Bionic pulmonary valve mold generation method and system and electronic device

Abstract

The present disclosure relates to the technical field of biomedical engineering, and discloses a method and system for generating a bionic pulmonary valve mold and an electronic device. The method comprises: acquiring a time-varying stress signal of a pulmonary valve; preprocessing the time-varying stress signal to obtain a preprocessed time-varying stress signal; obtaining an effective sampling frequency based on the preprocessed time-varying stress signal; obtaining a curved surface coordinate of the bionic pulmonary valve based on the effective sampling frequency; obtaining a macro stress field of the bionic pulmonary valve based on the curved surface coordinate; obtaining a local elastic modulus change rate of the bionic pulmonary valve based on the macro stress field; and determining a target bionic pulmonary valve mold based on an action value function and the local elastic modulus change rate. The present disclosure shortens the development cycle of the mold, improves the manufacturing precision, and improves the applicability of patient mold individualization.

Description

Technical Field

[0001] The present disclosure relates to the field of biomedical engineering technology, and in particular to a method, system, and electronic device for generating a bionic pulmonary valve mold. Background Art

[0002] Tetralogy of Fallot (TEF) is the most common complex congenital heart disease, accounting for approximately 10% of all complex congenital heart diseases. Typically, patients with TEF have a high cure rate and a good prognosis after one-stage surgery. However, some patients still have concurrent pulmonary valve hypoplasia and require pulmonary valvuloplasty. Pulmonary regurgitation is unavoidable in such patients after surgery, often occurring within the short to medium term after surgery and, in severe cases, leading to right heart failure. Studies have shown that postoperative pulmonary regurgitation is an independent risk factor for clinical adverse events such as sudden death and arrhythmias, and is the primary reason for secondary surgery after one-stage radical resection of TEF. Delaying the onset of pulmonary regurgitation after pulmonary valvuloplasty for TEF can significantly improve patients' quality of life.

[0003] Pulmonary valvuloplasty is a key step in the fourth-stage surgical procedure for pulmonary valve hypoplasia. Currently, international efforts have attempted to perform pulmonary valvuloplasty using the Monocusp method or the right ventricular outflow tract flap patch, but neither method has been able to effectively prevent or delay postoperative pulmonary regurgitation. While these traditional surgical methods can achieve good results in some cases, the lack of pulmonary valvuloplasty molds leads to significant variability in valvuloplasty results. Mold manufacturing often faces challenges such as long lead times, high costs, and limited flexibility. This is particularly true for complex, personalized molds, which are difficult to achieve precisely and often fail to fully account for individual patient anatomical and physiological differences. Due to individual anatomical variations, universal molds can result in poor valve fit and inadequate valvular function, making it difficult to achieve the desired anatomical outcome. Specifically, using universal molds for valve repair can lead to poor fit between the valve and the right ventricle, exacerbating symptoms of valvular insufficiency, leading to postoperative sequelae, and even the need for repeat surgery.

[0004] In view of this, the present invention is proposed. Summary of the Invention

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, the embodiments of the present disclosure provide a method, system and electronic equipment for generating a bionic pulmonary valve mold, which shortens the mold development cycle, improves manufacturing accuracy, and improves the applicability of personalized patient molds.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for generating a bionic pulmonary valve mold, the method comprising:

[0007] Acquire the time-varying stress signal of the pulmonary valve;

[0008] pre-process the time-varying stress signal to obtain a pre-processed time-varying stress signal;

[0009] obtain an effective sampling frequency based on the pre-processed time-varying stress signal;

[0010] obtain a curved surface coordinate of the bionic pulmonary valve based on the effective sampling frequency;

[0011] obtain a macro stress field of the bionic pulmonary valve based on the curved surface coordinate;

[0012] obtain a local elastic modulus change rate of the bionic pulmonary valve based on the macro stress field;

[0013] determine a target bionic pulmonary valve mold based on an action value function and the local elastic modulus gradient.

[0014] In a second aspect, the embodiments of the present disclosure further provide a bionic pulmonary valve mold generation system, which comprises:

[0015] a signal acquisition module configured to acquire a time-varying stress signal of a pulmonary valve;

[0016] a pre-processing module configured to pre-process the time-varying stress signal to obtain a pre-processed time-varying stress signal;

[0017] a frequency acquisition module configured to obtain an effective sampling frequency based on the pre-processed time-varying stress signal;

[0018] a coordinate acquisition module configured to obtain a curved surface coordinate of the bionic pulmonary valve based on the effective sampling frequency;

[0019] a stress field acquisition module configured to obtain a macro stress field of the bionic pulmonary valve based on the curved surface coordinate;

[0020] a change rate acquisition module configured to obtain a local elastic modulus change rate of the bionic pulmonary valve based on the macro stress field;

[0021] a mold acquisition module configured to determine a target bionic pulmonary valve mold based on an action value function and the local elastic modulus change rate.

