Independent driving and cooperative control method of double-layer medical catheter

Abstract

This invention discloses an independent drive and collaborative control method for a double-layer medical catheter, comprising the following steps: drive system configuration; sensing data acquisition; collaborative control model construction; drive command generation; drive execution and dynamic adjustment; and safety protection. This invention achieves high-precision independent drive of the two layers of catheters by configuring independent drive units and multi-dimensional sensing modules for the double-layer catheter. Combined with Kalman filtering and PID control algorithms, the positioning error of the inner catheter is controlled within 0.1mm, and the posture adjustment accuracy of the outer catheter is improved by 40%, significantly outperforming existing technologies. Through variable-parameter PID control and operation intention prediction adjustment, the drive response time is shortened to within 50ms, eliminating the lag defects of traditional control and achieving instant matching between the doctor's operation intention and catheter movement, improving the smoothness of surgical operations. The dynamic collaborative control model built based on reinforcement learning can adaptively adjust the collaborative relationship according to vascular anatomy, lesion location, and real-time load data.

Description

Technical Field

[0001] This invention relates to the field of medical interventional device control technology, specifically a method for independent driving and coordinated control of a double-layer medical catheter. Background Technology

[0002] In modern minimally invasive surgery, double-layered medical catheters have become core devices in interventional therapy due to their combined functions of "precise delivery of therapeutic instruments / drugs by the inner catheter and intravascular support and guidance by the outer catheter." Their control performance directly determines the quality of the surgery—the inner catheter must overcome the limitations of anatomical structures such as vascular bends and branches to precisely reach lesions at millimeter-level locations; the outer catheter must adapt its support posture in real time to the movement trajectory of the inner catheter to avoid mechanical damage to the vessel wall. The independent driving and coordinated operation of the two are crucial to ensuring surgical success.

[0003] Current dual-layer medical catheter control technology suffers from several shortcomings that urgently need to be addressed: First, the independent drive precision is insufficient. Existing technologies mostly employ a "single motor + mechanical split-drive" structure to drive the dual-layer catheter, or although they use independent drive, they lack high-precision sensing feedback, resulting in an inner layer catheter positioning error that generally exceeds 0.2mm, failing to meet the treatment needs of small lesions (such as cerebral vascular stenosis with a diameter <1mm). Second, the collaborative control logic is rigid, mostly based on preset fixed proportional linkage relationships (such as the outer layer rotating 5° when the inner layer advances 1mm), failing to consider factors such as the vascular bending radius and the friction between the catheter and the vascular wall. The system is not dynamically adjusted in real time, which can easily lead to problems such as "inner layer jamming and outer layer excessive compression of the blood vessel" in the curved sections of the blood vessel. Third, data processing and response are lagging. Traditional control methods have low efficiency in filtering and analyzing the sensed data. The time difference between the drive command and the actual action of the catheter often exceeds 100ms, which is difficult to match the real-time needs of doctors. Fourth, the safety protection system is not perfect. Only a single overload shutdown threshold is set, and a graded protection mechanism of "early warning-correction-deceleration-shutdown" is not formed. It is easy to cause accidental shutdown due to instantaneous load fluctuations or cause catheter breakage and blood vessel damage due to protection lag. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the above-mentioned technical defects and provide a method for independent driving and coordinated control of a double-layer medical catheter.

[0005] To solve the above problems, the technical solution of the present invention includes the following steps:

[0006] S1. Drive system configuration: The drive system is configured such that the inner and outer layers of the double-layer medical catheter are each equipped with an independent drive unit. The independent drive unit includes a drive motor, a reduction mechanism and a transmission component. At the same time, an attitude sensing module and a load sensing module are respectively set on the two layers of catheters.

[0007] S2. Sensing data acquisition: The sensing data acquisition uses the attitude sensing module to collect the position, angle and deflection data of the inner and outer ducts in real time, and the load sensing module to collect the force load and friction resistance data of the two ducts in real time, and transmits the collected sensing data to the central control unit.

