Bubble detection and positioning method in long sheath tube based on combined acoustic and optical detection

Abstract

A method for detecting and positioning bubbles in a long sheath tube based on combined acoustic and optical detection includes the following steps: Step 1: System model construction: First, a mathematical model of the acoustic field and light field in the lumen is established, and the disturbance effect of bubbles on the propagation of acoustic and light is analyzed to provide a theoretical basis for bubble detection; Step 2: Optimizing the configuration of the combined acoustic and optical detection unit; Step 3: Algorithmic identification of bubble acoustic characteristics, using Fourier transform to extract the time-frequency characteristics of the bubble, and then obtaining the size and speed of the bubble through support vector machine analysis; Step 4: Simulation and detection of bubble optical scattering; Step 5: Construction of an acoustic and optical multi-source data fusion positioning framework, obtaining the optimal data structure through correlation analysis, and obtaining the precise three-dimensional coordinates of the bubble through iterative calculation; Step 6: Stereoscopic display of bubble movement and alarm to improve surgical safety; Step 7: Bubble digital twin modeling, establishing a three-dimensional mathematical model of the lumen and bubbles for bubble detection system optimization.

Description

Technical Field

[0001] The invention relates to the technical field of bubble detection and positioning in a long sheath tube, and more specifically to a bubble detection and positioning method in a long sheath tube based on combined acoustic and optical detection. Background Art

[0002] In the field of interventional surgery, the use of long sheaths is common and critical. They are used to guide treatment tools to specific locations inside the human body. During these surgeries, monitoring the internal conditions of the long sheaths is crucial, especially the detection of bubbles, because the presence of bubbles may cause serious complications and even endanger the patient's life. Traditionally, the detection of bubbles mainly relies on acoustic methods, such as ultrasonic technology, or optical methods, such as endoscopy.

[0003] In recent years, with the development of technology, these methods have significantly improved in sensitivity and precision. For example, improved ultrasound technology can more clearly display the internal structure of blood vessels, while high-resolution endoscopes provide more detailed visual information. In addition, some emerging technologies, such as computer-based image processing and data analysis, have also begun to be applied in this field.

[0004] Although existing technologies have made progress in some aspects, they still have some significant drawbacks:

[0005] Limitations of a single data source: Traditional methods often rely on a single acoustic or optical data source, which limits the comprehensiveness and accuracy of detection. For example, ultrasound may not clearly show all the details of the bubble, while an endoscope may miss some bubbles that are out of the field of view.

[0006] Insufficient real-time monitoring capabilities: Existing technologies have limitations in real-time monitoring, especially in dynamic and complex surgical environments. This may lead to untimely detection and response to bubbles, increasing surgical risks.

[0007] Complexity in data interpretation: Even though existing technologies can provide detailed data, it is challenging for physicians and technicians to interpret this data, especially in tense and complex surgical environments.

[0008] In view of these drawbacks of the prior art, the necessity of the present invention is obvious. Summary of the invention

[0009] 1. Technical problems to be solved

[0010] In view of the problems existing in the prior art, the purpose of the present invention is to provide a method for detecting and positioning bubbles in long sheaths based on combined acoustic and optical detection, and to provide a more comprehensive and accurate bubble detection method by fusing acoustic and optical data; this multi-source data fusion not only improves the accuracy of detection, but also enhances the real-time monitoring capability of the system; in addition, the present invention also includes a three-dimensional dynamic display and an intelligent alarm system, which enable doctors to understand and respond to situations during surgery more intuitively and quickly, thereby significantly improving the safety and efficiency of the surgery. In short, the present invention overcomes many drawbacks of the prior art through innovative technical methods, and provides a more advanced and reliable solution for bubble detection in interventional surgery.

[0011] 2. Technical solution

[0012] To solve the above problems, the present invention adopts the following technical solutions:

[0013] The method for detecting and locating bubbles in a long sheath tube based on combined acoustic and optical detection is characterized by comprising the following steps:

[0014] Step 1: System model construction

[0015] Firstly, a mathematical model of the acoustic and optical fields in the tube cavity is established to analyze the disturbance effect of bubbles on the propagation of sound and light, thus providing a theoretical basis for bubble detection.

[0016] Step 2: Optimize the configuration of the acoustic and optical detection unit

[0017] By spirally distributing the acoustic and optical combination sensors around the tube, the three-dimensional coverage is improved to avoid detection blind spots.

[0018] Step 3: Algorithmic identification of bubble acoustic characteristics

[0019] The time-frequency characteristics of the bubbles are extracted using Fourier transform, and the size and speed of the bubbles are obtained through support vector machine analysis.

[0020] Step 4: Bubble optical scattering simulation and detection

[0021] Monte Carlo method is used to obtain the scattering characteristics of bubbles, and phase detection is used to determine the movement trajectory of bubbles.

