steps: Based on the collected data, combined with obstetrics and gynecology medical knowledge and experience, the finite element analysis method was used to construct an obstetrics and gynecology hemostasis model with different bleeding characteristics and difficulty levels. The mechanical properties of the blood vessels were simulated by the formula σ=Eε, where σ is stress, E is elastic modulus, and ε is strain. The blood vessels were divided into multiple finite element units, and the model was refined and optimized considering the branching structure, diameter change, and wall thickness difference of the blood vessels.

[0027] Augmented reality fusion steps: The constructed hemostasis model is integrated with the real operation scene, and the image registration algorithm based on feature matching is used. Calculate the distance between feature points, (x i1 ,y i1 ) and (x i2 ,y i2 ) are the coordinates of the corresponding feature points in the real scene and the virtual model, respectively, and the virtual hemostasis model is accurately presented in the operator's field of vision through an augmented reality device, specifically AR glasses;

[0028] Interactive operation steps: the operator performs hemostasis operation, and the system monitors the operation in real time through pressure sensors and displacement sensors, and uses the formula F=kx to calculate the pressing force, where F is the pressing force, k is the elastic coefficient, and x is the position), and gives feedback according to the requirements of the hemostasis model; the operator's operation trajectory and action sequence are recorded, and the feedback information is presented in multiple forms such as visual prompts, auditory prompts and tactile feedback.

[0029] Furthermore, it also includes:

[0030] Difficulty adjustment steps: according to user needs, modify the hemostasis model parameters according to the preset difficulty coefficient table D = a × v + b × n + c, D is the difficulty coefficient, a, b, c are preset coefficients, v is the bleeding speed, and n is the number of bleeding points; when adjusting the difficulty, comprehensively consider the bleeding speed, number of bleeding points, vascular elastic modulus, and tube diameter parameters, and automatically recommend the difficulty level based on the operator's historical training data and evaluation results to achieve personalized training;

[0031] Record evaluation steps: record operation data and results, adopt multi-index comprehensive evaluation method, and use the formula S=w 1 ×A+w 2 ×E+w 3 ×C is used for evaluation, S is the comprehensive evaluation score, w 1 、w 2 、w 3 is the weight of each indicator, A is the operation accuracy, E is the operation efficiency, and C is the bleeding control rate; the operation accuracy is calculated by the deviation between the operator's pressing position and the correct position, using the Euclidean distance formula Calculate the deviation value, d is the deviation distance between the pressing position and the correct position, (x 1 ,y 1 ) is the coordinate of the operator's pressing position, (x 2 ,y 2 ) is the coordinate of the correct position; the operation efficiency is based on the operation time t actual With standard time t standard The ratio is determined The bleeding volume control rate is determined by the actual controlled bleeding volume V controlled The initial bleeding volume V initial The ratio is measured The evaluation results are presented in the form of charts and text reports.

[0032] Case selection step: Select gynecological and obstetric bleeding cases from the case library for simulation training, using the text similarity algorithm Perform case retrieval, Sim is the similarity between the keyword and the case text, w i1 is the weight of the i-th word in the keyword, w i2 is the weight of the i-th word in the case text;

[0033] Remote collaboration steps: Multiple users conduct remote collaborative training via the Internet, using real-time video streaming technology and data synchronization algorithms. sync =T send -T receive Calculate the synchronization error, T sync is the synchronization error, T send is the sending time, T receive In order to receive time, during the training process, operators in different locations share operation screens and data in real time. At the same time, the system provides real-time voice communication and text chat functions to facilitate communication and collaboration between operators.

[0034] Furthermore, the simulation method of the obstetrics and gynecology hemostasis model based on augmented reality is characterized in that, in the data acquisition step, multi-sensor fusion technology is used to perform weighted fusion on the collected data from different sensors, and the performance of each sensor is monitored and evaluated in real time; all sensors are calibrated before data collection, and the calibration process follows strict industry standards and specifications, and the collected physiological data are classified and stored according to different types and time series.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] In terms of training effect, the hemostasis model constructed by the system through data collected by high-precision sensors highly restores the real obstetrics and gynecology bleeding scene, including the characteristics of different types of bleeding and complex vascular structures. Doctors can practice repeatedly in an augmented reality environment to gain an operating experience similar to that of actual surgery, effectively improving the proficiency and accuracy of hemostasis skills. Moreover, the system's real-time interactive feedback function allows doctors to understand the operation effect in a timely manner, identify problems and make improvements, and accelerate skill improvement.