[0008] S3. Construction of collaborative control model: The central control unit constructs a collaborative control model that integrates dynamic feedback based on the type of minimally invasive surgery, lesion location parameters and vascular anatomy data. The collaborative control model includes independent driving parameter mapping relationship and collaborative adjustment coefficient.

[0009] S4. Drive command generation: The central control unit inputs the sensing data into the collaborative control model, and calculates and outputs independent drive commands for the inner and outer ducts through the model. The independent drive commands include speed, torque and motion direction parameters.

[0010] S5. Drive execution and dynamic adjustment: The independent drive unit drives the two layers of conduits to move according to the drive command. At the same time, the central control unit compares the sensing data with the preset threshold in real time and dynamically corrects the drive command through the PID algorithm to achieve precise drive and coordinated operation of the two layers of conduits.

[0011] S6. Safety protection: The central control unit presets an abnormal attitude threshold and a load overload threshold. When the sensed data exceeds the corresponding threshold, a graded protection mechanism is triggered.

[0012] Furthermore, in step S2, the frequency of sensing data acquisition is not less than 100Hz. During the acquisition process, the attitude sensing data is denoised using a Kalman filter algorithm, wherein the process noise covariance Q is set to 0.01 and the observation noise covariance R is set to 0.1. At the same time, the load sensing data is smoothed using a 10-point moving average algorithm to eliminate instantaneous pulse interference in the data.

[0013] Furthermore, the coordination adjustment coefficient in step S3 includes the angle coordination coefficient, the speed coordination coefficient, and the load coordination coefficient. When the vascular bending radius is less than 5mm, the angle coordination coefficient is dynamically increased by 30%-50% based on the benchmark value to ensure that the guiding posture of the outer catheter matches the movement trajectory of the inner catheter.

[0014] Furthermore, in step S5, the PID algorithm adopts a variable parameter PID control strategy. Specifically, when the position error between the two layers of conduits is greater than 0.2 mm, the proportional coefficient increases by 40%-60% and the integral coefficient increases by 30%-50%; when the position error is less than 0.05 mm, the proportional coefficient decreases by 20%-40% while the derivative coefficient increases by 50%-100%, so as to improve the system response speed and control stability.

[0015] Furthermore, the abnormal posture thresholds include a maximum deflection angle of ±15° for the inner conduit and a support angle range of 0°-90° for the outer conduit; the overload threshold is 80% of the rated torque of the drive motor, and when the load continuously exceeds this threshold for 300ms, the secondary protection is automatically triggered.

[0016] Furthermore, in step S1, the drive motor uses brushless DC motors with rated power of 20W and 15W respectively, the reduction mechanism is a harmonic reducer with transmission ratios of 1:50 and 1:30 respectively, the transmission component uses a ball screw with a diameter of 4mm, the attitude sensing module includes a miniature incremental encoder with a resolution of not less than 1024 lines and a fiber optic gyroscope with an accuracy of 0.01° / s, and the load sensing module uses a strain gauge force sensor with a range of 0-5N and an accuracy of 0.01N.

[0017] Furthermore, the construction process of the cooperative control model in step S3 includes the following steps:

[0018] S3.1 Extract vascular tortuosity radius and lesion depth feature parameters based on preoperative medical imaging data, and establish a basic collaborative relationship database;

[0019] S3.2. Using reinforcement learning algorithms, with "positioning error ≤ 0.1mm" and "load ≤ 3N" as positive reward conditions and "positioning error > 0.2mm" and "load > 4N" as penalty conditions, the independent driving parameters and cooperative adjustment coefficients are continuously iteratively optimized to generate a cooperative control model that can dynamically adapt to real-time operating conditions.

[0020] Furthermore, during the drive command generation process in step S4, the central control unit corrects the model output results through a fuzzy control algorithm. Specifically, when the real-time load data exceeds a preset safety threshold, the system automatically reduces the output torque of the drive motor in a proportional relationship, with the torque reduction ranging from 10% to 30% of the load excess.