[0022] Step 5: Construction of the acoustic and optical multi-source data fusion positioning framework

[0023] The optimal data structure is obtained through correlation analysis, and the precise three-dimensional coordinates of the bubbles are obtained through iterative calculation;

[0024] Step 6: 3D display of bubble movement and alarm

[0025] Dynamically display the bubble motion trajectory model and issue intelligent alarms based on the volume and speed of the bubbles to improve surgical safety;

[0026] Step 7: Bubble Digital Twin Modeling

[0027] A three-dimensional mathematical model of the lumen and bubbles was established for the optimization of the bubble detection system.

[0028] The step 1 uses formulas (1.1) and (1.2) to establish a mathematical model of the propagation of the sound field and the light field in the long sheath tube. The specific formula is as follows:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] In formula (1.1), represents the pressure field of the sound wave, c is the speed of sound, is the sound source term, and t represent spatial position and time, respectively;

[0035] In formula (1.2), and are the electric field and the magnetic field, and are the electric displacement field and magnetic field respectively, ρ is the charge density, is the current density;

[0036] First, according to the physical properties of the long sheath, such as materials, dimensions and technical specifications of the acoustic and optical sensors, the model parameters, such as the speed of sound c and the propagation speed of electromagnetic waves, are initialized; then, according to the acoustic wave equation and Maxwell's equations, a mathematical model of the propagation of sound and light waves in the long sheath is established; finally, numerical methods, such as finite element analysis, are used to simulate the propagation of sound and light waves to predict the propagation characteristics under specific conditions. Through this step, a solid theoretical foundation has been laid for the acoustic and optical combined detection technology, providing a key prerequisite for subsequent bubble detection and positioning.

[0037] In step 2, formula (2) is used to optimize the layout of the acoustic-optical combination sensor to achieve all-round detection without blind spots in the long sheath tube. The specific formula is as follows:

[0038]

[0039] in, represents the position vector of the sensor, is the distance function between sensors i and j, w ij is the weight coefficient, which indicates the relative importance between sensors i and j. First, according to the output of step 1 (the propagation characteristics of sound waves and light waves), the parameters of the sensor network are initialized. Then, an optimization algorithm (such as genetic algorithm and particle swarm optimization) is used to determine the optimal location of the sensor to maximize the coverage and minimize the dead spots. Finally, the optimized sensor layout is verified in a computer simulation environment to ensure its effectiveness in practical applications.

[0040] Through the implementation of step 2, the effectiveness and accuracy of the combined acoustic and optical detection system inside the long sheath can be ensured, providing a solid foundation for subsequent bubble detection and positioning. The successful implementation of this step is crucial to the performance of the entire system.

[0041] The step 3 sequentially uses formulas (3.1), (3.2) and (3.3) to collect data from the optimized layout of the acoustic-optical combination sensor to identify and analyze the acoustic characteristics of the bubble in the long sheath tube. The specific process is as follows:

[0042] 4.1 Acoustic feature extraction: Fourier transform is used to convert acoustic wave signals from time domain to frequency domain:

[0043]

[0044] Among them, S(f) is the frequency domain signal, s(t) is the time domain signal, represents Fourier transform;

[0045] 4.2 Feature vector construction: Extract feature vector from frequency domain signal:

[0046]

[0047] Among them, v i It is the feature quantity extracted from the frequency domain signal;

[0048] 4.3 SVM classification: Use SVM to classify the feature vector to identify the size and speed of the bubble:

[0049]

[0050] Among them, y is the classification result;

[0051] First, the acoustic wave signal is collected from the sensor configured in step 2; then the signal is preprocessed, including filtering and denoising, to improve the signal quality; the preprocessed acoustic wave signal is Fourier transformed to extract the frequency domain features, and then the feature vector is constructed based on the extracted frequency domain features. Finally, the feature vector is classified using SVM to identify the characteristics of the bubble.

[0052] Through the implementation of step 3, the acoustic characteristics of bubbles in the long sheath can be accurately identified and analyzed, providing key information for subsequent bubble positioning and processing. The successful implementation of this step is crucial to the performance and accuracy of the entire system.

[0053] The step 4 adopts formula (4) to simulate and detect the optical scattering characteristics of bubbles in the long sheath tube by using the optical data collected from the optimized layout of the acoustic-optical combination sensor. The specific formula is as follows:

[0054]

[0055] Among them, I(θ,φ) is the scattered light intensity in the direction (θ,φ), I0 is the incident light intensity, f(θ′,φ′) is the scattering amplitude function, k is the wave number, r is the distance from the observation point to the scattering center, ω is the angular frequency, θ′ and φ′ are the scattering angle and azimuth, respectively. First, collect optical data from the sensor configured in step 2, then use the Monte Carlo method to simulate the scattering process of light waves on the bubble surface, then calculate the intensity distribution of scattered light according to the scattering model, and finally analyze the scattered light intensity data to extract the position and motion characteristics of the bubble.