[0022] In a third aspect, the embodiments of the present disclosure further provide an electronic device, which comprises: one or more processors; a storage device configured to store one or more programs; and the one or more programs, when executed by the one or more processors, implement the bionic pulmonary valve mold generation method as described above.

[0023] In a fourth aspect, the embodiments of the present disclosure further provide a machine readable medium storing a computer program, which, when executed by a processor, implements the bionic pulmonary valve mold generation method as described above.

[0024] The embodiments of the present disclosure provide a bionic pulmonary valve mold generation method, system and electronic device, the method comprising: acquiring a time-varying stress signal of a pulmonary valve; preprocessing the time-varying stress signal to obtain a preprocessed time-varying stress signal; obtaining an effective sampling frequency based on the preprocessed time-varying stress signal; obtaining a surface coordinate of the bionic pulmonary valve based on the effective sampling frequency; obtaining a macro stress field of the bionic pulmonary valve based on the surface coordinate; obtaining a local elastic modulus change rate of the bionic pulmonary valve based on the macro stress field; and determining a target bionic pulmonary valve mold based on an action value function and the local elastic modulus change rate. The present disclosure improves data integrity and accuracy by preprocessing the time-varying stress signal; further filters the preprocessed time-varying stress signal through the first formula, avoiding deviations in subsequent analysis due to the presence of noise, and improving data quality; then obtains the dynamic geometric characteristics of the pulmonary valve through the second formula, avoiding model distortion due to boundary problems, so that the entire modeling can more truly reflect the actual geometric characteristics and dynamic behavior of the valve; further optimizes the actual stress state using the third formula and the fourth formula, and finally shortens the development cycle of the mold through the action value function, improves the manufacturing precision, and improves the applicability of patient mold individualization. BRIEF DESCRIPTION OF DRAWINGS

[0025] The above and other features, advantages, and aspects of various embodiments of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings in which:

[0026] Figure 1 A flowchart of a bionic pulmonary valve mold generation method in an embodiment of the present disclosure.

[0027] Figure 2 A structural schematic diagram of a bionic pulmonary valve mold generation system in an embodiment of the present disclosure.

[0028] Figure 3 A structural schematic diagram of an electronic device in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0030] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0031] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes and are not intended to limit the scope of these messages or information.

[0032] In response to the above problems, the embodiments of the present disclosure provide a method for generating a bionic pulmonary valve mold, which shortens the mold development cycle, improves manufacturing accuracy, and enhances the applicability of personalized molds for patients.

[0033] Figure 1 This is a flow chart of a method for generating a bionic pulmonary valve mold in an embodiment of the present disclosure. This method can be executed by a bionic pulmonary valve mold generation system, which can be implemented in software and / or hardware, and can be configured in an electronic device. Figure 1 As shown, the method may specifically include the following steps:

[0034] S110: Acquire a time-varying stress signal of the pulmonary valve.

[0035] Specifically, the time-varying stress signal of the pulmonary valve can be acquired using various technical means or sensors. For example, a mechanical sensor can be used to record the dynamic stress changes of the valve during the cardiac cycle to obtain a time-varying stress signal. Specifically, a micro-mechanical (MEMS) sensor array can be used to directly measure the dynamic stress of the valve, or a fiber Bragg grating (FBG) sensor implanted or attached to the surface of the pulmonary valve can be used to acquire the wavelength, which is then reflected by wavelength shift and converted into a stress signal. In this article, the signal can be an analog signal, a data signal, or any other processable signal type. The data type can be text data, voice data, image data, or any other processable data type.

[0036] S120: Preprocessing the time-varying stress signal to obtain a preprocessed time-varying stress signal.

[0037] The purpose of preprocessing is to extract biomechanical features with high signal-to-noise ratio and eliminate sudden changes in the time-varying stress signals collected by the mechanical sensor due to equipment interference or motion artifacts.