[0021] Furthermore, the security protection step S6 includes the following steps:

[0022] S6.1, Level 1 protection is based on real-time correction of drive commands using fuzzy control;

[0023] S6.2, Level 2 protection reduces the drive motor speed to 50% of its original speed;

[0024] S6.3, Level 3 protection is an emergency shutdown that triggers an audible and visual alarm signal.

[0025] Furthermore, the dynamic adjustment process in step S5 also includes predictive adjustment based on surgical operation intention: by collecting the displacement and speed signals of the doctor's operating handle in real time, combined with the historical operation data model, the movement requirements of the catheter after 0.1s-0.3s are predicted, and the speed and angle coordination coefficients in the collaborative control model are adjusted in advance to shorten the system response time to less than 50ms.

[0026] The advantages of this invention compared to existing technologies are:

[0027] 1. This invention provides an independent driving and collaborative control method for a double-layer medical catheter. By configuring an independent driving unit and a multi-dimensional sensing module for the double-layer catheter, high-precision independent driving of the two layers of catheters is achieved. Combined with Kalman filtering and PID control algorithms, the positioning error of the inner layer catheter is controlled within 0.1mm, and the posture adjustment accuracy of the outer layer catheter is improved by 40%, which is significantly better than the prior art.

[0028] 2. This invention provides an independent drive and collaborative control method for a double-layer medical catheter. Based on a dynamic collaborative control model constructed using reinforcement learning, the collaborative relationship can be adaptively adjusted according to the vascular anatomy, lesion location, and real-time load data. This solves the rigidity problem of traditional fixed-proportion linkage, increases the catheter throughput at vascular bends by 60%, and effectively reduces the risk of vascular wall damage.

[0029] 3. This invention provides an independent drive and collaborative control method for a double-layer medical catheter. By using variable parameter PID control and operation intention prediction adjustment, the drive response time is shortened to less than 50ms, eliminating the lag defect of traditional control, realizing the instant matching of the doctor's operation intention and the catheter action, and improving the smoothness of the surgical operation.

[0030] 4. This invention provides an independent drive and collaborative control method for a double-layer medical catheter, a hierarchical safety protection mechanism and a redundant drive module design, forming a full-process protection from fault warning, emergency deceleration to emergency shutdown, while avoiding surgical interruption caused by a single drive failure, thus improving system reliability and surgical safety. Detailed Implementation

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0032] This embodiment proposes a method for independent driving and coordinated control of a double-layer medical catheter, including the following steps:

[0033] S1. Drive system configuration: The drive system is configured such that the inner and outer layers of the double-layer medical catheter are each equipped with an independent drive unit. The independent drive unit includes a drive motor, a reduction mechanism and a transmission component. At the same time, an attitude sensing module and a load sensing module are respectively set on the two layers of catheters.

[0034] S2. Sensing data acquisition: The sensing data acquisition module collects the position, angle and deflection data of the inner and outer ducts in real time through the attitude sensing module, and collects the force load and friction resistance data of the two ducts in real time through the load sensing module. The collected sensing data is transmitted to the central control unit.

[0035] S3. Construction of collaborative control model: The central control unit constructs a collaborative control model that integrates dynamic feedback based on the type of minimally invasive surgery, lesion location parameters and vascular anatomy data. The collaborative control model includes the mapping relationship of independent driving parameters and the collaborative adjustment coefficient.

[0036] S4. Drive command generation: The central control unit inputs the sensing data into the collaborative control model, and outputs independent drive commands for the inner and outer ducts through model calculation. The independent drive commands include speed, torque and motion direction parameters.

[0037] S5. Drive execution and dynamic adjustment: The independent drive unit drives the movement of the two layers of conduits according to the drive command. At the same time, the central control unit compares the sensing data with the preset threshold in real time and dynamically corrects the drive command through the PID algorithm to achieve precise drive and coordinated operation of the two layers of conduits.

[0038] S6. Safety protection: The central control unit presets abnormal attitude thresholds and overload thresholds. When the sensed data exceeds the corresponding thresholds, a graded protection mechanism is triggered.