[0056] By implementing step 4, the optical scattering characteristics of the bubble in the long sheath can be accurately simulated and analyzed, providing key information for the precise positioning of the bubble. The successful implementation of this step is critical to the performance and accuracy of the entire system.

[0057] The step 5 adopts formula (5) and combines the acoustic and optical data collected in steps 3 and 4 to construct a multi-source data fusion framework for achieving accurate three-dimensional positioning of the bubble. The specific formula is as follows:

[0058]

[0059] Among them, among them, is the estimated bubble position at time t, K t is the Kalman gain, z t is the observation value, H is the observation model matrix. First, the acoustic and optical data in steps 3 and 4 are collected, and necessary preprocessing, such as filtering and denoising, is performed. The acoustic and optical data are fused using a Bayesian filter to improve the accuracy of bubble positioning. The position estimate of the bubble is then updated based on the fused data. The position estimate is continuously updated iteratively until the predetermined accuracy requirement is met.

[0060] Through the implementation of step 5, the acoustic and optical data can be effectively combined to achieve accurate three-dimensional positioning of the bubble in the long sheath. The successful implementation of this step is critical to the performance of the entire system, especially in terms of improving positioning accuracy and reliability.

[0061] The step 6 adopts formula (6) and utilizes the bubble position data obtained by fusion in step 5 to realize the three-dimensional display of bubble movement and realize intelligent alarm according to the characteristics and behavior of the bubble. The specific formula is as follows:

[0062]

[0063] Where m is the mass of the bubble, is the position vector of the bubble, is the potential energy of the bubble, is resistance, It is buoyancy. First, receive the bubble position and motion data obtained by fusion in step 5; then build a three-dimensional dynamic display model based on the bubble position and motion data, and then dynamically display the bubble's motion trajectory in three-dimensional space. Finally, analyze the bubble's behavior and implement intelligent alarm according to preset rules such as bubble size and speed threshold.

[0064] Through the implementation of step 6, the movement state of the bubble can be intuitively displayed, and an alarm can be issued in time when the behavior of the bubble is abnormal, thereby improving the safety and efficiency of the operation. The successful implementation of this step is crucial to the user-friendliness and practicality of the entire system.

[0065] In step 7, formula (7) is used to establish a digital twin model of a bubble, which can simulate the behavior and characteristics of the bubble in a virtual environment for further optimization and prediction of system performance. The specific formula is as follows:

[0066]

[0067] Among them, among them, is the fluid velocity field, ρ is the fluid density, p is the pressure, v is the dynamic viscosity coefficient, is the acceleration of gravity; first, based on the principles of fluid dynamics, a mathematical model of bubble motion is constructed, and then the model parameters are adjusted according to the actual observation data to ensure the accuracy of the model. Next, the motion of the bubbles is simulated in a virtual environment and its behavior is analyzed. Finally, the system performance is evaluated based on the simulation results, and optimization suggestions are put forward.

[0068] Through the implementation of step 7, the behavior of bubbles can be accurately simulated in a virtual environment, providing a powerful tool for system optimization and prediction. The successful implementation of this step is crucial to improving the performance and reliability of the entire system.

[0069] 3. Beneficial effects

[0070] Compared with the prior art, the advantages of the present invention are:

[0071] 1. Improve positioning accuracy: By fusing acoustic and optical data, the present invention significantly improves the three-dimensional positioning accuracy of bubbles. Acoustic data provides preliminary information on the location and size of bubbles, while optical data provides supplementary information on the shape and precise location of bubbles. This multi-source data fusion method utilizes the complementarity of the two types of data, thereby achieving higher positioning accuracy than a single data source.

[0072] 2. Real-time monitoring and dynamic display: The present invention can monitor the movement of bubbles in real time and dynamically display their trajectories through 3D visualization technology. This not only helps doctors understand the behavior of bubbles more intuitively, but also can timely discover potential risks, thereby improving the safety of surgery.

[0073] 3. Intelligent alarm system: Combining machine learning and pattern recognition technology, the present invention can realize intelligent alarm according to the behavior and characteristics of bubbles. When the size, speed or movement trajectory of bubbles exceeds the safe range, the system will automatically sound an alarm, thereby helping doctors take timely measures to avoid potential dangers.

[0074] 4. Application of digital twin model: By establishing a digital twin model of bubbles, the present invention can simulate the behavior of bubbles in a virtual environment, providing a powerful tool for system optimization and prediction. This model can help doctors and researchers better understand the behavior of bubbles under different conditions, thereby optimizing surgical strategies and equipment design.

[0075] 5. Continuous optimization of system performance: Through iterative updates and model calibration, the present invention is able to continuously optimize its performance. As more data is collected and analyzed, the system will become more accurate and reliable, thus providing continuous improvement and optimization in the long term.