[0038] Specific reference table 1:

[0039] Time (ms) Original stress value Noise / artifact type 0 12.5 Normal 1 13.1 Normal 2 500.0 Motion artifact (sudden change) 3 14.2 Normal 4 12.8 Normal 5 13.5 High frequency noise (jitter)

[0040] In a specific embodiment, preprocessing the time-varying stress signal to obtain a preprocessed time-varying stress signal includes:

[0041] Outlier processing is performed on the time-varying stress signal to obtain a preprocessed time-varying stress signal.

[0042] Specifically, based on the above embodiment, the abnormal value detected at the 2nd ms in Table 1 can be directly eliminated to obtain a preprocessed time-varying stress signal. Alternatively, the abnormal value detected at the 2nd ms in Table 1 can be replaced, for example, using the adjacent points of the abnormal value, that is, the mean value of the previous and next frames (corrected value), as the replacement, where the corrected value = (13.1 + 14.2) / 2 = 13.65 kPa. The replaced time-varying stress signal and the original normal time-varying stress signal are combined as the preprocessed time-varying stress signal.

[0043] S130: Obtaining an effective sampling frequency based on the preprocessed time-varying stress signal.

[0044] For example, the frequency components of the pre-processed time-varying stress signal may vary over time (such as high-frequency transients caused by valve opening and closing during the cardiac cycle). This frequency may lead to oversampling (wasting resources), fixed sampling, or undersampling. This can be dynamically calculated using a formula to adapt it to the instantaneous characteristics of the signal (such as noise level and decay rate), thereby optimizing sampling efficiency. For example, the first formula can be used to pre-process the time-varying stress signal to obtain an effective sampling frequency.

[0045] In a specific embodiment, the preprocessed time-varying stress signal is processed using a first formula to obtain an effective sampling frequency, wherein the expression of the first formula is:

[0046]

[0047] in, represents the effective sampling frequency, represents the time period between any two time points of the preprocessed time-varying stress signals, represents the sampling time, represents the preprocessed time-varying stress signal, represents the baseline noise, represents the signal sensitivity coefficient, represents the time decay factor, represents the time point of preprocessing the time-varying stress signal.

[0048] Specifically, the maximum available sampling rate of the system is limited by the response time of the quantum dot sensor ( <1 / ). The first formula is obtained by Normalize the signal to frequency. The first formula is for the original signal (i.e., the pre-processed time-varying stress signal) Baseline noise removal , linear compression and time domain attenuation Operations such as these are designed to suppress noise and extract the effective components of the signal.

[0049] This embodiment processes the pre-processed time-varying stress signal using the inverse tangent function, which is like filtering a layer of impurities from the data and successfully eliminates baseline noise. This will make the data purer, avoid the deviation of subsequent analysis caused by the presence of noise, and improve the data quality. and time decay factor It reduces the impact of noise, further improves the reliability of the signal, and adds a layer of protection to the signal, so that subsequent research on the signal can more accurately reflect the real situation and provide solid data guarantee for the smooth implementation of subsequent steps.

[0050] S140: Obtaining the surface coordinates of the bionic pulmonary valve based on the effective sampling frequency.

[0051] Furthermore, based on the above embodiments, relevant formulas and parameters can be used to fully consider the influence of various factors such as curvature, spatial attenuation and boundary effects on valve morphology, so that the model can carefully present the surface changes of the valve under different cardiac cycles and other physiological conditions, providing a key basis for subsequent analysis of the relationship between mold shape and stress, and optimization of mold geometry design.

[0052] In a specific embodiment, the effective sampling frequency is processed using a second formula to obtain the surface coordinates of the bionic pulmonary valve, wherein the expression of the second formula is:

[0053]

[0054] in, Represents the surface coordinates of the bionic pulmonary valve, which is used to describe the geometric changes of the valve at different positions and times. Represents the time point of the preprocessed time-varying stress signal, which is used to describe the dynamic state of the valve at different time points. represents the curvature coupling coefficient, Indicates the local position on the surface The slope at represents the cardiac angular frequency, represents the spatial attenuation weight, Represents the local position of the surface, that is, the spatial position or coordinates or position of the valve surface along a specific spatial direction, represents the boundary effect suppression factor.

[0055] The second formula characterizes the dynamic changes of the valve surface, and the parameter and As the coupling coefficient, they represent the influence of curvature and the effect of spatial attenuation respectively. Indicates the angular frequency of the heart, which affects the movement pattern of the valve. It is used to suppress the disturbance caused by the boundary effect. Therefore, the parameter and The effects of curvature and spatial attenuation are reflected respectively, so that the model can carefully depict the morphological changes of the valve under different physiological conditions. This allows the model to capture the complex motion details of the valve as it moves through the cardiac cycle. This helps to more accurately simulate the motion of the valve in a real human environment. This suppresses the disturbance caused by boundary effects, ensuring the stability and accuracy of the model in the boundary area. This avoids model distortion caused by boundary issues, allowing the entire model to more realistically reflect the actual geometric characteristics and dynamic behavior of the valve.