[0037] Personalized training is a major advantage of the system. The difficulty adjustment module can flexibly adjust the difficulty of the hemostasis model according to the doctor's skill level and training needs, and realize personalized training programs. Whether it is a novice doctor conducting basic skill training or an experienced doctor challenging complex bleeding situations, they can find a training difficulty that suits them and improve the pertinence and efficiency of training.

[0038] The scientific and objective nature of the evaluation mechanism provides strong support for doctors' training. The record evaluation module uses a multi-index comprehensive evaluation method to quantitatively evaluate doctors' operation accuracy, efficiency, and bleeding control rate, and generate detailed reports. Doctors can clearly understand their strengths and weaknesses and strengthen their skills in a targeted manner. At the same time, training managers can also adjust training plans based on evaluation results to improve the overall training quality.

[0039] The rich case library and remote collaboration function further enhance the practicality of the system. The case library stores a large number of real and simulated cases, and doctors can choose different types of cases for training to broaden their clinical horizons. The remote collaboration function supports multiple doctors to train at the same time in different locations, share operation pictures and data in real time, promote the cultivation of team collaboration ability, and improve the ability to deal with complex surgical situations.

[0040] In the long run, the application of this system and method can reduce the mistakes made by novice doctors in actual operations, improve the safety and success rate of gynecological and obstetric operations, and provide more reliable protection for the life and health of patients. At the same time, it will also help promote the modernization and scientific development of gynecological and obstetric medical education. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic block diagram of a simulation method of an obstetrics and gynecology hemostasis model based on augmented reality proposed by the present invention;

[0042] Figure 2 This is a schematic block diagram of a simulation system of an obstetrics and gynecology hemostasis model based on augmented reality proposed by the present invention. DETAILED DESCRIPTION

[0043] 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.

[0044] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0045] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0046] Reference Figure 1-2 :A simulation system of an obstetrics and gynecology hemostasis model based on augmented reality, comprising:

[0047] Data acquisition module: equipped with high-precision pressure sensors, flow sensors, photoelectric sensors, heart rate sensors and other types of sensor equipment. The pressure sensor adopts the piezoresistive principle, with a measurement range of 0-50kPa and an accuracy of ±0.1kPa, which can accurately sense changes in blood pressure; the flow sensor adopts the ultrasonic principle, with a measurement range of 0-1000ml / min and an accuracy of ±1ml / min, which can accurately measure the amount of bleeding and bleeding rate; the photoelectric sensor is used to monitor the optical properties of blood and assist in determining the type of bleeding; the heart rate sensor uses photoplethysmography to obtain the patient's heart rate in real time, with a measurement accuracy of ±1 times / minute. Multi-sensor fusion technology is used to improve the accuracy and reliability of physiological data acquisition, and the collected data from different sensors is weighted and fused through the formula (Where X fused is the fused data, X i is the data collected by the i-th sensor, w i is the weight of the ith sensor, and ) to ensure the accuracy and comprehensiveness of the collected data. At the same time, the module also has data preprocessing functions, which will filter, denoise, normalize and perform other operations on the collected raw data to remove interference signals and improve data quality. In addition, the module can be connected to the hospital's electronic medical record system to automatically collect the patient's medical record information and related clinical examination results, including the patient's age, medical history, allergy history, various examination indicators, etc., to provide a rich and accurate data foundation for subsequent model construction. For blood pressure data, the oscillometric method is used to measure, and the pressure fluctuation signal collected by the pressure sensor is analyzed by fast Fourier transform (FFT), and the formula is used. (BPmean is the mean blood pressure, P sys is systolic blood pressure, P dia The average blood pressure value was calculated to provide more accurate data for subsequent model construction.

[0048] Model construction module: Using the collected data, combined with obstetrics and gynecology medical knowledge and experience, an obstetrics and gynecology hemostasis model with different bleeding characteristics and difficulty levels is constructed. The model contains detailed information such as vascular structure, tissue hierarchy, bleeding point location and bleeding type. The finite element analysis method is used to model the vascular structure, and the blood vessels are divided into multiple finite element units. The mechanical properties of blood vessels under different pressures are simulated by the formula σ=Eε (where σ is stress, E is elastic modulus, and ε is strain), so that the model is more in line with actual physiological conditions. At the same time, considering factors such as the branching structure of the blood vessels, changes in diameter, and differences in wall thickness, the model is refined and optimized. The construction of the tissue hierarchy refers to human anatomy knowledge to simulate the physical properties and relationships of different tissues. The setting of bleeding point location and bleeding type is classified and defined based on a large number of clinical cases and medical research, including different types such as arterial bleeding, venous bleeding, and capillary bleeding, each with different bleeding speed, pressure and color characteristics. The model also has a dynamic update function, which can be continuously optimized and improved based on newly collected data and medical research results.