[0076] In summary, the present invention achieves precise positioning and real-time monitoring of bubbles through a highly integrated and innovative approach, significantly improving the safety and efficiency of interventional surgery. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 The present invention is a flow chart of a method for detecting and locating bubbles in a long sheath tube based on combined acoustic and optical detection. DETAILED DESCRIPTION

[0078] The following will combine 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 the embodiments. All other embodiments obtained by ordinary technicians in this field without creative work based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0079] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "top / bottom" indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0080] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "mounted / connected", and "connected" should be understood in a broad sense. For example, "connected" means fixed connection, detachable connection, or integral connection; mechanical connection, electrical connection; direct connection, indirect connection through an intermediate medium, and internal communication between two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention are understood according to specific circumstances.

[0081] Example 1

[0082] Please refer to Figure 1 The method for detecting and locating bubbles in a long sheath tube based on combined acoustic and optical detection comprises the following steps:

[0083] The method for detecting and locating bubbles in a long sheath tube based on combined acoustic and optical detection is characterized by comprising the following steps:

[0084] Step 1: System model construction

[0085] Firstly, a mathematical model of the acoustic and optical fields in the tube cavity is established to analyze the disturbance effect of bubbles on the propagation of sound and light, thus providing a theoretical basis for bubble detection.

[0086] Step 2: Optimize the configuration of the acoustic and optical detection unit

[0087] By spirally distributing the acoustic and optical combination sensors around the tube, the three-dimensional coverage is improved to avoid detection blind spots.

[0088] Step 3: Algorithmic identification of bubble acoustic characteristics

[0089] The time-frequency characteristics of the bubbles are extracted using Fourier transform, and the size and speed of the bubbles are obtained through support vector machine analysis.

[0090] Step 4: Bubble optical scattering simulation and detection

[0091] Monte Carlo method is used to obtain the scattering characteristics of bubbles, and phase detection is used to determine the movement trajectory of bubbles.

[0092] Step 5: Construction of the acoustic and optical multi-source data fusion positioning framework

[0093] The optimal data structure is obtained through correlation analysis, and the precise three-dimensional coordinates of the bubbles are obtained through iterative calculation;

[0094] Step 6: 3D display of bubble movement and alarm

[0095] Dynamically display the bubble motion trajectory model and issue intelligent alarms based on the volume and speed of the bubbles to improve surgical safety;

[0096] Step 7: Bubble Digital Twin Modeling

[0097] A three-dimensional mathematical model of the lumen and bubbles was established for the optimization of the bubble detection system.

[0098] The step 1 uses formulas (1.1) and (1.2) to establish a mathematical model of the propagation of the sound field and the light field in the long sheath tube. The specific formula is as follows:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] In formula (1.1), represents the pressure field of the sound wave, c is the speed of sound, is the sound source term, and t represent spatial position and time, respectively;

[0105] In formula (1.2), and are the electric field and the magnetic field, and are the electric displacement field and magnetic field respectively, ρ is the charge density, is the current density;

[0106] Formula (1.1) is the acoustic wave equation: it describes the propagation of sound waves in a medium. Sound waves are generated by pressure fluctuations in the medium and can be described by partial differential equations.

[0107] Formula (1.2) is Maxwell's equations: describing the propagation of electromagnetic waves (including light waves) in space. This is the basis of electromagnetic theory, involving electric fields, magnetic fields and their changes over time.

[0108] The propagation of sound waves in a medium is affected by the properties of the medium (such as density and elasticity). As a type of electromagnetic wave, the propagation speed and direction of light waves in different media will change.

[0109] The data types in this step mainly include the propagation data of sound waves and light waves, such as waveform, frequency, intensity, etc. The data comes from experimental measurements or theoretical calculations. The preprocessing steps include data cleaning, standardization and normalization to meet the input requirements of the model and extract features that have an important impact on the propagation characteristics of sound waves and light waves, such as frequency response, attenuation coefficient, etc.

[0110] First, according to the physical characteristics of the long sheath, such as material, size, and technical specifications of the acoustic and optical sensor, the model parameters, such as the speed of sound c and the propagation speed of electromagnetic waves, are initialized;

[0111] Then, based on the acoustic wave equation and Maxwell equations, a mathematical model of the propagation of sound and light waves in a long sheath is established;

[0112] Finally, numerical methods such as finite element analysis are used to simulate the propagation of sound and light waves to predict the propagation characteristics under specific conditions.

[0113] Through the propagation model of sound waves and light waves, the propagation characteristics of sound waves and light waves in the long sheath are provided, such as propagation speed, attenuation characteristics, etc. All relevant parameters and their values ​​are listed to provide necessary input data for subsequent steps.

[0114] Through this step, a solid theoretical foundation is laid for the combined acoustic and optical detection technology, providing a key prerequisite for subsequent bubble detection and positioning.