[0056] This embodiment dynamically adjusts the valve's three-dimensional geometric model to simulate its morphological changes during the cardiac cycle and predict its geometric characteristics at different physiological stages. The optimized model adjusts the mold's shape and dimensions, ensuring that the resulting biomimetic valve's geometry better aligns with human anatomy and physiological motion patterns. This ensures its ability to function collaboratively with surrounding tissues and organs after implantation, reducing the risk of complications caused by shape mismatch.

[0057] S150: Based on the surface coordinates, a macroscopic stress field of the bionic pulmonary valve is obtained.

[0058] For example, based on the above embodiment, the The bond potential energy of a molecule , No. The local strain rate of each molecule and strain rate threshold At the same time, the additional scale coupling coefficient λ and the micro stress critical value The applicability of the model is further enhanced. Thus, the stress transmission at the molecular level is linked to the macroscopic stress field, building a bridge of communication between the microscopic world and the macroscopic world, making it possible to understand the generation and distribution of macroscopic stress from the microscopic molecular level, and comprehensively considering the first The bond potential energy of a molecule , No. The local strain rate of each molecule and strain rate threshold The effective stress can be calculated accurately by taking into account factors such as the stress distribution and the influence of the load.

[0059] In a specific embodiment, the surface coordinates are processed using a third formula to obtain the macroscopic stress field of the bionic pulmonary valve, wherein the expression of the third formula is:

[0060]

[0061] in, represents the macroscopic stress field of the bionic pulmonary valve, express The maximum value of Indicates the The rate of change of the bond potential energy of each molecule, Indicates the The bond potential energy of a molecule, represents the local strain rate, Represents the local position of the surface, that is, the spatial position or coordinates or position of the valve surface along a specific spatial direction, Indicates the The local strain rate of each molecule, represents the strain rate threshold, represents the scale coupling coefficient, Represents the critical value of micro stress.

[0062] This embodiment uses these laws to guide the selection of mold materials and structural design, ensuring that the performance of the material at the microscopic level can meet the macroscopic mechanical requirements, improving the durability and reliability of the bionic valve, and ensuring that it can withstand complex physiological stress environments without damage or functional degradation during long-term use.

[0063] S160: Based on the macroscopic stress field, obtain a local elastic modulus change rate of the bionic pulmonary valve.

[0064] Specifically, light intensity distribution can be used to intelligently control the local elastic modulus, achieving dynamic spatial and temporal changes in mold material properties. In practical applications of bionic pulmonary valve molding, the elastic modulus of the mold material can be precisely adjusted by controlling the light intensity distribution according to the mechanical requirements of different regions of the pulmonary valve. This allows for an optimal balance between flexibility and strength in each component of the manufactured valve, better simulating the mechanical response characteristics of a real pulmonary valve and enhancing its opening and closing function and blood compatibility in the blood circulation.

[0065] In a specific embodiment, the macroscopic stress field is processed using a fourth formula to obtain the local elastic modulus change rate of the bionic pulmonary valve, wherein the expression of the fourth formula is:

[0066]

[0067] in, represents the rate of change of the local elastic modulus of the bionic pulmonary valve, represents the energy diffusion coefficient, represents the gradient symbol, represents the local elastic modulus, represents the photoresponse gain, represents the nonlinear saturation factor, represents the light intensity distribution, Indicates the characteristic length of the mold.

[0068] The embodiments of the present disclosure describe the relationship between the local elastic modulus and the light intensity distribution through partial differential equations, and can accurately control the spatial and temporal distribution of the elastic modulus and other properties of the mold material according to different lighting conditions, so that the mechanical properties of the mold better fit the actual pulmonary valve under different physiological scenarios, thereby achieving the effect of optimizing the mold performance. Based on light intensity distribution The dynamic changes of the material can realize the intelligent control of the material properties, so that it can automatically adjust its elastic modulus according to different lighting conditions to meet different usage requirements and mechanical environments. Indicates the energy transfer efficiency in the material. By accurately describing and utilizing it, we can ensure the efficient transfer and distribution of energy in the material, so that the material can better play its due function and improve the efficiency and effect of the material. and nonlinear saturation factor The nonlinear characteristics involving light response can accurately describe the nonlinear response behavior of materials under different light intensities, providing more possibilities for the optimized design of pulmonary valve forming molds.

