Portable calculus removing system and method

Abstract

The invention relates to the technical field of calculus treatment, and discloses a portable calculus removing system and method.The portable calculus removing system comprises an output energy calculation module used for collecting calculus characteristic parameters corresponding to calculus parts through a built-in sensor of calculus removing equipment worn by a patient, conducting dynamic energy adaptation analysis on the calculus removing equipment and calculating the calculus characteristic parameters; obtaining an initial calculus removal parameter combination; the pressure detection module is used for performing layered pressure detection on the calculus part to obtain tissue pressure distribution data; the displacement risk cascade division module is used for calculating a calculus removal utility coefficient corresponding to the calculus part; the energy attenuation gradient calculation module is used for positioning a narrow channel region corresponding to the calculus part and calculating an energy attenuation gradient corresponding to the narrow channel region; and the calculus removal processing module is used for generating a safety intervention signal corresponding to the calculus removal equipment so as to execute calculus removal processing on the calculus part through the calculus removal equipment to obtain a processing result, and the calculus removal accuracy is improved.

Description

Technical Field

[0001] This invention belongs to the field of stone treatment technology, and in particular relates to a portable stone removal system and method. Background Technology

[0002] Urinary tract stones (such as kidney stones and ureteral stones) are common urinary system diseases. In traditional treatments, extracorporeal shock wave lithotripsy (ESWL) needs to be performed in a medical institution. The equipment is large, the operation is complicated, and the treatment cost is high. Conventional portable stone removal devices mostly use vibration modes with fixed frequency and intensity, lacking targeted adjustment. They are difficult to adapt to the different characteristics of stones (such as size, hardness, and location) and the body's tolerance, and are prone to problems such as low stone removal efficiency or overstimulation leading to tissue damage.

[0003] Currently, the shortcomings of existing portable stone removal devices are that they cannot perceive the dynamic state of the stones in real time, such as displacement trends and relative positions with surrounding tissues, making it difficult to accurately adjust the action parameters. Secondly, they lack dynamic analysis of the safety of tissues at the stone site and only control energy output through preset thresholds, which cannot cope with the individual risks of high-risk areas such as narrow passages, thus reducing the accuracy of stone removal. Summary of the Invention

[0004] This invention provides a portable stone removal system and method, the main purpose of which is to improve the accuracy of stone removal.

[0005] To achieve the above objectives, the present invention provides a portable stone removal system, comprising: an output energy calculation module, a pressure detection module, a displacement risk tier division module, an energy attenuation gradient calculation module, and a stone removal processing module; The output energy calculation module is used to collect stone characteristic parameters corresponding to the stone site using the built-in sensor of the stone removal device worn by the patient, perform dynamic energy adaptation analysis on the stone removal device based on the stone characteristic parameters, obtain an initial stone removal parameter combination, and calculate the vibration output energy corresponding to the initial stone removal parameter combination. The pressure detection module is used to analyze the structural stress state corresponding to the stone site based on the vibration output energy, query the safety action threshold corresponding to the structural stress state, and perform layered pressure detection on the stone site based on the safety action threshold to obtain tissue pressure distribution data. The displacement risk tier classification module is used to analyze the displacement response intensity corresponding to the stone site based on the tissue pressure distribution data, monitor the real-time physiological parameters corresponding to the stone site, and calculate the stone expulsion utility coefficient corresponding to the stone site by combining the displacement response intensity and the real-time physiological parameters, and classify the displacement risk tier corresponding to the stone expulsion utility coefficient. The energy attenuation gradient calculation module is used to locate the narrow channel region corresponding to the stone site and calculate the energy attenuation gradient corresponding to the narrow channel region. The stone removal module is used to generate a safety intervention signal corresponding to the stone removal device based on the energy attenuation gradient, send the safety intervention signal to the terminal control module of the stone removal device, obtain module feedback instructions, and formulate a dynamic stone removal strategy corresponding to the stone site by combining the module feedback instructions and the displacement risk gradient, so as to perform stone removal treatment on the stone site through the stone removal device and obtain the treatment result.

[0006] Optionally, generating the safety intervention signal corresponding to the stone removal device based on the energy attenuation gradient includes: Analyze the energy transfer efficiency range corresponding to the energy decay gradient; Based on the energy transfer efficiency range, the energy output level of the stone removal equipment is divided. Query the energy security data associated with the changes in the energy output level; Extract the energy regulation factors from the energy security data; Based on the energy regulation factor, a safety intervention signal corresponding to the stone removal device is generated.

[0007] A portable method for removing kidney stones, the method comprising: The built-in sensors of the stone removal device worn by the patient collect the stone characteristic parameters corresponding to the stone location. Based on the stone characteristic parameters, the stone removal device is subjected to dynamic energy adaptation analysis to obtain an initial stone removal parameter combination, and the vibration output energy corresponding to the initial stone removal parameter combination is calculated. Based on the vibration output energy, the structural stress state corresponding to the stone site is analyzed, the safety action threshold corresponding to the structural stress state is queried, and based on the safety action threshold, the layered pressure detection is performed on the stone site to obtain tissue pressure distribution data. Based on the tissue pressure distribution data, the displacement response intensity corresponding to the stone site is analyzed, and the real-time physiological parameters corresponding to the stone site are monitored. Combining the displacement response intensity and the real-time physiological parameters, the stone expulsion efficiency coefficient corresponding to the stone site is calculated, and the displacement risk level corresponding to the stone expulsion efficiency coefficient is divided. Locate the narrow channel region corresponding to the stone site and calculate the energy attenuation gradient corresponding to the narrow channel region; Based on the energy attenuation gradient, a safety intervention signal corresponding to the stone removal device is generated and sent to the terminal control module of the stone removal device to obtain module feedback instructions. Combining the module feedback instructions and the displacement risk gradient, a dynamic stone removal strategy corresponding to the stone site is formulated so as to perform stone removal treatment on the stone site through the stone removal device and obtain the treatment result.

[0008] This invention performs dynamic energy adaptation analysis on the stone-expelling device based on the stone's characteristic parameters to obtain an initial combination of stone-expelling parameters. This achieves dynamic matching between energy and stone characteristics, avoiding the problems of low stone-expelling efficiency or tissue damage in traditional fixed-parameter modes. Optionally, this invention analyzes the structural stress state corresponding to the stone location based on the vibration output energy. This allows for understanding the stress on the stone and surrounding tissues under energy action, preventing damage caused by excessive stress. By querying the safe operating threshold corresponding to the structural stress state, the tolerance limits of different tissues can be clarified, providing a safety benchmark for subsequent pressure testing and ensuring that treatment effectively expels stones while protecting tissues. This invention also analyzes the displacement response intensity corresponding to the stone location based on the tissue pressure distribution data, revealing the movement trend and amplitude of the stone under vibration energy. This invention precisely captures the dynamic effects of the stone removal process while monitoring real-time physiological parameters, enabling real-time perception of the treatment's impact on the body and avoiding the pursuit of stone removal effects at the expense of tissue tolerance. By locating the narrow passage region corresponding to the stone site, this invention can clearly identify the key obstruction locations in the stone removal path, providing spatial coordinate references for precisely controlling the distribution of treatment energy in this region. This avoids energy blindly acting on non-target areas, thereby reducing the risk of damage to healthy tissue and increasing the success rate of stones passing through narrow passages. Furthermore, this invention generates safety intervention signals for the stone removal device based on the energy attenuation gradient. It can transform abstract energy transfer data into specific device control commands based on the energy attenuation law in the narrow passage region, proactively avoiding the risks of insufficient or excessive energy, and providing a scientific basis for the precise operation of the stone removal device. Therefore, it improves the accuracy of stone removal. Attached Figure Description

[0009] Figure 1 A functional block diagram of a portable stone removal system provided in an embodiment of the present invention; Figure 2 A schematic diagram of the stone removal device provided by the present invention; Figure 3 This is a schematic flowchart of a portable lithotripsy method provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0012] In practice, the server-side equipment deployed in a portable lithotripsy system may consist of one or more devices. The aforementioned portable lithotripsy system can be implemented as: a business instance, a virtual machine, or a hardware device. For example, the portable lithotripsy system can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, the portable lithotripsy system can be understood as software deployed on a cloud node to provide portable lithotripsy services to various user terminals. Alternatively, the portable lithotripsy system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, the portable lithotripsy system can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide portable lithotripsy services to various user terminals.