[0115] In step 2, formula (2) is used to optimize the layout of the acoustic-optical combination sensor to achieve all-round detection without blind spots in the long sheath tube. The specific formula is as follows:

[0116]

[0117] in, represents the position vector of the sensor, is the distance function between sensors i and j, w ij is the weight coefficient, which indicates the relative importance between sensors i and j. First, according to the output of step 1 (the propagation characteristics of sound waves and light waves), the parameters of the sensor network are initialized. Then, an optimization algorithm (such as genetic algorithm and particle swarm optimization) is used to determine the optimal location of the sensor to maximize the coverage and minimize the dead spots. Finally, the optimized sensor layout is verified in a computer simulation environment to ensure its effectiveness in practical applications.

[0118] According to the optimization results, the acoustic and optical combination sensor is embedded in the wall of the long sheath tube at the optimal position, preferably embedded in the inner wall of the long sheath tube. This step directly depends on the sound and light wave propagation model established in step 1, and adopts the optimization theory, which is a mathematical method for determining the optimal layout of the sensor; sensor network: a distribution of a group of sensors for collecting sound and light wave data. The spatial area that each sensor can effectively detect. The physical distance between sensors affects the continuity and integrity of the detection. The data types include sensor location data, detection efficiency data, etc. The data comes from the model output of step 1 and the technical specifications of the sensor. The preprocessing step includes standardizing the sensor data to ensure that it is suitable for the optimization model. Extract key features that affect the sensor layout, such as the detection range, the sensitivity of the sensor, etc. Output the sensor layout plan, provide the specific position and angle of each sensor to achieve the best detection effect, and a performance evaluation report to evaluate the detection efficiency and coverage of the optimized layout.

[0119] Through the implementation of step 2, the effectiveness and accuracy of the combined acoustic and optical detection system inside the long sheath can be ensured, providing a solid foundation for subsequent bubble detection and positioning. The successful implementation of this step is crucial to the performance of the entire system.

[0120] The step 3 sequentially uses formulas (3.1), (3.2) and (3.3) to collect data from the optimized layout of the acoustic-optical combination sensor to identify and analyze the acoustic characteristics of the bubble in the long sheath tube. The specific process is as follows:

[0121] 4.1 Acoustic feature extraction: Fourier transform is used to convert acoustic wave signals from time domain to frequency domain:

[0122]

[0123] Among them, S(f) is the frequency domain signal, s(f) is the time domain signal, represents Fourier transform;

[0124] 4.2 Feature vector construction: Extract feature vector from frequency domain signal:

[0125]

[0126] Among them, v i It is the feature quantity extracted from the frequency domain signal;

[0127] 4.3SVM Classification: Use SVM to classify the feature vector to identify the size and speed of the bubble:

[0128]

[0129] Among them, y is the classification result;

[0130] First, the acoustic wave signal is collected from the sensor configured in step 2; then the signal is preprocessed, including filtering and denoising, to improve the signal quality; the preprocessed acoustic wave signal is Fourier transformed to extract the frequency domain features, and then the feature vector is constructed based on the extracted frequency domain features. Finally, the feature vector is classified using SVM to identify the characteristics of the bubble.

[0131] This step is the key to achieve accurate bubble detection. Acoustic signal processing is used to analyze the acoustic signal and extract features for identification and classification; machine learning, especially support vector machine (SVM), is used to identify the presence and characteristics of bubbles from the acoustic features.

[0132] Acoustic wave scattering, the scattering phenomenon caused by bubbles in the sound wave propagation path, affects the propagation characteristics of the sound wave. The purpose of frequency domain analysis is to identify the characteristics of bubbles by analyzing the frequency components of the sound wave signal.

[0133] The data types include acoustic wave signal data, including time series data. The data comes from the acoustic-optical combination sensor in step 2. The preprocessing steps include signal amplification, filtering, denoising, etc. to improve the data quality. Key features such as frequency, amplitude, duration, etc. are extracted from the acoustic wave signal. The bubble characteristics are output, including information such as the size and speed of the bubble, as well as the classification results, and the bubble recognition results based on the acoustic characteristics.

[0134] Through the implementation of step 3, the acoustic characteristics of bubbles in the long sheath can be accurately identified and analyzed, providing key information for subsequent bubble positioning and processing. The successful implementation of this step is crucial to the performance and accuracy of the entire system.

[0135] The step 4 adopts formula (4) to simulate and detect the optical scattering characteristics of bubbles in the long sheath tube by using the optical data collected from the optimized layout of the acoustic-optical combination sensor. The specific formula is as follows:

[0136]

[0137] Among them, I(θ,φ) is the scattered light intensity in the direction (θ,φ), I0 is the incident light intensity, f(θ′,φ′) is the scattering amplitude function, k is the wave number, r is the distance from the observation point to the scattering center, ω is the angular frequency, θ′ and φ′ are the scattering angle and azimuth, respectively. First, collect optical data from the sensor configured in step 2, then use the Monte Carlo method to simulate the scattering process of light waves on the bubble surface, then calculate the intensity distribution of scattered light according to the scattering model, and finally analyze the scattered light intensity data to extract the position and motion characteristics of the bubble.

