Endoscope auxiliary system and control method thereof

Abstract

The invention discloses an endoscope auxiliary system and a control method thereof, the endoscope auxiliary system is suitable for an endoscope, and the endoscope auxiliary system comprises a multi-core optical fiber sensor, a looping identification device and a visualization device; the multi-core optical fiber sensor is spirally distributed in the endoscope body along the axial direction of the endoscope body; and the looping identification device is used for receiving and analyzing the reflection spectrum signal of the fiber Bragg grating array in real time, constructing a three-dimensional shape of the endoscope body based on the reflection spectrum signal, identifying whether the endoscope body forms a looping or not based on the three-dimensional shape, and obtaining a looping result. Through combination of the optical fiber sensor and spectrum demodulation, more stable and reliable operation support is provided under the condition that deformation cannot be accurately monitored by a traditional endoscope, and the endoscopic examination efficiency, safety and diagnosis accuracy are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of medical engineering technology, and in particular to an endoscope auxiliary system and a control method thereof. BACKGROUND

[0002] An endoscope is a main minimally invasive instrument for diagnosing and treating colorectal diseases, and is widely used for endoscopic direct-viewing examination and screening of intestinal lesions, such as discovery, biopsy sampling, polyp resection and hemostasis of polyps, ulcers, inflammation and tumors, and therapeutic operations such as postoperative follow-up and image recording.

[0003] The existing endoscope operation highly depends on the experience and tactile sensation of the operator, and has a large subjective judgment component. It is difficult to identify and remove the looping of the endoscope body, and is mostly based on indirect signs such as contradiction between advancement and retraction, abnormal insertion distance, and the possibility of forming a blind area in the curved or residue area of the field of view. In addition, there is a lack of distributed shape and mechanical feedback to quantify the contact pressure and safety limit. Furthermore, anatomical variations of the intestinal tract, such as intestinal redundancy, multiple angles, previous pelvic and abdominal surgery adhesions, and poor intestinal preparation, also significantly increase the difficulty of operation, making it difficult for the operator to stably achieve clinical quality indicators, resulting in large fluctuations in examination time and complication rates, and operation difficulty. SUMMARY

[0004] The present application provides an endoscope auxiliary system and a control method thereof to reduce the operation complexity of the endoscope and improve the intubation success rate.

[0005] To solve the above technical problems, the present application provides an endoscope auxiliary system suitable for an endoscope, comprising: a multi-core optical fiber sensor, a loop forming recognition device, and a visualization device. The multi-core optical fiber sensor is arranged in a spiral shape along the axial direction of the endoscope body inside the endoscope body. The multi-core optical fiber sensor comprises an array of fiber Bragg grating arrays arranged in a longitudinal direction, and the fiber Bragg grating arrays are arranged in the inner core or side core of the multi-core optical fiber sensor. The loop forming recognition device is used to receive and analyze the reflection spectrum signal of the fiber Bragg grating array in real time, construct a three-dimensional shape of the endoscope body based on the reflection spectrum signal, and identify whether the endoscope body forms a loop based on the three-dimensional shape to obtain a loop forming result. The visualization device is used to visualize the three-dimensional shape and the loop forming result.

[0006] The present application can monitor the deformation and curvature change of the endoscope body in real time by adopting a multi-core optical fiber sensor, which is installed in the endoscope body in a spiral distribution along the endoscope body axis, and using the reflection spectrum signal of the fiber Bragg grating array. The fiber Bragg grating array reflects the test light signal and produces spectral changes, reflecting the small changes in the position and shape of the optical fiber. The time sequence center wavelength, reflection intensity and signal-to-noise ratio in the reflection spectrum signal, after efficient demodulation and analysis, can accurately capture the strain change of each optical fiber. By reconstructing the three-dimensional shape based on the reflection spectrum signal, the system can identify whether the endoscope body has deformation phenomena such as looping in real time, and can convert complex endoscope shape changes into real-time visual feedback, helping the operator to obtain the shape information of the endoscope in real time, not only enhancing the operator's perception of the operation state of the endoscope, but also reducing the risk of examination failure or missed diagnosis due to incorrect judgment of the endoscope shape. In addition, through the combination of optical fiber sensors and spectral demodulation, more stable and reliable operation support is provided in the case where the traditional endoscope cannot accurately monitor the deformation, improving the efficiency, safety and diagnostic accuracy of endoscopic examination.