[0069] S170: Determine a target bionic pulmonary valve mold based on the action value function and the local elastic modulus change rate.

[0070] Specifically, the action value function and the comprehensive consideration of state and action can be adopted, combined with information such as the objective function gradient. It is possible to adjust the optimization direction and strategy in real time according to the feedback during the optimization process, ensuring that the final mold optimization solution reaches the optimal state while meeting the requirements of multiple performance indicators, thereby further improving the performance and applicability of the mold.

[0071] In a specific embodiment, based on the action value function and the local elastic modulus change rate, determining the target bionic pulmonary valve mold includes: based on the action value function and the local elastic modulus change rate, determining the first The action value at the iteration;

[0072] When The action value at the iteration When the difference of action values ​​in the iteration is the smallest, the mold parameters are determined;

[0073] Based on the mold parameters, a target bionic pulmonary valve mold is determined.

[0074] Among them, the expression of the action value function is:

[0075]

[0076] in, Indicates the The action value at the iteration, Indicates the The action value at the iteration, represents the stress state vector, represents the action space vector, represents the gradient of the objective function, represents the learning rate, Indicates instant reward, the value is 1, 0 or -1, represents the maximum expected value at the next moment, represents the stress state vector at the next moment, Represents the action space vector at the next moment.

[0077] in, =1 indicates that after executing an action, the system performs well and meets or approaches the desired target, such as when the valve morphology and stress state reach ideal values. This represents "reward" or "positive feedback."

[0078] =0 means: when the action is neither obviously good nor obviously bad, there is no special good or bad performance, such as the system state is stable, there is no significant improvement or deterioration, which is a neutral or no feedback situation.

[0079] =-1 means: after executing a certain action, the system performs poorly and deviates from the target, such as abnormal valve stress or large morphological deviation, and is subject to "punishment" or "negative feedback".

[0080] For example, the difference between the action value at the k+1th iteration and the action value at the kth iteration is determined by the action value function and the local elastic modulus change rate, and the difference is continuously adjusted and optimized until it is minimized to obtain the mold parameters (i.e., the stress state vector and the action space vector ).

[0081] In this embodiment, the stress state vector and the action space vector The optimization can be achieved by the gradient of the objective function To guide the system, a boundary effect suppression factor (i.e., discount factor) γ is combined to balance long-term and short-term benefits. This avoids optimization bias caused by either pursuing immediate benefits or over-focusing on long-term goals. This enables the system to consider both short-term benefits and long-term development, ensuring the comprehensiveness and sustainability of optimization results.

[0082] In summary, the embodiments of the present disclosure provide a method, system and electronic device for generating a bionic pulmonary valve mold, the method comprising: acquiring a time-varying stress signal of the pulmonary valve; preprocessing the time-varying stress signal to obtain a preprocessed time-varying stress signal; obtaining an effective sampling frequency based on the preprocessed time-varying stress signal; obtaining the surface coordinates of the bionic pulmonary valve based on the effective sampling frequency; obtaining a macroscopic stress field of the bionic pulmonary valve based on the surface coordinates; obtaining a local elastic modulus change rate of the bionic pulmonary valve based on the macroscopic stress field; and determining a target bionic pulmonary valve mold based on the action value function and the local elastic modulus gradient change rate. The present disclosure improves data integrity and accuracy by preprocessing the time-varying stress signal; further, the first formula filters the preprocessed time-varying stress signal to avoid deviations in subsequent analysis due to the presence of noise, thereby improving data quality; then, the dynamic geometric characteristics of the pulmonary valve are obtained through the second formula, avoiding model distortion caused by boundary problems, so that the entire modeling can more realistically reflect the actual geometric characteristics and dynamic behavior of the valve; the third and fourth formulas are used to further optimize the actual stress state, and finally, the action value function is used to shorten the mold development cycle, improve manufacturing accuracy, and improve the personalized applicability of the mold to patients.

[0083] Figure 2FIG. 1 is a schematic diagram of the structure of the bionic pulmonary valve mold generation system in the embodiment of the present disclosure. Figure 2 As shown, the system includes a signal acquisition module 210 , a pre-processing module 220 , a frequency acquisition module 230 , a coordinate acquisition module 240 , a stress field acquisition module 250 , a change rate acquisition module 260 and a mold acquisition module 270 .