[0138] This step is the key to achieve precise positioning of bubbles.

[0139] Optical scattering theory is used to describe the scattering phenomenon when light waves interact with bubbles. The scattering phenomenon that occurs when light waves encounter bubbles affects the propagation path and intensity of the light waves.

[0140] Monte Carlo simulation is a random sampling method used to simulate the light wave scattering process. It simulates the behavior of complex systems through random sampling and is suitable for the simulation of light wave scattering.

[0141] The data type is optical scattering data, including light intensity and scattering angle. The data comes from the combined acoustic-optical sensor in step 2. The preprocessing steps include data cleaning and standardization to meet the input requirements of the scattering model. Key features such as scattering angle and light intensity change are extracted from the scattering data. The output of the bubble optical properties includes the intensity and pattern of light wave scattering caused by the bubble, the bubble position and motion information, and the bubble positioning results based on the optical scattering properties.

[0142] By implementing step 4, the optical scattering characteristics of the bubble in the long sheath can be accurately simulated and analyzed, providing key information for the precise positioning of the bubble. The successful implementation of this step is critical to the performance and accuracy of the entire system.

[0143] The step 5 adopts formula (5) and combines the acoustic and optical data collected in steps 3 and 4 to construct a multi-source data fusion framework for achieving accurate three-dimensional positioning of the bubble. The specific formula is as follows:

[0144]

[0145] Among them, among them, is the estimated bubble position at time t, K t is the Kalman gain, z t is the observation value, H is the observation model matrix. First, the acoustic and optical data in steps 3 and 4 are collected, and necessary preprocessing, such as filtering and denoising, is performed. The acoustic and optical data are fused using a Bayesian filter to improve the accuracy of bubble positioning. The position estimate of the bubble is then updated based on the fused data. The position estimate is continuously updated iteratively until the predetermined accuracy requirement is met.

[0146] Data fusion theory combines data from multiple sources to improve the accuracy and reliability of information. A Bayesian filter is a statistical method for updating an estimate of a system state under uncertainty. A Kalman filter is a special Bayesian filter used for state estimation of linear systems. The Kalman gain determines the extent to which new observations affect the state estimate. An observation model is a mathematical model that describes how observations are derived from the system state.

[0147] The data types include acoustic data (frequency, amplitude) and optical data (scattering intensity, angle). The data comes from the acoustic-optical combination sensor in step 3 and step 4. The preprocessing steps include data standardization, denoising, and filtering to meet the input requirements of the fusion model. Key features such as time series features, scattering features, etc. are extracted from the acoustic and optical data. Finally, the precise three-dimensional coordinates of the bubble are output: the bubble position estimation based on the fusion of acoustic and optical data and the positioning accuracy report: evaluate the accuracy and reliability of the positioning results after fusion.

[0148] The Kalman gain calculation method is used to calculate the Kalman gain based on the ratio of the prediction error and the measurement error.

[0149] The observation model selection and adjustment method is to select an observation model suitable for the characteristics of acoustic and optical data and adjust it according to the actual data.

[0150] Iteration process:

[0151] Iterations: Set the number of iterations based on the system dynamics and data changes.

[0152] Convergence conditions: Set convergence conditions, such as the change in state estimate is less than a certain threshold.

[0153] Performance evaluation metrics: Use metrics such as mean square error (MSE) to evaluate the accuracy of state estimation.

[0154] Through the implementation of step 5, the acoustic and optical data can be effectively combined to achieve accurate three-dimensional positioning of the bubble in the long sheath. The successful implementation of this step is critical to the performance of the entire system, especially in terms of improving positioning accuracy and reliability.

[0155] The step 6 adopts formula (6) and utilizes the bubble position data obtained by fusion in step 5 to realize the three-dimensional display of bubble movement and realize intelligent alarm according to the characteristics and behavior of the bubble. The specific formula is as follows:

[0156]

[0157] Where m is the mass of the bubble, is the position vector of the bubble, is the potential energy of the bubble, is resistance, It is buoyancy. First, receive the bubble position and motion data obtained by fusion in step 5; then build a three-dimensional dynamic display model based on the bubble position and motion data, and then dynamically display the bubble's motion trajectory in three-dimensional space. Finally, analyze the bubble's behavior and implement intelligent alarm according to preset rules such as bubble size and speed threshold.

[0158] The data types include the position, velocity, acceleration and other motion data of the bubble. The data comes from the data fusion results of step 5. The preprocessing step includes data format conversion to ensure that it is suitable for three-dimensional display and alarm system, and extract key features of bubble motion, such as velocity change, acceleration, etc. Finally, the three-dimensional dynamic display is output: a three-dimensional visual display of bubble motion; and an alarm signal: an alarm issued when the characteristics of the bubble meet the alarm conditions. The three-dimensional display parameters include display resolution, color coding, dynamic update frequency, etc. The alarm system parameters include the setting of alarm thresholds, alarm types (such as visual, sound), etc.