[0013] In terms of implementation, the portable stone removal system and the user terminal are mutually compatible. That is, if the portable stone removal system is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the portable stone removal system is implemented as a website, then the user terminal is implemented as a webpage; or if the portable stone removal system is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0014] Reference Figure 1 The diagram shown is a functional block diagram of a portable stone removal system provided in an embodiment of the present invention.

[0015] The portable stone removal system 100 of this invention can be installed on a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed in the cloud (e.g., a portable stone removal server, a server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the portable stone removal system 100 includes an output energy calculation module 101, a pressure detection module 102, a displacement risk tier division module 103, an energy attenuation gradient calculation module 104, and a stone removal processing module 105.

[0016] In this embodiment of the invention, based on portable lithotripsy tracking, each of the above modules can be implemented independently and can call other modules. Here, "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the portable lithotripsy system provided by this embodiment, without modifying the program code, the applicability of the portable lithotripsy architecture can be adjusted by adding modules and directly calling them, achieving cluster-based horizontal expansion to quickly and flexibly expand the portable lithotripsy system. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0017] The following describes each component and specific workflow of the portable stone removal system in conjunction with specific embodiments.

[0018] The output energy calculation module 101 is used to collect stone characteristic parameters corresponding to the stone location using the built-in sensor of the stone removal device worn by the patient, perform dynamic energy adaptation analysis on the stone removal device based on the stone characteristic parameters, obtain an initial stone removal parameter combination, and calculate the vibration output energy corresponding to the initial stone removal parameter combination.

[0019] This invention utilizes dynamic energy adaptation analysis based on the characteristic parameters of the stones to obtain an initial combination of expelling parameters for the stone-dissolving device. This achieves dynamic matching between energy and stone characteristics, avoiding the problems of low expulsion efficiency or tissue damage in traditional fixed-parameter modes. The expelling device is a portable instrument that patients can wear independently at the stone site, such as a wearable stone-dissolving belt. It features an ergonomic design that securely wraps around the waist, allowing for easy and convenient wearing by the patient at the stone location. Multiple functional units are distributed throughout, using physical vibration and other modes to assist in the movement and expulsion of the stones. It can be easily used at home. (For reference only.) Figure 2 This is a schematic diagram of a stone-dissolving belt in the portable stone-dissolving system provided in this application of the present invention; the built-in sensors are detection elements assisting the stone-dissolving device, such as ultrasound sensors and pressure sensors; the stone characteristic parameters are the physical and location information of the stone corresponding to the stone site, such as the stone diameter and location; the initial stone-dissolving parameter combination is a set of basic operating parameters obtained after dynamic energy adaptation analysis of the stone-dissolving device based on the stone characteristic parameters, such as a combination of vibration frequency of 8Hz and pulse intensity of 100mJ / cm²; optionally, the built-in sensors of the stone-dissolving device worn by the patient are used to collect the stone characteristic parameters corresponding to the stone site, such as the size of the stone (e.g., diameter 8mm), hardness (e.g., 280HV), location (e.g., upper right ureter), movement speed (e.g., 0.3mm / min), and distance from surrounding tissues (e.g., 3mm).

[0020] As an embodiment of the present invention, the step of performing dynamic energy adaptation analysis on the stone removal device based on the stone characteristic parameters to obtain an initial stone removal parameter combination includes: Analyze the initial stone-forming conditions corresponding to the stone characteristic parameters, and query the basic energy settings of the stone-removing device under the initial stone-forming conditions; Analyze the output response characteristics corresponding to the basic energy settings, and based on the output response characteristics, identify the energy distribution data corresponding to the stone removal device; Based on the energy distribution data, dynamic energy adaptation analysis is performed on the stone removal equipment to obtain the initial combination of stone removal parameters.

[0021] The initial stone conditions refer to the basic attribute status of the stone at the start of dynamic energy adaptation, including location, size, and density. This is the starting point for energy adaptation. For example, in the initial detection stage, the stone is located in the lower calyx of the kidney, has a diameter of 6mm, and a density of 1400HU. The basic energy setting refers to the preset energy output level (such as low / medium / high energy level) of the stone removal device under the initial stone conditions, providing a starting point for adaptation analysis. For example, the initial setting might be a frequency of 5Hz and an amplitude of 1.5mm. The output response characteristics refer to the vibration energy output by the device under the basic energy setting. The transmission efficiency, focusing, and attenuation characteristics within the tissue are varied. For example, when energy passes through tissue, the surface absorption rate is higher and the deep focusing rate is weaker. This spatial response difference constitutes the output response characteristics. The energy distribution data refers to the density value and diffusion status of the vibration energy output by the stone removal device at different depths or areas of the patient's stone site, reflecting the range of energy action in the body. For example, the calculated energy density at the body surface is 50 J / cm², the energy density at the stone site is 30 J / cm², and the energy density in the surrounding tissue is 10 J / cm². These data are used to adjust the parameter combination.

[0022] Optionally, the initial stone conditions corresponding to the stone characteristic parameters can be analyzed using clustering classification techniques, such as using the K-means algorithm for feature clustering to obtain normalized initial conditions for stone location and hardness. The basic energy settings corresponding to the stone removal device under the initial stone conditions can be retrieved using medical device knowledge base retrieval techniques, such as using an SQLite database to call a preset clinical data table and matching stone types to obtain basic energy settings based on safety thresholds. The output response characteristics corresponding to the basic energy settings can be analyzed using finite element modeling techniques, such as using COMSOL Multiphysics software to construct a tissue-stone coupling model for simulation to obtain energy decay curves and focusing characteristics. The energy distribution data corresponding to the stone removal device can be identified using thermal diffusion imaging techniques, such as using an infrared sensor array with ImageJ for energy field reconstruction to obtain quantitative data on three-dimensional energy density distribution. Dynamic energy adaptation analysis of the stone removal device can be performed using reinforcement learning techniques, such as using the Q-learning algorithm combined with the TensorFlow framework to optimize parameter combinations to obtain initial stone removal parameter combinations for individual patients.

[0023] This invention quantifies the energy output level of the stone-expelling device during the treatment phase by calculating the vibration output energy corresponding to the initial combination of stone-expelling parameters. This provides key data support for adjusting subsequent treatments and optimizing energy output to avoid tissue damage. The vibration output energy refers to the total amount of mechanical energy applied to the stone site by the stone-expelling device through vibration per unit time, measured in joules (J). By integrating data such as the frequency, amplitude, duration of action, and device efficiency factor of the parameter combination, this invention quantifies the effectiveness of the device's output energy and reflects the core indicator of its crushing performance. A larger value indicates a stronger crushing force.

[0024] As an embodiment of the present invention, the calculation of the vibration output energy corresponding to the initial combination of stone removal parameters includes: Based on the stone characteristic parameters, the treatment target in the initial stone expulsion parameter combination is determined; Identify the start and end times of the vibration output process in the initial combination of stone discharge parameters; The amplitude and frequency values ​​of the treatment target at different time points are extracted from the initial combination of stone expulsion parameters. Combining the start time, the end time, the amplitude value, and the frequency value, the vibration output energy corresponding to the initial combination of stone removal parameters is calculated using the following formula: Where E represents the vibration output energy corresponding to the initial combination of stone-expelling parameters, n represents the total number of treatment target points, and i represents the index of the number of treatment target points. and These represent the start and end times of the vibration output process in the initial combination of stone removal parameters. This represents the energy conversion efficiency constant of the stone removal equipment. This represents the amplitude value of the i-th treatment target point in the initial combination of stone removal parameters at time t. This represents the frequency value of the i-th treatment target in the initial combination of stone removal parameters at time t. This represents the directional efficiency factor of the i-th therapeutic target at time t. This represents the difference between the stone density corresponding to the i-th treatment target point and the tissue reference density. This represents the reference density of soft tissue.