[0007] Further, the looping recognition device comprises a deformation calculation module, a three-dimensional reconstruction module and an identification module, comprising: The deformation calculation module is configured to receive and analyze the reflection spectrum signal of the fiber Bragg grating array in real time, and calculate the fiber core deformation information based on the reflection spectrum signal. The three-dimensional reconstruction module is configured to obtain the spiral distribution data of the multi-core optical fiber sensor, calculate the curvature and torsion rate of the endoscope based on the fiber core deformation information and the spiral distribution data, and perform three-dimensional reconstruction on the endoscope based on the curvature, torsion rate and preset reconstruction algorithm to obtain the three-dimensional shape. The identification module is configured to identify the looping type and looping degree of the endoscope based on a preset identification model and the three-dimensional shape.

[0008] The present application calculates the fiber core deformation information based on the real-time reflection spectrum signal, and combines the spiral distribution data to accurately reconstruct the three-dimensional shape of the endoscope. The reconstruction module not only corrects the curvature of the endoscope based on the fiber core deformation information, but also uses a preset reconstruction algorithm for high-precision three-dimensional shape reconstruction, effectively improving the shape recognition ability of the endoscope in complex operation scenarios. Through real-time three-dimensional reconstruction and looping type identification, the present application can realize real-time dynamic feedback of the endoscope, effectively avoiding the limitations of traditional manual identification and fixed mode operation, and providing more scientific operation basis for the operator.

[0009] Further, the deformation calculation module is configured to receive and analyze the reflection spectrum signal of the fiber Bragg grating array in real time, and calculate the fiber core deformation information based on the reflection spectrum signal, comprising: Based on the fiber Bragg grating array, a reflection spectrum signal is acquired, peak spectrum detection is performed on the reflection spectrum signal, the center wavelength of each grating and the corresponding signal-to-noise ratio are acquired; A temperature signal is acquired in real time, the center wavelength is corrected based on a preset temperature compensation model, a wavelength change amount is acquired, and the strain value of each core is calculated based on the wavelength change amount.

[0010] Based on the reflection spectrum signal acquired by the fiber Bragg grating array, the peak spectrum detection and real-time temperature correction are adopted, which not only improves the accuracy of strain calculation, but also effectively eliminates the influence of temperature change on the reflection spectrum signal. Through this way, the system can accurately calculate the strain value of each optical fiber in real time, and accurately feedback the deformation of the endoscope, thereby further improving the accuracy of endoscope shape reconstruction, greatly enhancing the real-time monitoring ability in complex environment, and effectively supporting the accurate operation of doctors in high-risk scenarios.

[0011] Further, the three-dimensional reconstruction module is configured to acquire spiral distribution data of the multicore optical fiber sensor, calculate the curvature and torsion rate of the endoscope based on the core deformation information and the spiral distribution data, and perform three-dimensional reconstruction on the endoscope based on the curvature, torsion rate and preset reconstruction algorithm to acquire a three-dimensional shape, including: Acquire spiral distribution data of the multicore optical fiber sensor, the spiral distribution data including pitch, spiral angle and azimuth angle of each core relative to the center core; Correct the strain value of each core based on the spiral distribution data including pitch, spiral angle and azimuth angle of each core relative to the center core to acquire a target core strain distribution; Construct a strain-curvature mapping relationship based on the target core strain distribution, and calculate the local curvature and torsion rate of the endoscope based on the curvature mapping relationship; Perform three-dimensional reconstruction on the endoscope based on the local curvature, torsion rate and preset reconstruction algorithm of the endoscope to acquire a three-dimensional shape.

[0012] The present application can accurately acquire the spiral distribution data of the multicore optical fiber sensor, and calculate the curvature in combination with the core deformation information, so as to efficiently and accurately reconstruct the three-dimensional shape of the endoscope. Through the correction of the pitch, spiral angle and azimuth angle of each core relative to the center core, the system can dynamically adjust the strain distribution of each core, thereby more accurately calculating the local curvature and torsion rate of the endoscope, providing high-precision three-dimensional shape reconstruction based on the curvature and deformation information of the endoscope, and effectively making up for the shortcomings of traditional reconstruction methods in dynamicity and real-time performance.