[0084] The signal acquisition module 210 is used to acquire the time-varying stress signal of the pulmonary valve.

[0085] The preprocessing module 220 is used to preprocess the time-varying stress signal to obtain a preprocessed time-varying stress signal.

[0086] The frequency acquisition module 230 is configured to obtain an effective sampling frequency based on the preprocessed time-varying stress signal.

[0087] The coordinate acquisition module 240 is configured to obtain the surface coordinates of the bionic pulmonary valve based on the effective sampling frequency.

[0088] The stress field acquisition module 250 is used to obtain the macroscopic stress field of the bionic pulmonary valve based on the surface coordinates.

[0089] The change rate acquisition module 260 is used to obtain the change rate of the local elastic modulus of the bionic pulmonary valve based on the macroscopic stress field.

[0090] The mold acquisition module 270 is used to determine the target bionic pulmonary valve mold based on the action value function and the local elastic modulus gradient.

[0091] In an optional embodiment, the preprocessing module 220 is further configured to perform outlier processing on the time-varying stress signal to obtain a preprocessed time-varying stress signal.

[0092] In an optional embodiment, the frequency acquisition module 230 is further configured to process the preprocessed time-varying stress signal using a first formula to obtain an effective sampling frequency, wherein the first formula is expressed as:

[0093]

[0094] in, represents the effective sampling frequency, represents the time period between any two time points of the preprocessed time-varying stress signals, represents the sampling time, represents the preprocessed time-varying stress signal, represents the baseline noise, represents the signal sensitivity coefficient, represents the time decay factor, represents the time point of preprocessing the time-varying stress signal.

[0095] In an optional embodiment, the coordinate acquisition module 240 is further configured to process the effective sampling frequency using a second formula to obtain the surface coordinates of the bionic pulmonary valve, wherein the expression of the second formula is:

[0096]

[0097] in, Represents the surface coordinates of the bionic pulmonary valve, which is used to describe the geometric changes of the valve at different positions and times. Represents the time point of the preprocessed time-varying stress signal, which is used to describe the dynamic state of the valve at different time points. represents the curvature coupling coefficient, Indicates the local position on the surface The slope at represents the cardiac angular frequency, represents the spatial attenuation weight, Represents the local position of the surface, that is, the spatial position or coordinates or position of the valve surface along a specific spatial direction, represents the boundary effect suppression factor.

[0098] In an optional embodiment, the stress field acquisition module 250 is further configured to process the surface coordinates using a third formula to obtain the macroscopic stress field of the bionic pulmonary valve, wherein the third formula is expressed as:

[0099]

[0100] in, represents the macroscopic stress field of the bionic pulmonary valve, express The maximum value of Indicates the The rate of change of the bond potential energy of each molecule, Indicates the The bond potential energy of a molecule, represents the local strain rate, Represents the local position of the surface, that is, the spatial position or coordinates or position of the valve surface along a specific spatial direction, Indicates the The local strain rate of each molecule, represents the strain rate threshold, represents the scale coupling coefficient, Represents the critical value of micro stress.

[0101] In an optional embodiment, the change rate acquisition module 260 is further configured to process the macroscopic stress field using a fourth formula to obtain a change rate of the local elastic modulus of the bionic pulmonary valve, wherein the fourth formula is expressed as:

[0102]

[0103] in, represents the rate of change of the local elastic modulus of the bionic pulmonary valve, represents the energy diffusion coefficient, represents the gradient symbol, represents the local elastic modulus, represents the photoresponse gain, represents the nonlinear saturation factor, represents the light intensity distribution, Indicates the characteristic length of the mold.

[0104] In an optional embodiment, the mold acquisition module 270 is further configured to determine the first The action value at the iteration;

[0105] When The action value at the iteration When the difference of action values ​​in the iteration is the smallest, the mold parameters are determined;

[0106] Based on the mold parameters, a target bionic pulmonary valve mold is determined.

[0107] Among them, the expression of the action value function is:

[0108]

[0109] in, Indicates the The action value at the iteration, Indicates the The action value at the iteration, represents the stress state vector, represents the action space vector, represents the objective function gradient, represents the learning rate, Indicates instant reward, the value is 1, 0 or -1, represents the maximum expected value at the next moment, represents the stress state vector at the next moment, Represents the action space vector at the next moment.

[0110] in, =1 indicates that after executing an action, the system performs well and meets or approaches the desired target, such as when the valve morphology and stress state reach ideal values. This represents "reward" or "positive feedback."