[0025] Furthermore, in this invention, the vibration output energy calculation is based on the engineering equivalent logic of the interaction between vibration and stones in a stone expulsion scenario. From a physical mechanism perspective, the amplitude determines the intensity of the mechanical disturbance of the vibration on the stone (similar to simple harmonic motion, where energy is positively correlated with the square of the amplitude), and the frequency reflects the number of energy transfer cycles per unit time. Together, they characterize the potential of the vibration system to transfer energy to the stone. Because the interaction between the stone and soft tissue is complex in clinical stone expulsion, it is difficult to directly measure the physical energy absorbed by the stone. Therefore, by associating the core elements of energy transfer with amplitude and frequency, and combining equipment efficiency, direction matching degree, and tissue-stone density difference, an equivalent calculation model is constructed to quantify the energy output of the stone expulsion parameter combination, providing comparable engineering indicators for optimizing the stone expulsion scheme.

[0026] For example, taking the treatment of urinary tract stones as an example, assuming the treatment is for a single kidney stone, the number of treatment target points is 1, and the start time is set. =0, End Time =60, Energy conversion efficiency constant of stone removal equipment =0.8, during treatment, target amplitude ,frequency The directional efficiency factor is always 0.9 due to the precise focusing of the equipment, the density difference between the stone and the tissue is 100, and the soft tissue reference density is 1000. Substituting these parameters into the formula, the vibration output energy is calculated to be approximately 213.55.

[0027] It should be noted that the above vibration output energy calculation formula considers the energy output of the stone expulsion process in an integral form, defining the analysis interval by the start and end times. For each treatment target point, the energy conversion efficiency constant is used to correlate with the characteristics of the equipment. The square of the amplitude, frequency, direction efficiency factor, and density difference between the stone and the tissue are used to comprehensively characterize the energy contribution of the target point per unit time. Its core is to accurately aggregate the energy output of each target point during the stone expulsion period through multi-parameter coupling and integral accumulation. Furthermore, the parameters in the above formula are unitless pure values, which enables parallel calculation of multiple targets and adapts to the multi-regional characteristics of human stone distribution. The density difference factor highlights the influence of the difference in physical properties between the stone and the surrounding tissue on the energy effect.

[0028] Furthermore, in practical applications, such as in the treatment of urinary system stones, this method can precisely quantify the energy effects of different stone removal strategies (e.g., adjusting amplitude and frequency to suit stones of different hardness). Compared to single-parameter evaluation, it can more comprehensively reflect the effectiveness of stone removal equipment on stones. By adjusting parameters such as the number of target points and energy conversion efficiency, it can be adapted to different stone removal devices (e.g., multi-module output of wearable belts, single-point focusing of extracorporeal shock wave devices), achieving precise control of stone removal energy and personalized plan evaluation, helping to optimize the combination of stone removal parameters and improve the effectiveness and safety of stone removal treatment.

[0029] The treatment target is defined by dividing the stone area into multiple virtual locations (such as the center of the stone body and the edge of fragments) based on the stone's characteristic parameters. The amplitude value reflects the vibration displacement amplitude, the frequency value represents the impact rate, and the directional efficiency factor quantifies the energy loss in a specific direction. It is determined by the directional angle in the parameter combination (such as 0° for high efficiency in the vertical direction). A value less than 1 indicates energy attenuation in non-vertical directions. The difference between the stone density and the tissue reference density captures the stone's hardness characteristics (the larger the difference, the more difficult the stone is to break, requiring higher energy). The reference density normalizes the tissue influence (soft tissue buffering reduces effective energy), and the inherent mechanical conversion coefficient of the equipment (such as 0.5 J·s / Hz / mm²) is considered. The energy conversion efficiency constant is determined by the equipment model and calibration experiments to ensure that the energy calculation is consistent with the actual physical output. Furthermore, this formula provides accurate energy output prediction by integrating the dynamic values ​​of the parameter combination, density difference, and efficiency factor, which can dynamically optimize treatment. For example, if the energy is too high in the simulation (E>safety threshold), the system automatically reduces the amplitude or frequency.

[0030] The pressure detection module 102 is used to analyze the structural stress state corresponding to the stone site based on the vibration output energy, query the safety action threshold corresponding to the structural stress state, and perform layered pressure detection on the stone site based on the safety action threshold to obtain tissue pressure distribution data.

[0031] This invention analyzes the structural stress state corresponding to the stone site based on the vibration output energy, thereby understanding the stress on the stone and surrounding tissues under energy action, avoiding damage caused by excessive force, and querying the safe action threshold corresponding to the structural stress state to clarify the tolerance limit of different tissues, providing a safety benchmark for subsequent pressure testing, and ensuring that treatment effectively removes stones while protecting tissues.

[0032] The structural stress state refers to the comprehensive characterization of the mechanical state of the stone and surrounding tissues under the action of vibration energy, including deformation, tension, and shear force. It reflects the dynamic changes of the tissue under external force. For example, when vibration energy acts on a kidney stone, the stretching of the renal capsule, the tension of the surrounding muscles, and the distribution of friction between the stone and the tissue together constitute the structural stress state. The safe action threshold refers to the maximum tolerable pressure / stress limit set for different tissue types (such as muscle tissue, renal capsule, and ureteral wall). It is formulated based on the mechanical properties of human tissues and clinical safety data. For example, the safe action threshold for the renal capsule is set at 5 kPa. Exceeding this value may cause capsule damage, while the threshold for muscle tissue can be relaxed to 8 kPa. Optionally, the analysis of the structural stress state can be realized through finite element simulation technology, such as: using ANSYS software to establish a human tissue mechanical model, inputting vibration energy parameters to simulate stress distribution, and obtaining three-dimensional structural stress state related data; the safe action threshold can be queried through a tissue mechanical database, such as: calling human tissue tolerance research data collected in PubMed, and combining the patient's age and physical condition to adjust the threshold to obtain a personalized safe action threshold.

[0033] This invention performs layered pressure detection on the stone site based on the aforementioned safety threshold to obtain tissue pressure distribution data, capture the actual pressure differences between different tissue layers, and promptly identify pressure concentration areas exceeding the threshold. This provides a basis for dynamically adjusting the parameters of the stone removal device. The tissue pressure distribution data is a collection of the actual pressure values ​​and distribution patterns of each tissue layer obtained after layered pressure detection of the stone site. For example, the specific values ​​of 2 kPa pressure on the skin layer, 3.5 kPa pressure on the muscle layer, and 1.8 kPa pressure on the outer layer of organs, as well as the curves of pressure changes over time at different levels and the location information of pressure concentration areas.

[0034] As an embodiment of the present invention, the step of performing layered pressure detection on the stone site based on the safety threshold to obtain tissue pressure distribution data includes: Based on preset human tissue characteristics, the stone site is divided into layers to obtain tissue layers; The pressure sensor in the stone removal device is used to collect and process data from the tissue layers to obtain initial tissue pressure data. The initial tissue pressure data is subjected to spatiotemporal calibration to obtain calibrated tissue pressure data, and a pressure distribution field corresponding to the calibrated tissue pressure data is constructed. Based on the organizational hierarchy, the pressure distribution field is visualized and transformed to obtain a layered pressure heat map; Based on the aforementioned safety threshold, anomalies are marked in the pressure distribution field to obtain a pressure anomaly map; Extract the abnormal pressure features corresponding to the abnormal pressure map, and calculate the risk coefficient corresponding to the tissue level based on the abnormal pressure features; The risk coefficient, the stratified pressure heat map, and the abnormal pressure characteristics are integrated and processed to obtain tissue pressure distribution data.