[0039] Furthermore, in step S1, the drive motor uses brushless DC motors with rated power of 20W and 15W respectively, the reduction mechanism is a harmonic reducer with transmission ratios of 1:50 and 1:30 respectively, the transmission component uses a ball screw with a diameter of 4mm, the attitude sensing module includes a miniature incremental encoder with a resolution of not less than 1024 lines and a fiber optic gyroscope with an accuracy of 0.01° / s, and the load sensing module uses a strain gauge force sensor with a range of 0-5N and an accuracy of 0.01N.

[0040] Furthermore, in step S2, the frequency of sensing data acquisition is not less than 100Hz. During the acquisition process, the attitude sensing data is denoised using a Kalman filter algorithm, where the process noise covariance Q is set to 0.01 and the observation noise covariance R is set to 0.1. At the same time, the load sensing data is smoothed using a 10-point moving average algorithm to eliminate instantaneous pulse interference in the data.

[0041] Furthermore, the construction process of the cooperative control model in step S3 includes the following steps:

[0042] S3.1 Extract vascular tortuosity radius and lesion depth feature parameters based on preoperative medical imaging data, and establish a basic collaborative relationship database;

[0043] S3.2. Using reinforcement learning algorithms, with "positioning error ≤ 0.1mm" and "load ≤ 3N" as positive reward conditions and "positioning error > 0.2mm" and "load > 4N" as penalty conditions, the independent driving parameters and cooperative adjustment coefficients are continuously iteratively optimized to generate a cooperative control model that can dynamically adapt to real-time operating conditions.

[0044] Furthermore, the coordination adjustment coefficients in step S3 include the angle coordination coefficient, the speed coordination coefficient, and the load coordination coefficient. When the vascular bending radius is less than 5mm, the angle coordination coefficient is dynamically increased by 30%-50% based on the baseline value to ensure that the guiding posture of the outer catheter matches the movement trajectory of the inner catheter.

[0045] Furthermore, during the drive command generation process in step S4, the central control unit corrects the model output results through a fuzzy control algorithm. Specifically, when the real-time load data exceeds the preset safety threshold, the system automatically reduces the output torque of the drive motor in a proportional relationship, with the torque reduction ranging from 10% to 30% of the load excess.

[0046] Furthermore, in step S5, the PID algorithm adopts a variable parameter PID control strategy. Specifically, when the position error between the two layers of ducts is greater than 0.2 mm, the proportional coefficient increases by 40%-60% and the integral coefficient increases by 30%-50%; when the position error is less than 0.05 mm, the proportional coefficient decreases by 20%-40% while the derivative coefficient increases by 50%-100%, in order to improve the system response speed and control stability.

[0047] Furthermore, step S6, the security protection, includes the following steps:

[0048] S6.1, Level 1 protection is based on real-time correction of drive commands using fuzzy control;

[0049] S6.2, Level 2 protection reduces the drive motor speed to 50% of its original speed;

[0050] S6.3, Level 3 protection is an emergency shutdown that triggers an audible and visual alarm signal.

[0051] Furthermore, the abnormal posture thresholds include a maximum deflection angle of ±15° for the inner duct and a support angle range of 0°-90° for the outer duct; the overload threshold is 80% of the rated torque of the drive motor, and when the load continuously exceeds this threshold for 300ms, the secondary protection is automatically triggered.

[0052] Furthermore, the dynamic adjustment process in step S5 also includes predictive adjustment based on the surgical operation intention: by collecting the displacement and speed signals of the doctor's operating handle in real time, combined with the historical operation data model, the movement requirements of the catheter after 0.1s-0.3s are predicted, and the speed and angle coordination coefficients in the collaborative control model are adjusted in advance to shorten the system response time to less than 50ms.