[0159] Through the implementation of step 6, the movement state of the bubble can be intuitively displayed, and an alarm can be issued in time when the behavior of the bubble is abnormal, thereby improving the safety and efficiency of the operation. The successful implementation of this step is crucial to the user-friendliness and practicality of the entire system.

[0160] In step 7, formula (7) is used to establish a digital twin model of a bubble, which can simulate the behavior and characteristics of the bubble in a virtual environment for further optimization and prediction of system performance. The specific formula is as follows:

[0161]

[0162] Among them, among them, is the fluid velocity field, ρ is the fluid density, p is the pressure, v is the dynamic viscosity coefficient, is the acceleration of gravity; first, based on the principles of fluid dynamics, a mathematical model of bubble motion is constructed, and then the model parameters are adjusted according to the actual observation data to ensure the accuracy of the model. Next, the motion of the bubbles is simulated in a virtual environment and its behavior is analyzed. Finally, the system performance is evaluated based on the simulation results, and optimization suggestions are put forward.

[0163] The data types include the motion data (position, velocity, acceleration) and environmental data (fluid properties, pressure, temperature) of the bubble; the data comes from the three-dimensional dynamic display and actual observation data in step 6. The preprocessing steps include data cleaning and format conversion to meet the input requirements of the digital twin model. Extract key features that affect the behavior of bubbles, such as fluid dynamics parameters; finally output the simulation results of bubble behavior: provide simulation of bubble behavior under different conditions; and system performance report: system performance evaluation and optimization suggestions based on simulation results.

[0164] Through the implementation of step 7, the behavior of bubbles can be accurately simulated in a virtual environment, providing a powerful tool for system optimization and prediction. The successful implementation of this step is crucial to improving the performance and reliability of the entire system.

[0165] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the scope of protection of the invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on these embodiments, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in this field can still combine, add, delete or make other adjustments to the features in the various embodiments of the present invention according to the circumstances without conflict, without making creative work, so as to obtain different other technical solutions that do not deviate from the concept of the present invention in essence, and these technical solutions also belong to the scope of protection of the present invention.

Claims

1. A method for detecting and locating bubbles in a long sheath tube based on combined acoustic and optical detection, characterized in that: The following steps are involved: Step 1: System model construction Firstly, a mathematical model of the acoustic and optical fields in the tube cavity is established to analyze the disturbance effect of bubbles on the propagation of sound and light, thus providing a theoretical basis for bubble detection. Step 2: Optimize the configuration of the acoustic and optical detection unit By spirally distributing the acoustic and optical combination sensors around the tube, the three-dimensional coverage is improved to avoid detection blind spots. Step 3: Algorithmic identification of bubble acoustic characteristics The time-frequency characteristics of the bubbles are extracted using Fourier transform, and the size and speed of the bubbles are obtained through support vector machine analysis. Step 4: Bubble optical scattering simulation and detection Monte Carlo method is used to obtain the scattering characteristics of bubbles, and phase detection is used to determine the movement trajectory of bubbles. Step 5: Construction of the acoustic and optical multi-source data fusion positioning framework The optimal data structure is obtained through correlation analysis, and the precise three-dimensional coordinates of the bubbles are obtained through iterative calculation; Step 6: 3D display of bubble movement and alarm Dynamically display the bubble motion trajectory model and issue intelligent alarms based on the volume and speed of the bubbles to improve surgical safety; Step 7: Bubble Digital Twin Modeling A three-dimensional mathematical model of the lumen and bubbles was established for the optimization of the bubble detection system.

2. The method for detecting and locating bubbles in a long sheath tube based on combined acoustic and optical detection according to claim 1, characterized in that : The step 1 uses formulas (1.1) and (1.2) to establish a mathematical model of the propagation of the sound field and the light field in the long sheath tube. The specific formula is as follows: In formula (1.1), represents the pressure field of the sound wave, c is the speed of sound, is the sound source term, and t represent spatial position and time, respectively; In formula (1.2), and are the electric field and the magnetic field, and are the electric displacement field and magnetic field respectively, ρ is the charge density, is the current density; First, according to the physical characteristics of the long sheath, including the material, size, and technical specifications of the acoustic and optical sensors, the model parameters are initialized, including the speed of sound c, the propagation speed of electromagnetic waves; Then, based on the acoustic wave equation and Maxwell equations, a mathematical model of the propagation of sound and light waves in a long sheath is established; Finally, a numerical method is used to simulate the propagation of sound waves and light waves to predict the propagation characteristics under specific conditions. The numerical method is finite element analysis.