[0013] Further, the three-dimensional reconstruction module is configured to reconstruct the endoscope based on the local curvature and twist rate of the endoscope and a preset reconstruction algorithm, to obtain a three-dimensional shape, including: Based on the local curvature and twist rate of the endoscope, a space curve integral method is used to perform piecewise coordinate reconstruction to obtain a three-dimensional discrete space coordinate set of the endoscope. Based on a preset fitting algorithm, the three-dimensional discrete space coordinate set is processed to generate a three-dimensional center line model of the endoscope. Based on the three-dimensional center line model and the outer diameter parameter of the endoscope, a three-dimensional shape of the endoscope is established.

[0014] By using the space curve integral method for piecewise coordinate reconstruction and the preset fitting algorithm for continuous processing, the three-dimensional discrete coordinate set of the endoscope can be effectively converted into a smooth and continuous three-dimensional center line model. Not only the fitting accuracy of the three-dimensional shape is improved, but also a more realistic shape modeling can be performed in combination with the outer diameter parameter of the endoscope. By generating a continuous three-dimensional center line model and combining the endoscope parameters, the three-dimensional structure of the endoscope can be accurately reproduced, thereby further improving the visualization effect and operation flexibility of the endoscope in actual operation. More intuitive and accurate endoscope shape information can be provided to the operator, the operation process is optimized, and errors are reduced.

[0015] In a second aspect, the application provides a control method of an endoscope auxiliary system, which is applied to the endoscope auxiliary system, and the endoscope auxiliary control system includes a multi-core optical fiber sensor, a loop formation recognition module, and a visualization module. The multi-core optical fiber sensor is arranged in a spiral shape along the endoscope shaft in the endoscope; The loop formation recognition module is controlled to receive and analyze the reflection spectrum signal of the fiber Bragg grating array in real time, to construct a three-dimensional shape of the endoscope based on the reflection spectrum signal, to recognize whether the endoscope forms a loop based on the three-dimensional shape, and to obtain a loop formation result. Based on the loop formation result, the visualization module is driven to visualize the three-dimensional shape and the loop formation result.

[0016] Further, the control of the loop formation recognition module to receive and analyze the reflection spectrum signal of the fiber Bragg grating array in real time, to construct a three-dimensional shape of the endoscope based on the reflection spectrum signal, to recognize whether the endoscope forms a loop based on the three-dimensional shape, and to obtain a loop formation result, includes: The reflection spectrum signal of the fiber Bragg grating array is received and analyzed in real time, and the fiber core deformation information is calculated based on the reflection spectrum signal. acquire spiral distribution data of the multi-core optical fiber sensor, calculate the curvature of the endoscope based on the fiber core deformation information and the spiral distribution data, and perform three-dimensional reconstruction on the endoscope based on the endoscope curvature and a preset reconstruction algorithm to acquire a three-dimensional shape; identify the loop type and loop degree of the endoscope based on a preset identification model and the three-dimensional shape.

[0017] Further, the reflection spectrum signal of the fiber Bragg grating array is received and analyzed in real time, and fiber core deformation information is calculated based on the reflection spectrum signal, including: Based on the reflection spectrum signal of the fiber Bragg grating array, peak spectrum detection is performed on the reflection spectrum signal to acquire the center wavelength of each grating and the corresponding signal-to-noise ratio; A temperature signal is acquired in real time, the center wavelength is corrected based on a preset temperature compensation model to acquire a wavelength change amount, and the strain value of each fiber core is calculated based on the wavelength change amount.