[0035] The preset human tissue characteristics are based on the physical properties (such as elasticity, density, and thickness) and distribution patterns of different tissues defined anatomically; the tissue hierarchy is a continuous tissue layer (such as skin layer, muscle layer, organ capsule layer, etc.) distinguished by anatomical structure after the stone site is divided into layers; the pressure sensor is a miniature sensing element in the stone removal device used to collect pressure signals from each tissue layer at the stone site; the initial tissue pressure data is the set of raw pressure values ​​collected by the sensors at the tissue level; the calibrated tissue pressure data is the accurate pressure data obtained after the initial tissue pressure data has undergone spatiotemporal synchronization, noise reduction, and other processing; the pressure distribution field... The pressure data refers to the continuous pressure value distribution in three-dimensional space corresponding to the calibrated tissue pressure data; the layered pressure heatmap is an image obtained by visualizing the pressure distribution field based on the tissue layers, displaying the pressure differences between each layer with color gradients; the pressure anomaly map is a map that highlights the pressure regions exceeding the threshold pressure, obtained by marking anomalies in the pressure distribution field based on the safety threshold; the abnormal pressure characteristics are the key parameters (such as pressure peak, area, and duration) of the abnormal regions corresponding to the pressure anomaly map; the risk coefficient is a quantitative indicator of the probability of damage corresponding to the tissue layer, calculated based on the abnormal pressure characteristics.

[0036] Optionally, based on preset human tissue characteristics, the stone site can be segmented using ultrasound computed tomography (e.g., B-mode ultrasound imaging) to obtain tissue layers; the initial tissue pressure data can be spatiotemporally calibrated using a Kalman filter algorithm (e.g., combining timestamp synchronization and spatial coordinate calibration) to obtain calibrated tissue pressure data; the pressure distribution field corresponding to the calibrated tissue pressure data can be constructed using inverse distance weighted interpolation (e.g., constructing a continuous field model based on discrete pressure points); based on the tissue layers, the pressure distribution field can be visualized using a color mapping algorithm (e.g., using blue to red to represent pressure from low to high) to obtain a layered pressure heatmap; based on the safety threshold, a threshold determination model (e.g., ...) can be used to... Anomalies are marked in the pressure distribution field by setting a critical value to mark the region exceeding the threshold, resulting in a pressure anomaly map. Abnormal pressure features corresponding to the pressure anomaly map can be extracted using feature extraction algorithms (such as extracting parameters like pressure peak value and area of ​​the abnormal region). Based on these abnormal pressure features, the risk coefficient corresponding to the tissue level can be calculated using a risk assessment model (such as calculating the probability of damage by combining tissue tolerance). The risk coefficient, the layered pressure heatmap, and the abnormal pressure features can be integrated and processed using multi-dimensional data fusion techniques (such as integrating images, parameters, and indices to generate a comprehensive dataset) to obtain tissue pressure distribution data.

[0037] The displacement risk tier classification module 103 is used to analyze the displacement response intensity corresponding to the stone site based on the tissue pressure distribution data, monitor the real-time physiological parameters corresponding to the stone site, and calculate the stone expulsion utility coefficient corresponding to the stone site by combining the displacement response intensity and the real-time physiological parameters, and classify the displacement risk tier corresponding to the stone expulsion utility coefficient.

[0038] This invention analyzes the displacement response intensity corresponding to the stone site based on the tissue pressure distribution data, thereby understanding the movement trend and amplitude of the stone under the action of vibration energy, accurately capturing the dynamic effect of the stone expulsion process, and monitoring real-time physiological parameters. It can also perceive the impact of treatment on the human body in real time, avoiding the pursuit of stone expulsion effect while ignoring tissue tolerance.

[0039] The displacement response intensity refers to the comprehensive characterization of the dynamic characteristics of the stone under vibration pressure, such as displacement, movement speed, and acceleration, reflecting the degree of stone response to energy. For example, if a stone moves 3 mm within 10 minutes after vibration, with an average speed of 0.3 mm / min, its displacement response intensity can be quantified as moderate. The real-time physiological parameters refer to dynamic physiological indicators related to the stone site and the overall condition during treatment, used to assess tissue tolerance and safety. Examples include local tissue temperature (e.g., 38℃), muscle tension (e.g., 2.5N), heart rate (e.g., 85 beats / min), and pain threshold (e.g., VAS score of 3). Optionally, the real-time physiological parameters corresponding to the stone site, such as temperature sensors and electromyography sensors, can be monitored by a flexible biosensor array in the stone removal belt.

[0040] As an embodiment of the present invention, the step of analyzing the displacement response intensity corresponding to the stone site based on the tissue pressure distribution data includes: Extract the pressure gradient features from the tissue pressure distribution data; Based on the pressure gradient characteristics, the stress-bearing area corresponding to the stone location is determined; Quantify the pressure impulse index corresponding to the force-affected area; Based on the pressure impulse index, the initial displacement trend corresponding to the stone site is evaluated. Based on the initial displacement trend, the displacement response intensity corresponding to the stone site is analyzed.

[0041] The pressure gradient characteristic refers to the rate of change and directional characteristics of pressure values ​​with spatial location in tissue pressure distribution data, reflecting the distribution pattern of forces propelling the stone. For example, a pressure value of 5 kPa on the left side of the stone and 3 kPa on the right side forms a pressure gradient of 2 kPa / mm, pointing to the right, suggesting that the stone may move to the right. The force-bearing area refers to the tissue region that directly exerts a pushing force on the stone, determined based on the pressure gradient characteristic. This is typically the area where the pressure gradient points towards the stone; for example, the pressure concentration area in the muscle layer below the stone is identified as the main force-bearing area. The pressure impulse index is... The pressure value in the area of ​​force application is the product of the application time, quantifying the cumulative effect of force on the stone. For example, a pressure of 5 kPa applied for 0.5 seconds results in a pressure impulse index of 2.5 kPa·s. The initial displacement trend refers to the dynamic characteristics of the stone's initial movement, such as direction and initial velocity, predicted based on the pressure impulse index. For example, when the pressure impulse index reaches 3 kPa·s, it is predicted that the stone will move along the pressure gradient direction with an initial velocity of 0.1 mm / s. Through the above steps, key information related to stone displacement is gradually extracted from the tissue pressure data, ultimately achieving accurate analysis of the displacement response intensity.

[0042] Optionally, pressure gradient features in the tissue pressure distribution data can be extracted using a spatial gradient operator (such as the Sobel operator) combined with a pressure field interpolation algorithm; based on the pressure gradient features, the stress-bearing area corresponding to the stone location can be determined using a region growing algorithm combined with stone contour recognition technology; the pressure impulse index corresponding to the stress-bearing area can be quantified using a pressure-time integration algorithm combined with dynamic threshold segmentation technology; based on the pressure impulse index, the initial displacement trend corresponding to the stone location can be evaluated by establishing a stone-tissue contact mechanics model combined with finite element simulation technology; based on the initial displacement trend, the analysis can be verified by multi-parameter fusion regression analysis combined with real-time ultrasound monitoring data. The displacement response intensity corresponding to the stone location was determined by, for example, using the Sobel operator to calculate the gradient components of the pressure field in the x, y, and z directions, obtaining a pressure gradient vector diagram; using a region growing algorithm, starting from the edge point of the stone, regions satisfying the gradient direction pointing towards the stone and the gradient amplitude > 0.5 kPa / mm were divided into force-bearing regions; the pressure-time curve of this region was integrated, and when the integral value > 2.5 kPa·ms, it was determined as an effective impulse; the impulse value was substituted into the contact mechanics model to predict the stone's displacement trend of 0.3 mm within 0.1 s; finally, the actual displacement of the stone was monitored in real time by ultrasound, and the error rate compared with the predicted value was < 12%, verifying the accuracy of the displacement response intensity analysis.

[0043] This invention calculates the stone expulsion efficiency coefficient corresponding to the stone site by combining the displacement response intensity and the real-time physiological parameters. This allows us to understand the dynamic balance between stone expulsion effect and tissue tolerance, and to quantify the comprehensive effectiveness of "effective stone expulsion" and "safety protection" during the treatment process. The stone expulsion efficiency coefficient represents the comprehensive quantitative ratio of stone displacement ability and surrounding tissue physiological tolerance at the stone site under specific treatment intervention.