[0049] Augmented reality display module: The constructed obstetrics and gynecology hemostasis model is integrated with the real operation scene, and the virtual hemostasis model is accurately presented in the operator's field of view through augmented reality equipment (such as AR glasses), so that the operator can intuitively see the bleeding site and surrounding tissue structure. The image registration algorithm based on feature matching is used, and the formula (where (x i1 ,y i1 ) and (x i2 ,y i2 ) are the coordinates of the corresponding feature points in the real scene and the virtual model respectively) calculate the distance between the feature points to ensure the accurate fusion of the virtual model and the real scene, and the fusion error is controlled within ±2mm. The module also supports multiple display modes, such as stereoscopic display, transparent display, etc., and the operator can switch according to actual needs. At the same time, in order to improve the display effect, a high-resolution display screen and advanced optical projection technology are used to ensure the clear display and realism of the virtual model. In addition, the module also has a voice prompt function, which can provide the operator with operation guidance and precautions in real time.

[0050] Interactive feedback module: monitors the operator's operation in real time, such as the pressing position, force, time, etc., and gives the operator corresponding feedback information according to the requirements of the hemostasis model and the actual situation, such as prompts on whether the operation is correct or not, changes in the amount of bleeding, etc. Pressure sensors and displacement sensors are used to monitor the pressing force and position, and the pressing force is calculated by the formula F=kx (where F is the pressing force, k is the elastic coefficient, and x is the displacement), and the operation is judged to be correct according to the preset force range. The module also records the operator's operation trajectory and action sequence for subsequent analysis and evaluation. Feedback information is presented in various forms, including visual prompts (such as color changes, graphic marks), auditory prompts (such as voice warnings, sound effects) and tactile feedback (such as vibration), so that the operator can understand the operation effect in time. At the same time, the module will adjust the state of the hemostasis model in real time according to the operator's operation, such as changing the bleeding speed and bleeding volume, etc., to increase the authenticity and challenge of the training.

[0051] The present invention also includes the following modules:

[0052] Difficulty adjustment module: allows users to adjust the difficulty level of the hemostasis model according to their own skill level and training needs. Difficulty adjustment can include changing the bleeding speed, increasing the number of bleeding points, simulating complex vascular structures, etc. The difficulty adjustment is achieved by modifying the parameters of the hemostasis model. The parameter modification is based on the preset difficulty coefficient table. The relationship between the difficulty coefficient D and the parameters such as the bleeding speed v and the number of bleeding points n is D = a × v + b × n + c (where D is the difficulty coefficient, a, b, c are preset coefficients, v is the bleeding speed, and n is the number of bleeding points), ensuring the scientificity and rationality of the difficulty adjustment. This module provides a detailed difficulty setting interface. Users can choose different difficulty levels according to their needs, or customize difficulty parameters. When adjusting the difficulty, the system will automatically adjust the relevant parameters of the hemostasis model according to the difficulty coefficient table, while ensuring the rationality of the physical characteristics and physiological logic of the model. For example, when the difficulty level is increased, not only the bleeding speed and the number of bleeding points will be increased, but also the elastic modulus and diameter of the blood vessels will be adjusted to simulate more complex bleeding situations.

[0053] Recording and evaluation module: records the operator's operation data and results during the simulation training, such as operation time, hemostasis success rate, bleeding control, etc., and evaluates the operator's performance according to the preset evaluation criteria to generate a detailed evaluation report. A multi-index comprehensive evaluation method is used, and the evaluation indicators include operation accuracy A, operation efficiency E, bleeding control rate C, etc. The comprehensive evaluation score S = w 1 ×A+w 2 ×E+w 3 ×C (where S is the comprehensive evaluation score, w 1 、w 2 、w 3The evaluation results are presented in the form of intuitive charts and text reports. The operation accuracy is calculated by the deviation between the operator's pressing position and the correct position. The smaller the deviation, the higher the accuracy. The operation efficiency is determined by the ratio of the operation time to the standard time. The closer the ratio is to 1, the higher the efficiency. The bleeding control rate is measured by the ratio of the actual controlled bleeding volume to the initial bleeding volume. The lower the ratio, the better the control effect. The module will also analyze the operator's operation trajectory, action sequence, etc., find out the advantages and disadvantages in the operation, and provide targeted improvement suggestions. The evaluation report can be saved and printed, which is convenient for the operator to review and summarize.