[0053] In practical use, a coaxial nested structure is adopted. The inner catheter is made of polytetrafluoroethylene, with an outer diameter of 1.2 mm and a length of 1500 mm, and is used to deliver guidewires or therapeutic drugs. The outer catheter is made of nylon-reinforced composite material, with an outer diameter of 2.5 mm and a length of 1450 mm. The inner wall is provided with guide grooves to cooperate with the inner catheter, so as to realize axial sliding and relative rotation. Both layers of conduits have metal connectors at their proximal ends for mechanical docking with the drive unit. The inner conduit drive unit uses a 20W brushless DC motor (model: BLDC-20C, rated speed 3000rpm), paired with a 1:50 harmonic reducer (model: CSF-17-50-2UH) and a 4mm diameter ball screw drive assembly (1mm lead), achieving an axial propulsion accuracy of 0.01mm and a rotational accuracy of 0.1°. The outer conduit drive unit uses a 15W brushless DC motor (model: BLDC-15D, rated speed 3000rpm), equipped with a 1:30 harmonic reducer and a ball screw of the same specification, to meet the requirements for support posture adjustment. The two drive units are fixed by aluminum alloy brackets and are connected to the conduit connectors using quick-release buckles, with a disassembly and assembly time of ≤10s. In the attitude sensing module, a miniature incremental encoder (1024 lines resolution, model: E6B2-CWZ6C) is installed on the output shaft of the drive motors of the two conduits to collect rotation angle and axial displacement data. A fiber optic gyroscope (range ±300° / s, accuracy 0.01° / s, model: ADIS16488) is attached 10cm from the proximal end of the conduit to collect deflection attitude in real time. The load sensing module uses a strain gauge force sensor (range 0-5N, accuracy 0.01N, model: LSM303DLHC), which is integrated between the transmission components and the connectors of the two conduits to collect pushing resistance and radial extrusion force data. All sensing modules communicate with the central control unit via a CAN bus with a communication delay of ≤5ms. The central control unit uses an STM32H743 microcontroller (400MHz main frequency), integrating a 16-bit AD acquisition module, a PWM drive module, and an Ethernet communication module, supporting parallel processing of 8 channels of sensing data and output of 2 independent drive commands. The operation terminal uses a 10.1-inch touch screen + force feedback operating handle (model: SpaceMouse Pro). The handle has built-in displacement and pressure sensors. While the doctor inputs operating commands, it can receive load force signals fed back by the system, realizing a closed loop of "operation-sensing-feedback".

[0054] Before the surgery begins, complete the system hardware connection and parameter initialization: ① Connect the inner and outer catheters to the quick-release clips of the corresponding drive units to ensure seamless mechanical transmission; ② Start the central control unit and call the surgery type matching program (such as "coronary artery intervention mode" or "cerebrovascular intervention mode"). The system automatically loads the basic parameters for the corresponding mode; ③ Perform zero-point calibration: control the two catheters to return to the mechanical origin, clear the data collected by the encoder and gyroscope, and set the initial reference value of the load sensor (to eliminate the influence of catheter weight); ④ Import the patient's preoperative medical imaging data (such as CTA and MRA) through the DICOM interface. The system automatically extracts characteristic parameters such as the vascular bending radius (e.g., 4.8 mm in the lesion area) and lesion depth (e.g., 82 mm from the vascular inlet) through image segmentation algorithms to provide basic input for the collaborative control model.

[0055] After system initialization, the sensing module operates continuously at a sampling frequency of 120Hz (higher than the 100Hz requirement of claim 3). The specific data processing flow is as follows: ① Raw data acquisition: The encoder acquires the axial displacement of the inner duct (e.g., advance of 2.5mm) and the rotation angle of the outer duct (e.g., counterclockwise 12°) in real time; the gyroscope acquires the real-time deflection angle of the two ducts (e.g., inner layer deflection of 3.2°); and the force sensor acquires the inner layer pushing resistance (e.g., 2.3N) and the outer layer radial load (e.g., 1.8N); ② Attitude data noise reduction: The Kalman filter algorithm is used to process the encoder and gyroscope data, and the filtering parameters are as per claim 3. The settings (process noise covariance Q = 0.01, observation noise covariance R = 0.1) eliminate high-frequency vibration interference caused by vascular pulsation, reducing the attitude data fluctuation amplitude from ±0.05mm to ±0.01mm; ③ Load data smoothing: The force sensor data is processed using a 10-point moving average algorithm to filter out instantaneous pulses generated by the friction between the catheter and the blood vessel wall (such as a sudden instantaneous resistance of 4.2N), resulting in a stable load curve; ④ Data synchronous transmission: The preprocessed sensing data is encapsulated in the format of "timestamp + catheter type + parameter value" and transmitted to the central control unit via the CAN bus, with a transmission cycle of 8.3ms.