3. The method for detecting and locating bubbles in a long sheath tube based on combined acoustic and optical detection according to claim 2, characterized in that: In step 2, formula (2) is used to optimize the layout of the acoustic-optical combination sensor to achieve all-round detection without blind spots in the long sheath tube. The specific formula is as follows: in, represents the position vector of the sensor, is the distance function between sensors i and j, w ij is the weight coefficient, which indicates the relative importance between sensors i and j. First, according to the output of step 1, the parameters of the sensor network are initialized. Then, an optimization algorithm is used to determine the optimal location of the sensor to maximize the coverage and minimize the blind spot. Finally, the optimized sensor layout is verified in a computer simulation environment to ensure its effectiveness in practical applications.

4. The method for detecting and locating bubbles in a long sheath tube based on combined acoustic and optical detection according to claim 3, characterized in that: The step 3 sequentially uses formulas (3.1), (3.2) and (3.3) to collect data from the optimized layout of the acoustic-optical combination sensor to identify and analyze the acoustic characteristics of the bubble in the long sheath tube. The specific process is as follows: 4.1 Acoustic feature extraction: Fourier transform is used to convert acoustic wave signals from time domain to frequency domain: Among them, S(f) is the frequency domain signal, s(t) is the time domain signal, represents Fourier transform; 4.2 Feature vector construction: Extract feature vector from frequency domain signal: Among them, v i It is the feature quantity extracted from the frequency domain signal; 4.3SVM Classification: Use SVM to classify the feature vector to identify the size and speed of the bubble: Among them, y is the classification result; First, the acoustic wave signal is collected from the sensor configured in step 2. Then, the signal is preprocessed, including filtering and denoising, to improve the signal quality. The preprocessed acoustic wave signal is Fourier transformed to extract the frequency domain features. Then, a feature vector is constructed based on the extracted frequency domain features. Finally, the feature vector is classified using SVM to identify the characteristics of the bubble.

5. The method for detecting and locating bubbles in a long sheath tube based on combined acoustic and optical detection according to claim 4, characterized in that: The step 4 adopts formula (4) to simulate and detect the optical scattering characteristics of bubbles in the long sheath tube by using the optical data collected from the optimized layout of the acoustic-optical combination sensor. The specific formula is as follows: Among them, I(θ,φ) is the scattered light intensity in the direction (θ,φ), I0 is the incident light intensity, f(θ′,φ′) is the scattering amplitude function, k is the wave number, r is the distance from the observation point to the scattering center, ω is the angular frequency, θ′ and φ′ are the scattering angle and azimuth, respectively. First, collect optical data from the sensor configured in step 2, then use the Monte Carlo method to simulate the scattering process of light waves on the bubble surface, then calculate the intensity distribution of scattered light according to the scattering model, and finally analyze the scattered light intensity data to extract the position and motion characteristics of the bubble.

6. The method for detecting and locating bubbles in a long sheath tube based on combined acoustic and optical detection according to claim 5, characterized in that: The step 5 adopts formula (5) and combines the acoustic and optical data collected in steps 3 and 4 to construct a multi-source data fusion framework for achieving accurate three-dimensional positioning of the bubble. The specific formula is as follows: Among them, among them, is the estimated bubble position at time t, K t is the Kalman gain, z t is the observation value, H is the observation model matrix. First, the acoustic and optical data in steps 3 and 4 are collected and necessary preprocessing is performed, including filtering and denoising. The acoustic and optical data are fused using a Bayesian filter to improve the accuracy of bubble positioning. The position estimate of the bubble is then updated based on the fused data. The position estimate is continuously updated iteratively until the predetermined accuracy requirement is met.

7. The method for detecting and locating bubbles in a long sheath tube based on combined acoustic and optical detection according to claim 6, characterized in that: The step 6 adopts formula (6) and utilizes the bubble position data obtained by fusion in step 5 to realize the three-dimensional display of bubble movement and realize intelligent alarm according to the characteristics and behavior of the bubble. The specific formula is as follows: Where m is the mass of the bubble, is the position vector of the bubble, is the potential energy of the bubble, is resistance, It is buoyancy. First, receive the bubble position and motion data obtained by fusion in step 5; then build a three-dimensional dynamic display model based on the bubble position and motion data, and then dynamically display the bubble's motion trajectory in three-dimensional space. Finally, analyze the bubble's behavior and implement intelligent alarm according to preset rules such as bubble size and speed threshold.

8. The method for detecting and locating bubbles in a long sheath tube based on combined acoustic and optical detection according to claim 7, characterized in that: In step 7, formula (7) is used to establish a digital twin model of a bubble, which can simulate the behavior and characteristics of the bubble in a virtual environment for further optimization and prediction of system performance. The specific formula is as follows: in, is the fluid velocity field, ρ is the fluid density, p is the pressure, v is the dynamic viscosity coefficient, is the acceleration of gravity; first, based on the principles of fluid dynamics, a mathematical model of bubble motion is constructed, and then the model parameters are adjusted according to the actual observation data to ensure the accuracy of the model. Next, the motion of the bubbles is simulated in a virtual environment and its behavior is analyzed. Finally, the system performance is evaluated based on the simulation results, and optimization suggestions are put forward.

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