[0054] Case Library Module: Stores a large number of gynecological and obstetric bleeding cases, including real cases and simulated cases. Each case contains detailed patient information, bleeding conditions, treatment plans, etc. Users can select different cases for simulation training as needed. The case library module has case update and retrieval functions, which can update case data in a timely manner and quickly retrieve relevant cases based on the keywords entered by the user. The text similarity algorithm is used for case retrieval, and the formula (Sim is the similarity between the keyword and the case text, w i1 is the weight of the i-th word in the keyword, w i2 The similarity between the keyword and the case text is calculated (where the weight of the i-th word in the case text is the weight of the i-th word in the case text). The cases with higher similarity are displayed first. The case library regularly obtains the latest case data from clinical databases and medical research institutions for update to ensure the timeliness and practicality of the cases. At the same time, the case library also supports the classification management of cases. Users can search by category according to the type of case, difficulty level, type of disease, etc., to improve the search efficiency. For each case, a detailed analysis and interpretation is also provided, including the cause of bleeding, treatment ideas, precautions, etc., to help operators better understand and master the case.

[0055] Remote collaboration module: supports multiple users to conduct remote collaborative training via the network. During the training process, operators in different locations can share operation images and data in real time, communicate and collaborate, and jointly complete the hemostasis operation. Real-time video streaming technology and data synchronization algorithm are used to ensure the real-time and consistency of operation images and data. The data synchronization error is controlled within ±50ms, and the formula T sync =T send -T receive (where T sync is the synchronization error, T send is the sending time, T receiveThe module uses advanced network transmission protocols and optimization algorithms to ensure stable and smooth collaborative training in different network environments. At the same time, it provides real-time voice communication and text chat functions to facilitate communication and collaboration between operators. In addition, it also has a permission management function, and the administrator can set the operation permissions of different users to ensure the security and standardization of collaborative training.

[0056] In the present invention, the difficulty adjustment module realizes difficulty adjustment by modifying the parameters of the hemostasis model. The parameter modification is based on a preset difficulty coefficient table. The relationship between the difficulty coefficient D and the parameters such as the bleeding speed v and the number of bleeding points n is D=a×v+b×n+c (where D is the difficulty coefficient, a, b, c are preset coefficients, v is the bleeding speed, and n is the number of bleeding points). According to different difficulty levels, the bleeding speed, the number of bleeding points, the elastic modulus of the blood vessel, the diameter and other parameters are comprehensively adjusted to ensure the scientificity and rationality of the difficulty adjustment. For example, when the difficulty level increases, the bleeding speed and the number of bleeding points are increased accordingly according to the difficulty coefficient formula, and the elastic modulus of the blood vessel is reduced at the same time, simulating a more fragile blood vessel structure and increasing the difficulty of hemostasis. The module will also automatically recommend a suitable difficulty level based on the operator's historical training data and evaluation results to achieve personalized training.

[0057] In the present invention, the record evaluation module adopts a multi-index comprehensive evaluation method, and the evaluation indicators include operation accuracy A, operation efficiency E, bleeding control rate C, etc. The comprehensive evaluation score S=w 1 ×A+w 2 ×E+w 3 ×C (where S is the comprehensive evaluation score, w 1 、w 2 、w 3 is the weight of each indicator, A is the operation accuracy, E is the operation efficiency, and C is the bleeding control rate). The operation accuracy is calculated by the deviation between the operator's pressing position and the correct position, using the Euclidean distance formula (where d is the deviation distance between the pressing position and the correct position, (x 1 ,y 1 ) is the coordinate of the operator's pressing position, (x 2 ,y 2 ) is the coordinate of the correct position) calculates the deviation value; the operation efficiency is based on the operation time t actual With standard time t standard The ratio of is determined, that is The bleeding volume control rate is determined by the actual controlled bleeding volume V controlled The initial bleeding volume V initial is measured by the ratio of The evaluation results are presented in the form of intuitive charts and text reports. The charts include bar charts, line charts, etc., which intuitively show the scores of various evaluation indicators. The text report analyzes the operator's strengths and weaknesses in detail and provides specific improvement suggestions to help the operator improve the training effect.

[0058] In the present invention, the case library module has case update and retrieval functions, can timely update case data, and quickly retrieve relevant cases according to the keywords input by the user. The text similarity algorithm is used for case retrieval, and the formula (Sim is the similarity between the keyword and the case text, w i1 is the weight of the i-th word in the keyword, w i2 The similarity between the keyword and the case text is calculated by using the weight of the i-th word in the case text. The case library regularly obtains the latest case data from multiple authoritative clinical databases and medical research institutions for update, and the update cycle is once a month. When updating cases, new cases will be reviewed and sorted to ensure the quality and accuracy of the cases. At the same time, the case library also supports fuzzy retrieval function, which can retrieve relevant cases even if the keywords do not match completely, thereby improving the flexibility of retrieval. For the retrieved cases, a case preview function is also provided, so that users can quickly understand the basic information and main content of the case without opening the case details.