[0044] As an embodiment of the present invention, the step of calculating the stone expulsion efficiency coefficient corresponding to the stone site by combining the displacement response intensity and the real-time physiological parameters includes: The displacement response intensity is standardized to obtain the effective displacement ratio; Based on the real-time physiological parameters, the physiological risk coefficient corresponding to the stone site is calculated; Based on the effective displacement ratio and the physiological risk coefficient, the stone expulsion efficiency coefficient corresponding to the stone site is calculated.

[0045] Wherein, the effective displacement ratio is the ratio of the actual effective displacement to the expected displacement threshold in the displacement response intensity. For example, if the stone actually moves 2mm during treatment and is expected to move 3mm to pass through the narrow segment, then the effective displacement ratio is 2 / 3≈0.67. The physiological risk coefficient is a comprehensive quantitative value of the tissue tolerance risk corresponding to the stone site, calculated based on the real-time physiological parameters. For example, if the local temperature exceeds the standard by 2℃ (weight 0.4) and the muscle tension exceeds the standard by 30% (weight 0.6) in real-time monitoring, then the physiological risk coefficient = (2 / 5)×0.4+(30% / 50%)×0.6=0.16+0.36=0.52 (where 5℃ and 50% are the maximum safe deviation thresholds for temperature and muscle tension, respectively). The site correction coefficient is set according to the anatomical complexity of the stone location (e.g., 1.0 for the upper ureter, 0.9 for the middle ureter, and 0.8 for the lower ureter, correcting for the differences in stone expulsion difficulty at different sites). Furthermore, the formula for calculating the stone expulsion utility coefficient corresponding to the stone site is: Where B represents the stone expulsion efficiency coefficient corresponding to the stone location, and D represents the effective displacement ratio. Indicates the physiological risk coefficient. Indicates the correction factor for the location.

[0046] This invention, by dividing the displacement risk levels corresponding to the stone removal efficiency coefficient, can transform the abstract stone removal efficiency and potential risks into intuitive hierarchical markers, making it easy to quickly identify high-risk stages in the stone movement process (such as triggering a red warning when the coefficient is below 0.3), thereby allowing for targeted adjustment of treatment energy parameters or suspension of the operation, reducing the probability of adverse events such as stone obstruction and tissue damage, and improving the safety and controllability of treatment.

[0047] The displacement risk tier is a hierarchical range corresponding to the stone expulsion utility coefficient, divided from low to high risk. For example, a coefficient of 0.8-1.0 corresponds to a low-risk tier (safe stone expulsion), 0.4-0.8 corresponds to a medium-risk tier (requiring dynamic monitoring), and 0-0.4 corresponds to a high-risk tier (requiring intervention and adjustment). This is used to intuitively reflect the risk level during stone displacement. Optionally, the displacement risk tier corresponding to the stone expulsion utility coefficient can be divided by a preset coefficient threshold range combined with a fuzzy comprehensive evaluation method. For example, a stone expulsion utility coefficient ≥0.8 is set as Level I (low risk, no intervention required), 0.5-0.8 as Level II (medium risk, enhanced monitoring), and <0.5 as Level III (high risk, treatment parameters need adjustment). At the same time, the anatomical complexity of the stone location can be considered (e.g., the threshold for Level II can be lowered to 0.6 in ureteral strictures), and the interval boundaries can be optimized using a fuzzy clustering algorithm to make the risk tier division more in line with clinical practice.

[0048] The energy attenuation gradient calculation module 104 is used to locate the narrow channel region corresponding to the stone site and calculate the energy attenuation gradient corresponding to the narrow channel region.

[0049] This invention, by locating the narrow channel region corresponding to the stone site, can clearly identify the key obstruction location in the stone expulsion path, providing a spatial coordinate reference for precisely controlling the distribution of treatment energy in this region, avoiding the blind application of energy to non-target areas, thereby reducing the risk of damage to healthy tissue, and improving the success rate of the stone passing through the narrow channel. The narrow channel region is a tubular structure region in the expulsion path corresponding to the stone site with a lumen diameter smaller than the normal anatomical threshold.

[0050] As an embodiment of the present invention, locating the narrow passage region corresponding to the stone site includes: Acquire ultrasound images of the area corresponding to the stone location, and perform noise reduction processing on the ultrasound images of the area to obtain the target ultrasound image; The target ultrasound image is segmented into a cavity structure to obtain a cavity segmentation mask; The cavity segmentation mask is subjected to cavity diameter quantization processing to obtain cavity diameter distribution data; Threshold determination is performed on the lumen diameter distribution data, and based on the determination result, potential narrow channel areas in the stone site are identified; Boundary verification is performed on the potential narrow passage region to obtain the narrow passage region corresponding to the stone site.

[0051] The ultrasound image of the location is the original image data generated by ultrasound equipment scanning the location of the stone, such as a DICOM format tomographic image obtained by scanning the ureteral stone area with an abdominal ultrasound probe; the target ultrasound image is a cleared image obtained after preprocessing the ultrasound image of the location, such as denoising and contrast enhancement, for example, an image with 25% improved grayscale contrast between the lumen and surrounding tissues after removing noise by Gaussian filtering of the original image; the lumen segmentation mask is a binary image marking the lumen contour obtained after segmenting the lumen structure of the target ultrasound image, such as the ureteral lumen region segmented by the U-Net model. The mask is white (pixel value 1) and black (pixel value 0) in other areas; the lumen diameter distribution data is the quantified diameter data at each position along the lumen axis obtained by morphological analysis of the lumen segmentation mask. For example, a diameter value is recorded every 1 mm along the ureter axis, forming a sequence of data such as "position 0mm-diameter 5mm, position 1mm-diameter 4.8mm..."; the potential narrowing channel region is a continuous interval in the stone site where the lumen diameter is lower than the anatomical narrowing threshold. For example, if the diameter of a continuous 8mm length of a certain ureter is 3.2mm (lower than the 4mm threshold), this interval is marked as the potential narrowing channel region.

[0052] Optionally, an ultrasound image of the area corresponding to the stone site can be acquired using an integrated ultrasound probe (such as a convex array probe with a frequency of 3-5MHz) in the stone removal device. The ultrasound image of the area can be denoised to obtain a target ultrasound image. A semantic segmentation module based on deep learning (such as a U-Net model deployed in the device's edge computing unit) can be used to segment the lumen structure of the target ultrasound image to obtain a lumen segmentation mask. A morphological analysis algorithm (such as contour moment calculation and distance transformation function combined with the OpenCV library) can be used to quantize the lumen diameter of the lumen segmentation mask to obtain lumen diameter distribution data. A threshold determination can be performed on the lumen diameter distribution data using a comparison program with preset anatomical thresholds (such as a diameter-threshold comparison table embedded in the device firmware, supporting custom threshold parameter adjustment). Based on the determination result, a potential narrowing channel region in the stone site can be identified. A multimodal image fusion verification tool (such as an algorithm module that registers real-time ultrasound with preoperative CT images) can be used to perform boundary verification on the potential narrowing channel region to obtain the narrowing channel region corresponding to the stone site. For example, after the 3.5MHz convex array probe of the stone removal device acquires ultrasound images of the renal pelvis region, the target image is obtained by the Gaussian filtering module built into the device for noise reduction; the U-Net segmentation module outputs the ureteral lumen mask within 0.5 seconds; the morphological algorithm automatically calculates the diameter value of every 1mm along the lumen axis; the threshold comparison program marks the 8mm long tube segment with a diameter <4mm; finally, the continuity of the tube segment boundary is confirmed by CT-ultrasound image registration, and it is determined to be a narrow channel area.

[0053] This invention calculates the energy attenuation gradient corresponding to the narrow channel region to understand the loss pattern and transmission efficiency of therapeutic energy within the narrow channel, providing a quantitative basis for precise control of incident energy intensity. The energy attenuation gradient is the energy attenuation value per unit distance along the channel axis during the propagation of the therapeutic energy (such as shock wave or vibration energy) corresponding to the narrow channel region.