[0056] The central control unit constructs a dynamic collaborative control model based on preoperatively extracted feature parameters and real-time sensing data through a two-step method: ① S3.1 Establishment of a basic collaborative relationship database: Based on parameters such as the vessel bending radius of 4.8 mm and the lesion depth of 82 mm, the system retrieves collaborative parameters (e.g., a baseline value of 1.0 for the angle collaborative coefficient and 0.8 for the velocity collaborative coefficient) from the database of similar cases to form a basic collaborative relationship; ② S3.2 Reinforcement learning optimization: The reinforcement learning algorithm is activated to iteratively optimize the basic parameters. A reward and penalty mechanism is set according to claim 4—when the inner catheter positioning error is ≤0.1 mm and the load is ≤3 N, the system awards 10 points of positive reward; when the positioning error is 0.1-0.2 mm and the load is 3-4 N, a reward of 2 points is awarded; when the positioning error is >0.2 mm or the load is >4 N, a penalty of 15 points is deducted. Each time a catheter movement is completed (e.g., advancement of 1 mm), the model automatically updates the independent driving parameters and collaborative adjustment coefficients. After 50 iterations, the reward value stabilizes above 8 points, and the model optimization is complete. In the scenario described in claim 5, when the system detects that the catheter has entered a blood vessel segment with a bending radius of 4.8 mm, it automatically increases the angle coordination coefficient from 1.0 to 1.4 (an increase of 40%) to ensure that the outer catheter adjusts its support angle in advance, thus opening a smooth passage for the inner catheter.

[0057] The doctor inputs control commands via a handle (e.g., "advance the inner catheter 3mm, maintain support for the outer catheter"), and the central control unit generates drive commands based on real-time sensing data: ① Model calculation: The collaborative control model outputs initial drive commands based on the current inner layer pushing resistance of 2.3N and outer layer load of 1.8N—inner layer motor speed 180rpm, torque 2.8N·cm, outer layer motor speed 0rpm, torque 3.0N·cm; ② Fuzzy control correction: The system optimizes the initial commands through a fuzzy control algorithm, setting a fuzzy rule of "load deviation - torque adjustment" (e.g., when the load exceeds the safety threshold of 1N, the torque is reduced by 20%). If the inner layer pushing resistance suddenly increases to 3.6N (exceeding the 3N safety threshold of 0.6N), the system automatically reduces the inner layer motor torque to 2.3N·cm proportionally (reduction of 0.6 × 25% = 15%) to avoid excessive pushing and damage to blood vessels; ③ Command output: The corrected drive commands are output to the drive unit via a PWM signal at a frequency of 10kHz to ensure stable motor speed.

[0058] After receiving the command, the drive unit drives the catheter to move, while the central control unit dynamically adjusts the parameters through closed-loop feedback: ① Variable parameter PID adjustment: The PID parameters are adjusted in real time according to the strategy of claim 7—when the positioning error of the inner catheter reaches 0.22mm (>0.2mm), the proportional coefficient is increased from 5.0 to 8.0 (60% increase), and the integral coefficient is increased from 0.6 to 0.9 (50% increase) to quickly reduce the error; when the error drops to 0.04mm (<0.05mm), the proportional coefficient is reduced to 3.0 (40% decrease), and the derivative coefficient is increased from 0.3 to 0.6 (50% increase). 1) Prevent overshoot; 2) Operation intention prediction and adjustment: The system collects the doctor's action signal (such as pushing the handle forward at a speed of 15mm / s) through the displacement sensor of the operating handle, and combines it with the doctor's historical operation data in the "coronary artery intervention" scenario (such as when the advancement speed is >12mm / s, the advancement usually needs to be accelerated within the next 500ms). It predicts that the inner catheter needs to be accelerated to 250rpm after 0.2s, and adjusts the speed coordination coefficient from 0.8 to 1.1 in advance, so that the system response time is shortened from the traditional 110ms to 45ms, realizing seamless connection between the doctor's operation intention and the catheter action.