[0059] The present invention also discloses a simulation method of a gynecological and obstetric hemostasis model based on augmented reality, comprising the following steps:

[0060] Data collection steps: Multi-sensor fusion technology is used to collect physiological data and medical records of obstetrics and gynecology patients in different bleeding scenarios through high-precision pressure sensors, flow sensors, photoelectric sensors, heart rate sensors and other equipment. For blood pressure data, the oscillometric method is used to measure and the formula is used to calculate the blood pressure. (BP mean is the mean blood pressure, P sys is systolic blood pressure, P dia The average blood pressure value is calculated (where diastolic pressure is the mean blood pressure value). During the collection process, the raw data is pre-processed by filtering, denoising, and normalization to remove interference signals and improve data quality. At the same time, it is connected to the hospital's electronic medical record system to automatically obtain the patient's medical record information and related clinical examination results. The collected data from different sensors are weighted and fused, and the formula is used to calculate the average blood pressure value. (Where X fused is the fused data, X i is the data collected by the i-th sensor, w i is the weight of the ith sensor, and ) Ensure the accuracy and completeness of collected data.

[0061] Model construction steps: Based on the collected data, combined with obstetrics and gynecology medical knowledge and experience, the finite element analysis method is used to construct an obstetrics and gynecology hemostasis model with different bleeding characteristics and difficulty levels, and the formula σ=Eε (where σ is stress, E is elastic modulus, and ε is strain) is used to simulate the mechanical properties of blood vessels. The blood vessels are divided into multiple finite element units, and the model is refined and optimized considering factors such as the branching structure of the blood vessels, changes in diameter, and differences in wall thickness. The construction of the tissue hierarchy refers to human anatomy knowledge to simulate the physical properties and relationships of different tissues. The location of the bleeding point and the setting of the bleeding type are classified and defined based on a large number of clinical cases and medical research. The model has a dynamic update function and can be continuously optimized and improved based on newly collected data and medical research results.

[0062] Augmented reality fusion steps: The constructed hemostasis model is integrated with the real operation scene, and the image registration algorithm based on feature matching is used. (where (x i1 ,y i1 ) and (x i2 ,y i2 ) are the coordinates of the corresponding feature points in the real scene and the virtual model respectively) calculate the distance between the feature points to ensure the accurate fusion of the virtual model and the real scene, and the fusion error is controlled within ±2mm. The virtual hemostasis model is accurately presented in the operator's field of view through augmented reality equipment (such as AR glasses). This step supports multiple display modes, such as stereoscopic display, transparent display, etc., and the operator can switch according to actual needs. At the same time, high-resolution display screens and advanced optical projection technology are used to ensure the clear display and realism of the virtual model, and it has a voice prompt function to provide operators with real-time operation guidance and precautions.

[0063] Interactive operation steps: The operator performs hemostasis operation, and the system monitors the operation in real time through pressure sensors and displacement sensors, calculates the pressing force using the formula F=kx (where F is the pressing force, k is the elastic coefficient, and x is the displacement), and gives feedback according to the requirements of the hemostasis model. The operator's operation trajectory and action sequence are recorded, and the feedback information is presented in various forms such as visual prompts (such as color changes, graphic marks), auditory prompts (such as voice warnings, sound effects) and tactile feedback (such as vibration). The system will adjust the state of the hemostasis model in real time according to the operator's operation, such as changing the bleeding speed and bleeding volume, to increase the authenticity and challenge of the training.

[0064] The present invention also includes the following steps:

[0065] Difficulty adjustment step: According to user needs, modify the hemostasis model parameters according to the preset difficulty coefficient table D=a×v+b×n+c (where D is the difficulty coefficient, a, b, c are preset coefficients, v is the bleeding speed, and n is the number of bleeding points) to adjust the training difficulty. This step provides a detailed difficulty setting interface, and users can choose different difficulty levels or customize difficulty parameters. When adjusting the difficulty, multiple parameters such as bleeding speed, number of bleeding points, vascular elastic modulus, and tube diameter are comprehensively considered to ensure the rationality of the model's physical properties and physiological logic. At the same time, based on the operator's historical training data and evaluation results, the appropriate difficulty level is automatically recommended to achieve personalized training.