[0111] =0 means: when the action is neither obviously good nor obviously bad, there is no special good or bad performance, such as the system state is stable, there is no significant improvement or deterioration, which is a neutral or no feedback situation.

[0112] =-1 means: after executing a certain action, the system performs poorly and deviates from the target, such as abnormal valve stress or large morphological deviation, and is subject to "punishment" or "negative feedback".

[0113] An embodiment of the present disclosure provides a bionic pulmonary valve mold generation system, which can execute the steps of a bionic pulmonary valve mold generation method provided in a method embodiment of the present disclosure. The execution steps and beneficial effects are not repeated here.

[0114] Figure 3 This is a schematic diagram of the structure of an electronic device in an embodiment of the present disclosure. Figure 3 , which shows a structural diagram of an electronic device 500 suitable for implementing the embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0115] like Figure 3 As shown, electronic device 500 may include a processing device 501, ROM 502, RAM 503, a bus 504, an input / output (I / O) interface 505, an input device 506, an output device 507, a storage device 508, and a communication device 509. The processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501 can perform various appropriate actions and processes according to a program stored in the read-only memory (ROM) 502 or a program loaded from the storage device 508 into the random access memory (RAM) 503 to implement a method for generating a bionic pulmonary valve mold according to an embodiment of the present disclosure. RAM 503 also stores various programs and data required for the operation of electronic device 500. The processing device 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0116] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart, thereby implementing a bionic pulmonary valve mold generation method as described above. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0117] It should be noted that the computer-readable medium described above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wire, optical cable, RF (radio frequency), or any suitable combination thereof.

[0118] The computer-readable medium may be included in the electronic device, or may exist independently and not be incorporated into the electronic device. The computer-readable medium carries one or more programs. When executed by the electronic device, the electronic device: obtains a time-varying stress signal of the pulmonary valve; preprocesses the time-varying stress signal to obtain a preprocessed time-varying stress signal; obtains an effective sampling frequency based on the preprocessed time-varying stress signal; obtains the surface coordinates of the bionic pulmonary valve based on the effective sampling frequency; obtains a macroscopic stress field of the bionic pulmonary valve based on the surface coordinates; obtains a local elastic modulus change rate of the bionic pulmonary valve based on the macroscopic stress field; and determines a target bionic pulmonary valve mold based on the action value function and the local elastic modulus gradient.

[0119] Optionally, when the above one or more programs are executed by the electronic device, the electronic device may also execute other steps described in the above embodiments.

[0120] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0121] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the present disclosure is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the scope of the above disclosure. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

Claims

1. A method for generating a bionic pulmonary valve mold, characterized in that: The bionic pulmonary valve mold generation method comprises: Acquire the time-varying stress signal of the pulmonary valve; Preprocessing the time-varying stress signal to obtain a preprocessed time-varying stress signal; Obtaining an effective sampling frequency based on the preprocessed time-varying stress signal; Based on the effective sampling frequency, obtaining the surface coordinates of the bionic pulmonary valve; Based on the surface coordinates, a macroscopic stress field of the bionic pulmonary valve is obtained; Based on the macroscopic stress field, a local elastic modulus change rate of the bionic pulmonary valve is obtained; Based on the action value function and the local elastic modulus change rate, a target bionic pulmonary valve mold is determined. Among them, the expression of the action value function is: ； in, Indicates the The action value at the iteration, Indicates the The action value at the iteration, represents the stress state vector, represents the action space vector, represents the gradient of the objective function, represents the learning rate, Indicates instant reward, the value is 1, 0 or -1, represents the maximum expected value at the next moment, represents the stress state vector at the next moment, represents the action space vector at the next moment, represents the rate of change of the local elastic modulus of the bionic pulmonary valve, represents the boundary effect suppression factor.

2. The method for generating a bionic pulmonary valve mold according to claim 1, characterized in that: Preprocessing the time-varying stress signal to obtain a preprocessed time-varying stress signal includes: Outlier processing is performed on the time-varying stress signal to obtain a preprocessed time-varying stress signal.

3. The method for generating a bionic pulmonary valve mold according to claim 2, characterized in that: Based on the preprocessed time-varying stress signal, an effective sampling frequency is obtained, including: The preprocessed time-varying stress signal is processed using a first formula to obtain an effective sampling frequency, wherein the expression of the first formula is: ； in, represents the effective sampling frequency, represents the time period between any two time points of the preprocessed time-varying stress signals, represents the sampling time, represents the preprocessed time-varying stress signal, represents the baseline noise, represents the signal sensitivity coefficient, represents the time decay factor, represents the time point of preprocessing the time-varying stress signal.