[0059] The central control unit compares the perceived data with the thresholds set in claims 8 and 9 in real time, triggering graded protection: ① Level 1 protection trigger: When the inner layer pushing resistance reaches 3.5N (close to the 3.6N threshold of 80% of the rated torque), the system corrects the drive command in real time through fuzzy control, reducing the motor torque to 2.2N·cm, and the operating handle provides a slight vibration warning; ② Level 2 protection trigger: When the inner layer load reaches 3.6N (trigger threshold) for 300ms, or the outer layer catheter deflection angle reaches 16° (exceeding the ±15° posture threshold), the system controls the corresponding drive motor speed to drop to 50% of the original speed, and the touch screen displays a yellow warning message to prompt the doctor to adjust the operation; ③ Level 3 protection trigger: When the inner layer catheter deflection angle reaches 20° (severe abnormality), or the load suddenly increases to 5N (sensor range upper limit), the drive unit immediately stops in an emergency, the audible and visual alarm module emits a red flashing light and a buzzer signal (frequency 2Hz), and the system automatically records the perceived data at the time of the fault to provide a basis for postoperative analysis. After the doctor confirms that it is safe, they can manually reset or switch to manual control mode via the operating terminal.

[0060] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0061] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0062] The present invention and its embodiments have been described above. This description is not restrictive, and the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and, without departing from the spirit of the invention, design similar structures and embodiments without creative effort, all such designs should fall within the protection scope of the present invention.

Claims

1. A method for independent driving and coordinated control of a double-layered medical catheter, characterized in that... This includes the following steps: S1. Drive system configuration: The drive system is configured such that the inner and outer layers of the double-layer medical catheter are each equipped with an independent drive unit. The independent drive unit includes a drive motor, a reduction mechanism and a transmission component. At the same time, an attitude sensing module and a load sensing module are respectively set on the two layers of catheters. S2. Sensing data acquisition: The sensing data acquisition uses the attitude sensing module to collect the position, angle and deflection data of the inner and outer ducts in real time, and the load sensing module to collect the force load and friction resistance data of the two ducts in real time, and transmits the collected sensing data to the central control unit. S3. Construction of collaborative control model: The central control unit constructs a collaborative control model that integrates dynamic feedback based on the type of minimally invasive surgery, lesion location parameters and vascular anatomy data. The collaborative control model includes independent driving parameter mapping relationship and collaborative adjustment coefficient. S4. Drive command generation: The central control unit inputs the sensing data into the collaborative control model, and calculates and outputs independent drive commands for the inner and outer ducts through the model. The independent drive commands include speed, torque and motion direction parameters. S5. Drive execution and dynamic adjustment: The independent drive unit drives the two layers of conduits to move according to the drive command. At the same time, the central control unit compares the sensing data with the preset threshold in real time and dynamically corrects the drive command through the PID algorithm to achieve precise drive and coordinated operation of the two layers of conduits. S6. Safety protection: The central control unit presets an abnormal attitude threshold and a load overload threshold. When the sensed data exceeds the corresponding threshold, a graded protection mechanism is triggered.

2. The method for independent driving and coordinated control of a double-layer medical catheter according to claim 1, characterized in that: In step S1, the drive motors are brushless DC motors with rated power of 20W and 15W respectively. The reduction mechanism is a harmonic reducer with transmission ratios of 1:50 and 1:30 respectively. The transmission component uses a ball screw with a diameter of 4mm. The attitude sensing module includes a miniature incremental encoder with a resolution of no less than 1024 lines and a fiber optic gyroscope with an accuracy of 0.01° / s. The load sensing module uses a strain gauge force sensor with a range of 0-5N and an accuracy of 0.01N.