[0066] Record evaluation steps: record operation data and results, adopt multi-index comprehensive evaluation method, and use the formula S=w 1 ×A+w 2 ×E+w 3 ×C (where S is the comprehensive evaluation score, w 1 、w 2 、w 3 The operation accuracy is calculated by the deviation between the operator's pressing position and the correct position, using the Euclidean distance formula. (where d is the deviation distance between the pressing position and the correct position, (x 1 ,y 1 ) is the coordinate of the operator's pressing position, (x 2 ,y 2 ) is the coordinate of the correct position) calculates the deviation value; the operation efficiency is based on the operation time t actual With standard time t standard The ratio of is determined, that is The bleeding volume control rate is determined by the actual controlled bleeding volume V controlled The initial bleeding volume V initial is measured by the ratio of The evaluation results are presented in the form of intuitive charts and text reports. The charts include bar charts, line graphs, etc. The text reports analyze the operator's strengths and weaknesses in detail and provide improvement suggestions.

[0067] Case selection step: Select appropriate gynecological and obstetric bleeding cases from the case library for simulation training, using the text similarity algorithm (Sim is the similarity between the keyword and the case text, w i1 is the weight of the i-th word in the keyword, w i2The case database regularly obtains the latest case data from multiple authoritative clinical databases and medical research institutions for update, and the update cycle is once a month. This step supports fuzzy search and case preview functions. Even if the keywords do not match completely, relevant cases can be retrieved. Users can also quickly understand the basic information and main content of the case without opening the case details.

[0068] Remote collaboration steps: Multiple users conduct remote collaborative training via the Internet, using real-time video streaming technology and data synchronization algorithms. sync =T send -T receive (where T sync is the synchronization error, T send is the sending time, T receive The synchronization error is calculated for the receiving time, and the data synchronization error is controlled within ±50ms. During the training process, operators in different locations can share the operation screen and data in real time. At the same time, the system provides real-time voice communication and text chat functions to facilitate communication and collaboration between operators. In addition, it has a permission management function, and the administrator can set the operation permissions of different users to ensure the security and standardization of collaborative training.

[0069] In the present invention, in the data collection step, multi-sensor fusion technology is used to improve the accuracy and reliability of physiological data collection. In addition to weighted fusion of the collected data from different sensors, the performance of each sensor is also monitored and evaluated in real time. If a sensor has abnormal data fluctuations or exceeds the preset error range, the system will automatically start the backup sensor or use the data repair algorithm to correct the abnormal data. At the same time, to ensure the consistency and accuracy of the data, all sensors will be calibrated before collecting data, and the calibration process follows strict industry standards and specifications. The collected physiological data will be classified and stored according to different types and time series to facilitate subsequent model construction and analysis.

[0070] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A simulation system of a gynecological and obstetric hemostasis model based on augmented reality, characterized in that: include: Data acquisition module: equipped with pressure sensor, flow sensor, photoelectric sensor, heart rate sensor equipment, using multi-sensor fusion technology to perform weighted fusion on the collected data from different sensors, and through the formula Calculate the collected data, where X fused is the fused data, X i is the data collected by the i-th sensor, w i is the weight of the ith sensor, and For blood pressure data, the oscillometric method is used to measure the pressure fluctuation signal collected by the pressure sensor through fast Fourier transform analysis, and the formula is used Calculate the average blood pressure value, BP mean is the mean blood pressure, P sys is systolic blood pressure, P dia is diastolic blood pressure; Model building module: using the collected data, combined with obstetrics and gynecology medical knowledge and experience, to build obstetrics and gynecology hemostasis models with different bleeding characteristics and difficulty levels, using the finite element analysis method to model the vascular structure, dividing the blood vessels into multiple finite element units, and using the formula σ = Eε to simulate the mechanical properties of blood vessels under different pressures, σ is stress, E is elastic modulus, and ε is strain; Augmented reality display module: The constructed obstetrics and gynecology hemostasis model is integrated with the real operation scene. The virtual hemostasis model is presented in the operator's field of view through augmented reality equipment, specifically AR glasses. The operator can intuitively see the bleeding site and surrounding tissue structure. The image registration algorithm based on feature matching is used. Calculate the distance between feature points, (x i1 ,y i1 ) and (x i2 ,y i2 ) are the coordinates of the corresponding feature points in the real scene and the virtual model respectively; Interactive feedback module: real-time monitoring of the operator's operating actions, specifically the pressing position, force, and time, and providing the operator with corresponding feedback information based on the requirements of the hemostasis model and the actual situation. Pressure sensors and displacement sensors are used to monitor the pressing force and position, and the pressing force is calculated using the formula F=kx, where F is the pressing force, k is the elastic coefficient, and x is the displacement.