[0054] As an embodiment of the present invention, calculating the energy attenuation gradient corresponding to the narrow channel region includes: Calculate the change in impact pressure corresponding to the narrow channel region, and assign resistance weights to the narrow channel region; Calculate the rate of change of tissue viscoelasticity corresponding to the narrow channel region; Combining the change in impact pressure, the resistance weight, and the tissue viscoelasticity rate of change, the energy attenuation gradient corresponding to the narrow channel region is calculated using the following formula: in, This represents the energy decay gradient corresponding to the narrow channel region. This represents the change in impact pressure in the b-th region of the narrow passage. This represents the resistance weight of the b-th region within the narrow passage area. This represents the contraction ratio of the b-th region within the narrow channel area. Indicates the thermal conductivity coefficient of the tissue. This represents the rate of change of tissue viscoelasticity in the b-th region of the narrow channel. Represents the energy absorption constant. denoted by 'b', where 'b' represents the sequence number of the narrow passage region, and 'Q' represents the number of narrow passage regions.

[0055] Furthermore, This reflects the pressure loss when energy passes through a narrow channel. It represents the percentage contribution of resistance in different regions of the channel, used for spatial distribution correction. It reflects the absorption / scattering of energy by changes in tissue mechanical properties, makes corrections to tissue properties, and jointly characterizes the energy attenuation coupling process. The initial dimension is [ The value obtained after normalization in the formula is a dimensionless value.

[0056] It should be noted that the above energy attenuation gradient calculation formula performs a region-by-region analysis of energy attenuation in the narrow channel region through a summation form. Taking the narrow channel sub-region (sequence b) as the calculation unit, it integrates parameters such as impact pressure change, resistance weight, contraction ratio, tissue thermal conductivity coefficient, and tissue viscoelasticity change rate to construct the basic term of energy attenuation for a single sub-region. Then, an exponential term containing stone density and energy absorption constant is introduced to comprehensively consider the influence of stone characteristics on energy attenuation. Its core is to describe the difference in energy attenuation in different regions of the narrow channel through the coupling of multiple physical parameters (mechanical, thermal, viscoelastic, etc.). The accumulation of multiple regions adapts to the complex structural distribution of the channel, and the exponential term highlights the regulatory role of stone density, a key factor, on energy attenuation.

[0057] Furthermore, in practical applications, such as in the treatment of urinary system stones, this method can precisely quantify the energy reduction when a stone passes through a ureteral stenosis (e.g., energy changes under different degrees of stenosis and stone density). Compared to single-parameter assessment, it can more comprehensively reflect the role of stone expulsion energy in complex channel environments. By adjusting parameters such as the number of sub-regions and energy absorption constant, it can be adapted to the channel and stone characteristics of different patients (e.g., increasing when the stenosis is complex over multiple days, and adjusting when the stone density is high), helping to optimize the setting of stone expulsion energy parameters, improve energy utilization efficiency and stone passage effect in stone expulsion treatment, and provide a quantitative basis for the formulation of personalized stone expulsion plans.

[0058] The impact pressure change refers to the dynamic change in pressure value of the therapeutic shock wave during propagation in the narrow channel region. This is obtained by collecting pressure data at different points within the channel in real time using a pressure sensor array and calculating the difference. For example, if the pressure at the entrance of the narrow channel is 120 kPa, and it drops to 90 kPa after propagating 5 mm, the impact pressure change is 30 kPa. The resistance weight is a weighted coefficient representing the resistance of the narrow channel wall to stone movement. This is calculated by analyzing parameters such as channel diameter and wall roughness and assigning different weight values. For example, the resistance weight is set to 0.8 for a narrow section with a diameter of 3 mm and 0.5 for a diameter of 4 mm. The contraction ratio is the ratio of the diameter at the narrowest point of the narrow channel region to the diameter of the adjacent normal lumen. This is obtained by measuring and comparing the two diameters. For example, if the diameter at the narrow point is 3 mm and the diameter of the adjacent normal lumen is 6 mm, the contraction ratio is 0.5. The tissue thermal conductivity coefficient is a parameter representing the ability of the tissue surrounding the narrow channel region to transfer heat. The thermal imager collects tissue temperature change data and calculates it using the heat conduction equation. For example, if the temperature of a certain area of ​​tissue rises from 36℃ to 37℃ within 10 seconds, the calculated heat conduction coefficient is 0.5 W / (m·K). The tissue viscoelasticity change rate is the rate at which the tissue viscoelastic parameters in the narrow channel area change over time. It is obtained by monitoring tissue stress-strain changes using a dynamic mechanical analyzer and calculating the changes. For example, if the tissue elastic modulus decreases from 2 MPa to 1.5 MPa within 10 minutes, the viscoelasticity change rate is 0.05 MPa / min. The energy absorption constant is the inherent coefficient of the tissue in the narrow channel area that absorbs therapeutic energy. It is obtained by measuring the difference between incident energy and transmitted energy and normalizing it. For example, if the incident energy is 100 mJ and the transmitted energy is 30 mJ, the energy absorption constant is 0.7. The stone density is the mass per unit volume of the stone. It is obtained by measuring the volume and mass of the stone and comparing them. For example, if a stone has a mass of 0.8 g, a volume of 0.4 cm³, and a density of 2 g / cm³, it is considered to have a density of 2 g / cm³.

[0059] The stone removal processing module 105 is used to generate a safety intervention signal corresponding to the stone removal device based on the energy attenuation gradient, send the safety intervention signal to the terminal control module of the stone removal device, obtain module feedback instructions, and formulate a dynamic stone removal strategy corresponding to the stone site by combining the module feedback instructions and the displacement risk gradient, so as to perform stone removal processing on the stone site through the stone removal device and obtain the processing result.

[0060] This invention generates a safety intervention signal for the stone removal device based on the energy attenuation gradient. It transforms abstract energy transfer data into specific device control commands based on the energy attenuation pattern in narrow channels, proactively mitigating the risks of insufficient or excessive energy and providing a scientific basis for the precise operation of the stone removal device. The safety intervention signal refers to an instruction output based on an energy safety threshold to adjust the energy parameters of the stone removal device (such as "increase shockwave energy to 90mJ" or "reduce vibration frequency to 5Hz"). For example, when the energy attenuation gradient exceeds 4mJ / mm, a signal is generated to "pause treatment and adjust shockwave energy from 80mJ to 100mJ," ensuring that the energy effectively targets the stones.

[0061] As an embodiment of the present invention, generating a safety intervention signal corresponding to the stone removal device based on the energy attenuation gradient includes: Analyze the energy transfer efficiency range corresponding to the energy decay gradient; Based on the energy transfer efficiency range, the energy output level of the stone removal equipment is divided. Query the energy security data associated with the changes in the energy output level; Extract the energy regulation factors from the energy security data; Based on the energy regulation factor, a safety intervention signal corresponding to the stone removal device is generated.