4. The method for generating a bionic pulmonary valve mold according to claim 3, characterized in that: Based on the effective sampling frequency, the surface coordinates of the bionic pulmonary valve are obtained, including: The effective sampling frequency is processed using a second formula to obtain the surface coordinates of the bionic pulmonary valve, wherein the expression of the second formula is: ； in, Represents the surface coordinates of the bionic pulmonary valve, which is used to describe the geometric changes of the valve at different positions and times. Represents the time point of the preprocessed time-varying stress signal, which is used to describe the dynamic state of the valve at different time points. represents the curvature coupling coefficient, Indicates the local position on the surface The slope at represents the cardiac angular frequency, represents the spatial attenuation weight, Represents the local position of the surface, that is, the spatial position or coordinates or position of the valve surface along a specific spatial direction, represents the boundary effect suppression factor.

5. The method for generating a bionic pulmonary valve mold according to claim 4, characterized in that: Based on the surface coordinates, the macroscopic stress field of the bionic pulmonary valve is obtained, including: The surface coordinates are processed using a third formula to obtain the macroscopic stress field of the bionic pulmonary valve, wherein the expression of the third formula is: ； in, represents the macroscopic stress field of the bionic pulmonary valve, express The maximum value of Indicates the The rate of change of the bond potential energy of each molecule, Indicates the The bond potential energy of a molecule, represents the local strain rate, Represents the local position of the surface, that is, the spatial position or coordinates or position of the valve surface along a specific spatial direction, Indicates the The local strain rate of each molecule, represents the strain rate threshold, represents the scale coupling coefficient, Represents the critical value of micro stress.

6. The method for generating a bionic pulmonary valve mold according to claim 5, characterized in that: Based on the macroscopic stress field, the local elastic modulus change rate of the bionic pulmonary valve is obtained, including: The macroscopic stress field is processed using the fourth formula to obtain the local elastic modulus change rate of the bionic pulmonary valve, wherein the expression of the fourth formula is: ； in, represents the rate of change of the local elastic modulus of the bionic pulmonary valve, represents the energy diffusion coefficient, represents the gradient symbol, represents the local elastic modulus, represents the photoresponse gain, represents the nonlinear saturation factor, represents the light intensity distribution, Indicates the characteristic length of the mold.

7. The method for generating a bionic pulmonary valve mold according to claim 1, characterized in that: Determining a target bionic pulmonary valve mold based on the action value function and the local elastic modulus change rate includes: Based on the action value function and the local elastic modulus change rate, determine the The action value at the iteration; When The action value at the iteration When the difference of action values ​​in the iteration is the smallest, the mold parameters are determined; Based on the mold parameters, a target bionic pulmonary valve mold is determined.

8. A bionic pulmonary valve mold generation system, characterized in that: The bionic pulmonary valve mold generation system includes: A signal acquisition module, used for acquiring a time-varying stress signal of the pulmonary valve; a preprocessing module, configured to preprocess the time-varying stress signal to obtain a preprocessed time-varying stress signal; A frequency acquisition module, configured to obtain an effective sampling frequency based on the preprocessed time-varying stress signal; A coordinate acquisition module, configured to obtain the surface coordinates of the bionic pulmonary valve based on the effective sampling frequency; A stress field acquisition module, configured to obtain a macroscopic stress field of the bionic pulmonary valve based on the surface coordinates; a change rate acquisition module, configured to obtain a change rate of the local elastic modulus of the bionic pulmonary valve based on the macroscopic stress field; A mold acquisition module is used to determine a target bionic pulmonary valve mold based on the action value function and the local elastic modulus change rate. Among them, the expression of the action value function is: ； in, Indicates the The action value at the iteration, Indicates the The action value at the iteration, represents the stress state vector, represents the action space vector, represents the gradient of the objective function, represents the learning rate, Indicates instant reward, the value is 1, 0 or -1, represents the maximum expected value at the next moment, represents the stress state vector at the next moment, represents the action space vector at the next moment, represents the rate of change of the local elastic modulus of the bionic pulmonary valve, represents the boundary effect suppression factor.

9. An electronic device comprising: one or more processors; a storage device for storing one or more programs, Wherein, when the one or more programs are executed by the one or more processors, the method for generating a bionic pulmonary valve mold according to any one of claims 1-7 is implemented.

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