2. The simulation system of the obstetrics and gynecology hemostasis model based on augmented reality according to claim 1, characterized in that: Also includes: Difficulty adjustment module: Users can adjust the difficulty level of the hemostasis model according to their own skill level and training needs. Difficulty adjustment includes changing the bleeding speed, increasing the number of bleeding points, and simulating complex vascular structures. Difficulty adjustment is achieved by modifying the parameters of the hemostasis model. The parameter modification is based on the preset difficulty coefficient table. The relationship between the difficulty coefficient D and the bleeding speed v and the number of bleeding points n is D = a × v + b × n + c, where D is the difficulty coefficient, a, b, c are preset coefficients, v is the bleeding speed, and n is the number of bleeding points; Recording and evaluation module: records the operator's operation data and results during the simulation training process, specifically the operation time, hemostasis success rate, and bleeding volume control situation. The operator's performance is evaluated according to the preset evaluation criteria and an evaluation report is generated. A multi-indicator comprehensive evaluation method is adopted. The evaluation indicators include operation accuracy A, operation efficiency E, and bleeding volume control rate C. The comprehensive evaluation score S = w1×A+w2×E+w3×C, where S is the comprehensive evaluation score, w1, w2, and w3 are the weights of each indicator, A is the operation accuracy, E is the operation efficiency, and C is the bleeding volume control rate. The evaluation results are presented in the form of charts and text reports.

3. The simulation system of the obstetrics and gynecology hemostasis model based on augmented reality according to claim 1, characterized in that: Also includes: Case library module: stores gynecological and obstetric bleeding cases, including real cases and simulated cases. Users can select different cases for simulation training according to their needs. The case library module has case update and retrieval functions, updates case data in a timely manner, retrieves relevant cases based on the keywords entered by the user, and uses text similarity algorithm for case retrieval. Calculate the similarity between the keyword and the case text, Sim is the similarity between the keyword and the case text, w i1 is the weight of the i-th word in the keyword, w i2 is the weight of the i-th word in the case text; Remote collaboration module: supports multiple users to conduct remote collaborative training via the network. During the training process, operators in different locations share operation images and data in real time, communicate and collaborate, and jointly complete the hemostasis operation. It uses real-time video streaming technology and data synchronization algorithm to sync =T send- T receive Calculate the synchronization error, T sync is the synchronization error, T send is the sending time, T receive The receiving time.

4. The simulation system of the obstetrics and gynecology hemostasis model based on augmented reality according to claim 2, characterized in that: The difficulty adjustment module realizes difficulty adjustment by modifying the parameters of the hemostasis model. The parameter modification is based on the preset difficulty coefficient table. The relationship between the difficulty coefficient D and the bleeding speed v and the number of bleeding points n is D = a×v+b×n+c, where D is the difficulty coefficient, a, b, c are preset coefficients, v is the bleeding speed, and n is the number of bleeding points. According to different difficulty levels, the bleeding speed, number of bleeding points, vascular elastic modulus, and tube diameter parameters are comprehensively adjusted.

5. The simulation system of the obstetrics and gynecology hemostasis model based on augmented reality according to claim 2, characterized in that: The record evaluation module adopts a multi-index comprehensive evaluation method. The evaluation indicators include operation accuracy A, operation efficiency E, and bleeding control rate C. The comprehensive evaluation score S = w1×A+w2×E+w3×C, where S is the comprehensive evaluation score, w1, w2, and w3 are the weights of each indicator, A is operation accuracy, E is operation efficiency, and C is bleeding control rate; the operation accuracy is calculated by the deviation between the operator's pressing position and the correct position, using the Euclidean distance formula Calculate the deviation value, d is the deviation distance between the pressed position and the correct position, (x1, y1) is the coordinate of the operator's pressed position, and (x2, y2) is the coordinate of the correct position; the operation efficiency is based on the operation time t actual With standard time t standard The ratio is determined The bleeding volume control rate is determined by the actual controlled bleeding volume V controlled The initial bleeding volume V initial The ratio is measured The evaluation results are presented in the form of charts and text reports, including bar charts and line graphs, which intuitively display the scores of each evaluation indicator.

6. The simulation system of the obstetrics and gynecology hemostasis model based on augmented reality according to claim 3, characterized in that: The case library module has case update and retrieval functions, timely updates case data, and retrieves cases based on keywords entered by users; uses text similarity algorithm for case retrieval, and uses formula Calculate the similarity between the keyword and the case text, sim is the similarity between the keyword and the case text, w i1 is the weight of the i-th word in the keyword, w i2 is the weight of the i-th word in the case text.