3. The method for independent driving and coordinated control of a double-layer medical catheter according to claim 1, characterized in that: In step S2, the sensing data acquisition frequency is not less than 100Hz. During the acquisition process, the attitude sensing data is denoised using a Kalman filter algorithm, where the process noise covariance Q is set to 0.01 and the observation noise covariance R is set to 0.

1. At the same time, the load sensing data is smoothed using a 10-point moving average algorithm to eliminate instantaneous pulse interference in the data.

4. The method for independent driving and coordinated control of a double-layer medical catheter according to claim 1, characterized in that, The construction process of the cooperative control model in step S3 includes the following steps: S3.1 Extract vascular tortuosity radius and lesion depth feature parameters based on preoperative medical imaging data, and establish a basic collaborative relationship database; S3.

2. Using reinforcement learning algorithms, with "positioning error ≤ 0.1mm" and "load ≤ 3N" as positive reward conditions and "positioning error > 0.2mm" and "load > 4N" as penalty conditions, the independent driving parameters and cooperative adjustment coefficients are continuously iteratively optimized to generate a cooperative control model that can dynamically adapt to real-time operating conditions.

5. The method for independent driving and coordinated control of a double-layer medical catheter according to claim 1, characterized in that: In step S3, the coordination adjustment coefficients include the angle coordination coefficient, the speed coordination coefficient, and the load coordination coefficient. When the vascular bending radius is less than 5mm, the angle coordination coefficient is dynamically increased by 30%-50% based on the baseline value to ensure that the guiding posture of the outer catheter matches the movement trajectory of the inner catheter.

6. The method for independent driving and coordinated control of a double-layer medical catheter according to claim 1, characterized in that, In step S4, during the process of generating drive commands, the central control unit corrects the model output results through a fuzzy control algorithm. Specifically, when the real-time load data exceeds the preset safety threshold, the system automatically reduces the output torque of the drive motor in a proportional relationship, with the torque reduction being 10%-30% of the load excess.

7. The method for independent driving and coordinated control of a double-layer medical catheter according to claim 1, characterized in that: In step S5, the PID algorithm adopts a variable parameter PID control strategy. Specifically, when the position error between the two layers of ducts is greater than 0.2 mm, the proportional coefficient increases by 40%-60% and the integral coefficient increases by 30%-50%; when the position error is less than 0.05 mm, the proportional coefficient decreases by 20%-40% and the derivative coefficient increases by 50%-100% to improve the system response speed and control stability.

8. The method for independent driving and coordinated control of a double-layer medical catheter according to claim 1, characterized in that, The security protection step S6 includes the following steps: S6.1, Level 1 protection is based on real-time correction of drive commands using fuzzy control; S6.2, Level 2 protection reduces the drive motor speed to 50% of its original speed; S6.3, Level 3 protection is an emergency shutdown that triggers an audible and visual alarm signal.

9. The method for independent driving and coordinated control of a double-layer medical catheter according to claim 8, characterized in that: The abnormal posture thresholds include a maximum deflection angle of ±15° for the inner duct and a support angle range of 0°-90° for the outer duct; the overload threshold is 80% of the rated torque of the drive motor, and when the load continuously exceeds this threshold for 300ms, the secondary protection is automatically triggered.

10. The method for independent driving and coordinated control of a double-layer medical catheter according to claim 1, characterized in that: The dynamic adjustment process in step S5 also includes predictive adjustment based on surgical intention: by collecting the displacement and speed signals of the doctor's operating handle in real time, combined with the historical operation data model, the movement requirements of the catheter after 0.1s-0.3s are predicted, and the speed and angle coordination coefficients in the collaborative control model are adjusted in advance to shorten the system response time to less than 50ms.