[0062] The energy transfer efficiency range refers to the range of therapeutic energy transfer efficiency in narrow channel areas, defined by the energy attenuation gradient. It is divided by efficiency boundaries corresponding to the gradient value (e.g., a gradient of 1-3 mJ / mm corresponds to an efficiency of 60%-80%), reflecting the actual effectiveness range of energy transfer. For example, an energy attenuation gradient of 2 mJ / mm corresponds to an energy transfer efficiency range of 70%-75%. The energy output level refers to the grading of the energy output intensity of the lithotripsy device according to the energy transfer efficiency range. It is divided into low output, medium output, and high output levels based on efficiency, reflecting differences in the device's energy supply. For example, an efficiency range below 50% corresponds to a "high output level" (requiring increased energy output); an efficiency range above 80% corresponds to a "low output level" (allowing for reduced energy output). The energy safety data refers to a set of parameters associated with the energy output level, indicating that the stone-dissolving equipment will not damage surrounding tissues and can effectively break up stones under a specific energy output (such as maximum safe energy, energy adjustment step size, and action time). For example, the energy safety data corresponding to a high output level may include "maximum safe energy 120mJ, adjustment step size 10mJ", reflecting the energy range for safe and efficient stone breaking. The energy regulation factor refers to key parameters extracted from the energy safety data that can adjust the energy output of the stone-dissolving equipment to make it both safe and effective (such as energy amplification ratio, single action duration, and interval time). For example, at a medium output level, the energy regulation factor may be "energy amplification 15%, single action duration 0.5s, and interval 2s", used to precisely control the energy output of the equipment. Optionally, the analysis of the energy transfer efficiency range corresponding to the energy attenuation gradient can be achieved through energy attenuation model simulation technology, such as: using ANSYS software to establish a finite element model of shock wave propagation, and finally obtaining the energy transfer efficiency range corresponding to different gradients; the division of the energy output level corresponding to the stone removal device can be achieved through cluster analysis technology, such as: applying the K-means algorithm combined with Python's Scikit-learn library to classify energy efficiency data, and finally obtaining different energy output levels; the querying of energy safety data associated with the level changes in the energy output level can be achieved through data association technology, such as: using the association query function of the MongoDB database to analyze the correspondence between energy output and tissue damage, and finally obtaining the energy safety data corresponding to each level; the extraction of energy regulation factors from the energy safety data can be achieved through factor analysis technology, such as: using the principal component analysis function of SPSS software to calculate key influencing variables, and finally obtaining the energy regulation factors that dominate energy regulation; the generation of safety intervention signals corresponding to the stone removal device can be achieved through control algorithm modeling technology, such as: establishing a fuzzy control model and calculating energy deviation in real time through PLC programming, and finally outputting safety intervention signals for regulating the device.

[0063] This invention enables real-time data interaction between the device and the control terminal by sending the safety intervention signal to the terminal control module of the lithotripsy device and receiving feedback instructions from the module. This allows the terminal control module to receive and respond to energy regulation needs in a timely manner, facilitating rapid adjustment of device operating parameters. At the same time, it can verify the execution of the intervention signal, forming an instant closed loop of "monitoring-regulation-feedback", thereby improving the response speed of treatment from the perspective of device collaboration.

[0064] The terminal control module of the stone removal equipment refers to the core component of the stone removal equipment used to receive instructions and control the operating parameters of the equipment. It has signal reception, parameter adjustment and status feedback functions, and can integrate microprocessors, control circuits, etc. For example, the central control unit of an extracorporeal shock wave lithotripter can receive safety intervention signals, automatically adjust the energy, frequency and other parameters of the shock wave, and send back the execution status. The module feedback instruction refers to the execution result and equipment status data sent back by the terminal control module after receiving and executing the safety intervention signal. It includes the instruction execution status (such as whether the energy is adjusted to the correct level), equipment operating parameters (such as the current shock wave energy value), fault prompts (such as sensor abnormality), etc. For example, the module feedback "The energy increase instruction has been executed. The current shock wave energy is 95mJ. The equipment is operating normally". Optionally, the terminal control module that sends the safety intervention signal to the stone removal equipment can be implemented through industrial bus transmission technology, such as using the PROFINET bus protocol to build a high-speed data transmission link, and finally obtain the module feedback instruction containing instruction confirmation and equipment status information.

[0065] This invention combines the module feedback instructions and the displacement risk tier to formulate a dynamic stone removal strategy corresponding to the stone location. It can comprehensively consider the actual operating status of the equipment and the stone displacement risk level to accurately match energy parameters, action mode and treatment rhythm, and dynamically optimize the treatment plan to ensure efficient stone removal under safe conditions, thus achieving an upgrade from a static plan to a dynamically adapted treatment mode.

[0066] The dynamic stone removal strategy refers to a real-time adjusted treatment plan based on module feedback instructions and displacement risk levels, tailored to the stone location. This plan encompasses energy parameters (such as shockwave energy and vibration frequency), duration of action (such as single treatment time and interval), operation method (such as the location of the shockwave focal point), and risk mitigation measures. This strategy dynamically changes with feedback instructions and risk levels to adapt to the real-time treatment status. For example, if the module feedback energy has been adjusted to the target value and the displacement risk level is medium risk, the strategy can be adjusted to "maintain the current energy, fine-tune the focal point by 0.5mm, and pause for 5 seconds every 30 seconds of treatment for observation," and link the device to record treatment data, achieving precise control and dynamic risk management of the treatment process. Optionally, the dynamic stone removal strategy corresponding to the stone location can be implemented using a multi-objective optimization algorithm, such as using a genetic algorithm combined with MATLAB's Optimization Toolbox to construct a treatment parameter optimization model, ultimately obtaining a dynamic stone removal strategy that includes energy, duration, and operation method.

[0067] The present invention uses the stone removal device to perform stone removal treatment on the stone site and obtain the treatment result, thereby improving the accuracy of stone removal.

[0068] This invention performs dynamic energy adaptation analysis on the stone-expelling device based on the stone's characteristic parameters to obtain an initial combination of stone-expelling parameters. This achieves dynamic matching between energy and stone characteristics, avoiding the problems of low stone-expelling efficiency or tissue damage in traditional fixed-parameter modes. Optionally, this invention analyzes the structural stress state corresponding to the stone location based on the vibration output energy. This allows for understanding the stress on the stone and surrounding tissues under energy action, preventing damage caused by excessive stress. By querying the safe operating threshold corresponding to the structural stress state, the tolerance limits of different tissues can be clarified, providing a safety benchmark for subsequent pressure testing and ensuring that treatment effectively expels stones while protecting tissues. This invention also analyzes the displacement response intensity corresponding to the stone location based on the tissue pressure distribution data, revealing the movement trend and amplitude of the stone under vibration energy. This invention precisely captures the dynamic effects of the stone removal process while monitoring real-time physiological parameters, enabling real-time perception of the treatment's impact on the body and avoiding the pursuit of stone removal effects at the expense of tissue tolerance. By locating the narrow passage region corresponding to the stone site, this invention can clearly identify the key obstruction locations in the stone removal path, providing spatial coordinate references for precisely controlling the distribution of treatment energy in this region. This avoids energy blindly acting on non-target areas, thereby reducing the risk of damage to healthy tissue and increasing the success rate of stones passing through narrow passages. Furthermore, this invention generates safety intervention signals for the stone removal device based on the energy attenuation gradient. It can transform abstract energy transfer data into specific device control commands based on the energy attenuation law in the narrow passage region, proactively avoiding the risks of insufficient or excessive energy, and providing a scientific basis for the precise operation of the stone removal device. Therefore, it improves the accuracy of stone removal.

[0069] like Figure 3 The diagram shown is a schematic flowchart of a portable lithotripsy method provided in an embodiment of the present invention. In this embodiment, the portable lithotripsy method includes: The built-in sensors of the stone removal device worn by the patient collect the stone characteristic parameters corresponding to the stone location. Based on the stone characteristic parameters, the stone removal device is subjected to dynamic energy adaptation analysis to obtain an initial stone removal parameter combination, and the vibration output energy corresponding to the initial stone removal parameter combination is calculated. Based on the vibration output energy, the structural stress state corresponding to the stone site is analyzed, the safety action threshold corresponding to the structural stress state is queried, and based on the safety action threshold, the layered pressure detection is performed on the stone site to obtain tissue pressure distribution data. Based on the tissue pressure distribution data, the displacement response intensity corresponding to the stone site is analyzed, and the real-time physiological parameters corresponding to the stone site are monitored. Combining the displacement response intensity and the real-time physiological parameters, the stone expulsion efficiency coefficient corresponding to the stone site is calculated, and the displacement risk level corresponding to the stone expulsion efficiency coefficient is divided. Locate the narrow channel region corresponding to the stone site and calculate the energy attenuation gradient corresponding to the narrow channel region; Based on the energy attenuation gradient, a safety intervention signal corresponding to the stone removal device is generated and sent to the terminal control module of the stone removal device to obtain module feedback instructions. Combining the module feedback instructions and the displacement risk gradient, a dynamic stone removal strategy corresponding to the stone site is formulated so as to perform stone removal treatment on the stone site through the stone removal device and obtain the treatment result.