7. A simulation method of an augmented reality-based gynecological hemostasis model based on the system according to any one of claims 1 to 6, characterized in that: The following steps are involved: Data collection steps: Multi-sensor fusion technology is used to collect physiological data and medical records of obstetrics and gynecology patients in different bleeding scenarios through pressure sensors, flow sensors, photoelectric sensors, and heart rate sensors. For blood pressure data, the oscillometric method is used to measure and the formula is used to calculate the blood pressure. Calculate the average blood pressure value, BP mean is the mean blood pressure, P sys is systolic blood pressure, P dia is the diastolic pressure; the collected data from different sensors are weighted and fused, and the formula Calculate data, X fused is the fused data, X i is the data collected by the i-th sensor, w i is the weight of the ith sensor, and Model construction steps: Based on the collected data, combined with obstetrics and gynecology medical knowledge and experience, the finite element analysis method was used to construct an obstetrics and gynecology hemostasis model with different bleeding characteristics and difficulty levels. The mechanical properties of the blood vessels were simulated by the formula σ=Eε, where σ is stress, E is elastic modulus, and ε is strain. The blood vessels were divided into multiple finite element units, and the model was refined and optimized considering the branching structure, diameter change, and wall thickness difference of the blood vessels. Augmented reality fusion steps: The constructed hemostasis model is integrated with the real operation scene, and the image registration algorithm based on feature matching is used. Calculate the distance between feature points, (x i1 ,y i1 ) and (x i2 ,y i2 ) are the coordinates of the corresponding feature points in the real scene and the virtual model, respectively, and the virtual hemostasis model is accurately presented in the operator's field of vision through an augmented reality device, specifically AR glasses; Interactive operation steps: the operator performs hemostasis operation, and the system monitors the operation in real time through pressure sensors and displacement sensors, and uses the formula F=kx to calculate the pressing force, where F is the pressing force, k is the elastic coefficient, and x is the displacement. Feedback is given according to the requirements of the hemostasis model; the operator's operation trajectory and action sequence are recorded, and feedback information is presented in multiple forms such as visual prompts, auditory prompts and tactile feedback.

8. The simulation method of the obstetrics and gynecology hemostasis model based on augmented reality according to claim 7, characterized in that: Also includes: Difficulty adjustment steps: according to user needs, modify the hemostasis model parameters according to the preset difficulty coefficient table D = a × v + b × n + c, D is the difficulty coefficient, a, b, c are preset coefficients, v is the bleeding speed, and n is the number of bleeding points; when adjusting the difficulty, comprehensively consider the bleeding speed, number of bleeding points, vascular elastic modulus, and tube diameter parameters, and automatically recommend the difficulty level based on the operator's historical training data and evaluation results to achieve personalized training; Record evaluation steps: record operation data and results, and use a multi-index comprehensive evaluation method to evaluate through the formula S = w1 × A + w2 × E + w3 × C, where S is the comprehensive evaluation score, w1, w2, and w3 are the weights of each index, A is the operation accuracy, E is the operation efficiency, and C is the bleeding control rate; the operation accuracy is calculated by the deviation between the operator's pressing position and the correct position, using the Euclidean distance formula Calculate the deviation value, d is the deviation distance between the pressed position and the correct position, (x1, y1) is the coordinate of the operator's pressed position, and (x2, y2) is the coordinate of the correct position; the operation efficiency is based on the operation time t actual With standard time t standard The ratio is determined The bleeding volume control rate is determined by the actual controlled bleeding volume V controlled The initial bleeding volume V initial The ratio is measured The evaluation results are presented in the form of charts and text reports.

9. The simulation method of the obstetrics and gynecology hemostasis model based on augmented reality according to claim 7, characterized in that: Also includes: Case selection step: Select gynecological and obstetric bleeding cases from the case library for simulation training, using the text similarity algorithm Perform case retrieval, Sim is the similarity between the keyword and the case text, w i1 is the weight of the i-th word in the keyword, w i2 is the weight of the i-th word in the case text; Remote collaboration steps: Multiple users conduct remote collaborative training via the Internet, using real-time video streaming technology and data synchronization algorithms. sync =T send -T receive Calculate the synchronization error, T sync is the synchronization error, T send is the sending time, T receive To receive the time, during the training process, operators in different locations share the operation screen and data in real time. The system provides real-time voice communication and text chat functions to facilitate communication and collaboration between operators.

10. The simulation method of the obstetrics and gynecology hemostasis model based on augmented reality according to claim 7, characterized in that: In the data collection step, multi-sensor fusion technology is used to perform weighted fusion on the collected data from different sensors, and the performance of each sensor is monitored and evaluated in real time; All sensors are calibrated before data collection, and the collected physiological data are classified and stored according to different types and time series.

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