[0070] In the several embodiments provided by this invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0071] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A portable lithotripsy system, characterized in that, The portable stone removal system includes: an output energy calculation module, a pressure detection module, a displacement risk tier division module, an energy attenuation gradient calculation module, and a stone removal processing module; The output energy calculation module is used to collect stone characteristic parameters corresponding to the stone site using the built-in sensor of the stone removal device worn by the patient, perform dynamic energy adaptation analysis on the stone removal device based on the stone characteristic parameters, obtain an initial stone removal parameter combination, and calculate the vibration output energy corresponding to the initial stone removal parameter combination. The pressure detection module is used to analyze the structural stress state corresponding to the stone site based on the vibration output energy, query the safety action threshold corresponding to the structural stress state, and perform layered pressure detection on the stone site based on the safety action threshold to obtain tissue pressure distribution data. The displacement risk tier classification module is used to analyze the displacement response intensity corresponding to the stone site based on the tissue pressure distribution data, monitor the real-time physiological parameters corresponding to the stone site, and calculate the stone expulsion utility coefficient corresponding to the stone site by combining the displacement response intensity and the real-time physiological parameters, and classify the displacement risk tier corresponding to the stone expulsion utility coefficient. The energy attenuation gradient calculation module is used to locate the narrow channel region corresponding to the stone site and calculate the energy attenuation gradient corresponding to the narrow channel region. The stone removal module is used to generate a safety intervention signal corresponding to the stone removal device based on the energy attenuation gradient, send the safety intervention signal to the terminal control module of the stone removal device, obtain module feedback instructions, and formulate a dynamic stone removal strategy corresponding to the stone site by combining the module feedback instructions and the displacement risk gradient, so as to perform stone removal treatment on the stone site through the stone removal device and obtain the treatment result.

2. The portable lithotripsy system as described in claim 1, characterized in that, The dynamic energy adaptation analysis of the stone removal device based on the stone characteristic parameters is used to obtain an initial combination of stone removal parameters, including: Analyze the initial stone-forming conditions corresponding to the stone characteristic parameters, and query the basic energy settings of the stone-removing device under the initial stone-forming conditions; Analyze the output response characteristics corresponding to the basic energy settings, and based on the output response characteristics, identify the energy distribution data corresponding to the stone removal device; Based on the energy distribution data, dynamic energy adaptation analysis is performed on the stone removal equipment to obtain the initial combination of stone removal parameters.

3. The portable lithotripsy system as described in claim 1, characterized in that, The calculation of the vibration output energy corresponding to the initial combination of stone removal parameters includes: Based on the stone characteristic parameters, the treatment target in the initial stone expulsion parameter combination is determined; Identify the start and end times of the vibration output process in the initial combination of stone discharge parameters; The amplitude and frequency values ​​of the treatment target at different time points are extracted from the initial combination of stone expulsion parameters. By combining the start time, the end time, the amplitude value, and the frequency value, the vibration output energy corresponding to the initial combination of stone discharge parameters is calculated.

4. The portable lithotripsy system as described in claim 1, characterized in that, Based on the safety threshold, the layered pressure detection of the stone site is performed to obtain tissue pressure distribution data, including: Based on preset human tissue characteristics, the stone site is divided into layers to obtain tissue layers; The pressure sensor in the stone removal device is used to collect and process data from the tissue layers to obtain initial tissue pressure data. The initial tissue pressure data is subjected to spatiotemporal calibration to obtain calibrated tissue pressure data, and a pressure distribution field corresponding to the calibrated tissue pressure data is constructed. Based on the organizational hierarchy, the pressure distribution field is visualized and transformed to obtain a layered pressure heat map; Based on the aforementioned safety threshold, anomalies are marked in the pressure distribution field to obtain a pressure anomaly map; Extract the abnormal pressure features corresponding to the abnormal pressure map, and calculate the risk coefficient corresponding to the tissue level based on the abnormal pressure features; The risk coefficient, the stratified pressure heat map, and the abnormal pressure characteristics are integrated and processed to obtain tissue pressure distribution data.

5. The portable lithotripsy system as described in claim 1, characterized in that, The analysis of the displacement response intensity corresponding to the stone site based on the tissue pressure distribution data includes: Extract the pressure gradient features from the tissue pressure distribution data; Based on the pressure gradient characteristics, the stress-bearing area corresponding to the stone location is determined; Quantify the pressure impulse index corresponding to the force-affected area; Based on the pressure impulse index, the initial displacement trend corresponding to the stone site is evaluated. Based on the initial displacement trend, the displacement response intensity corresponding to the stone site is analyzed.

6. The portable lithotripsy system as described in claim 1, characterized in that, The calculation of the stone expulsion efficiency coefficient corresponding to the stone site, combining the displacement response intensity and the real-time physiological parameters, includes: The displacement response intensity is standardized to obtain the effective displacement ratio; Based on the real-time physiological parameters, the physiological risk coefficient corresponding to the stone site is calculated; Based on the effective displacement ratio and the physiological risk coefficient, the stone expulsion efficiency coefficient corresponding to the stone site is calculated.

7. The portable lithotripsy system as described in claim 1, characterized in that, The location of the narrow passage area corresponding to the stone site includes: Acquire ultrasound images of the area corresponding to the stone location, and perform noise reduction processing on the ultrasound images of the area to obtain the target ultrasound image; The target ultrasound image is segmented into a cavity structure to obtain a cavity segmentation mask; The cavity segmentation mask is subjected to cavity diameter quantization processing to obtain cavity diameter distribution data; Threshold determination is performed on the lumen diameter distribution data, and based on the determination result, potential narrow channel areas in the stone site are identified; Boundary verification is performed on the potential narrow passage region to obtain the narrow passage region corresponding to the stone site.

8. The portable lithotripsy system as described in claim 1, characterized in that, The calculation of the energy attenuation gradient corresponding to the narrow channel region includes: Calculate the change in impact pressure corresponding to the narrow channel region, and assign resistance weights to the narrow channel region; Calculate the rate of change of tissue viscoelasticity corresponding to the narrow channel region; By combining the change in impact pressure, the resistance weight, and the tissue viscoelasticity change rate, the energy attenuation gradient corresponding to the narrow channel region is calculated.

9. The portable lithotripsy system as described in claim 1, characterized in that, The step of generating a safety intervention signal corresponding to the stone removal device based on the energy attenuation gradient includes: Analyze the energy transfer efficiency range corresponding to the energy decay gradient; Based on the energy transfer efficiency range, the energy output level of the stone removal equipment is divided. Query the energy security data associated with the changes in the energy output level; Extract the energy regulation factors from the energy security data; Based on the energy regulation factor, a safety intervention signal corresponding to the stone removal device is generated.

10. A portable method for removing kidney stones, characterized in that, The method includes: The built-in sensors of the stone removal device worn by the patient collect the stone characteristic parameters corresponding to the stone location. Based on the stone characteristic parameters, the stone removal device is subjected to dynamic energy adaptation analysis to obtain an initial stone removal parameter combination, and the vibration output energy corresponding to the initial stone removal parameter combination is calculated. Based on the vibration output energy, the structural stress state corresponding to the stone site is analyzed, the safety action threshold corresponding to the structural stress state is queried, and based on the safety action threshold, the layered pressure detection is performed on the stone site to obtain tissue pressure distribution data. Based on the tissue pressure distribution data, the displacement response intensity corresponding to the stone site is analyzed, and the real-time physiological parameters corresponding to the stone site are monitored. Combining the displacement response intensity and the real-time physiological parameters, the stone expulsion efficiency coefficient corresponding to the stone site is calculated, and the displacement risk level corresponding to the stone expulsion efficiency coefficient is divided. Locate the narrow channel region corresponding to the stone site and calculate the energy attenuation gradient corresponding to the narrow channel region; Based on the energy attenuation gradient, a safety intervention signal corresponding to the stone removal device is generated and sent to the terminal control module of the stone removal device to obtain module feedback instructions. Combining the module feedback instructions and the displacement risk gradient, a dynamic stone removal strategy corresponding to the stone site is formulated so as to perform stone removal treatment on the stone site through the stone removal device and obtain the treatment result.

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