[0018] Further, the spiral distribution data of the multi-core optical fiber sensor is acquired, the curvature of the endoscope is calculated based on the fiber core deformation information and the spiral distribution data, and three-dimensional reconstruction is performed on the endoscope based on the endoscope curvature and a preset reconstruction algorithm to acquire a three-dimensional shape, including: The spiral distribution data of the multi-core optical fiber sensor is acquired, and the spiral distribution data includes the pitch, the spiral angle, and the azimuth angle of each fiber core relative to the central fiber core; The strain value of each fiber core is corrected based on the spiral distribution data including the pitch, the spiral angle, and the azimuth angle of each fiber core relative to the central fiber core to acquire a target fiber core strain distribution; A strain-curvature mapping relationship is constructed based on the target fiber core strain distribution, and the local curvature and torsion rate of the endoscope are calculated based on the curvature mapping relationship; The endoscope is three-dimensionally reconstructed based on the local curvature, the torsion rate of the endoscope, and a preset reconstruction algorithm to acquire a three-dimensional shape.

[0019] Further, the endoscope is three-dimensionally reconstructed based on the local curvature, the torsion rate of the endoscope, and a preset reconstruction algorithm to acquire a three-dimensional shape, including: Based on the local curvature and the torsion rate of the endoscope, spatial curve integration is performed for segment-by-segment coordinate reconstruction to acquire a three-dimensional discrete spatial coordinate set of the endoscope; The three-dimensional discrete spatial coordinate set is continuously processed based on a preset fitting algorithm to generate a three-dimensional centerline model of the endoscope; Based on the three-dimensional centerline model and the outer diameter parameter of the endoscope body, a three-dimensional shape of the endoscope is established. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A structural schematic diagram of an endoscope auxiliary system provided by an embodiment of the present application is shown in the figure; Figure 2 A flowchart of a control method of an endoscope auxiliary system provided by an embodiment of the present application is shown in the figure; Figure 3 A three-dimensional structural schematic diagram of a control method of an endoscope auxiliary system provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0021] The specific embodiments of the present application will be further described in detail below with reference to the accompanying drawings and embodiments. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0022] The terms "first" and "second" and the like in the specification and claims of the present application and the accompanying drawings are used to distinguish different objects, and are not used to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0023] Reference to "an embodiment" in this document means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] Embodiment 1 Reference Figure 1 , Figure 1 A structural schematic diagram of an endoscope auxiliary system provided by an embodiment of the present application is shown in the figure. The present application provides an endoscope auxiliary system suitable for an endoscope, which comprises a multi-core optical fiber sensor 101, a loop recognition device 102 and a visualization device 103. The multi-core optical fiber sensor is arranged in a spiral shape along the axial direction of the endoscope body and disposed in the endoscope body; the multi-core optical fiber sensor comprises an array of fiber Bragg grating arranged in a longitudinal direction, and the array of fiber Bragg grating is arranged in the inner core or side core of the multi-core optical fiber sensor; The loop recognition device is configured to receive and analyze the reflection spectrum signal of the fiber Bragg grating array in real time, construct a three-dimensional shape of the endoscope body based on the reflection spectrum signal, and recognize whether the body forms a loop based on the three-dimensional shape and obtain a loop result. The visualization device is configured to visualize the three-dimensional shape and the loop result.

[0025] In this embodiment, the loop recognition device includes a deformation calculation module, a three-dimensional reconstruction module, and a recognition module, which include: The deformation calculation module is configured to receive and analyze the reflection spectrum signal of the fiber Bragg grating array in real time, and calculate core deformation information based on the reflection spectrum signal. The three-dimensional reconstruction module is configured to obtain spiral distribution data of the multicore optical fiber sensor, calculate the curvature and torsion of the endoscope based on the core deformation information and the spiral distribution data, and perform three-dimensional reconstruction on the endoscope based on the curvature, the torsion, and a preset reconstruction algorithm to obtain a three-dimensional shape. The recognition module is configured to recognize the loop type and the loop degree of the endoscope based on a preset recognition model and the three-dimensional shape.

[0026] In this embodiment, the multicore optical fiber sensor 101 is a seven-core optical fiber structure arranged in a spiral shape along the axis in the longitudinal direction outside the body and embedded in a thin protective layer. The fiber Bragg grating (FBG) array is written on the fiber at a predetermined interval, for example, every 5-20 mm, which can be adjusted according to the reconstruction resolution requirement. The FBG is preferably written on the outer core or the side core to obtain a strain response sensitive to bending. A reference temperature grating or a dedicated temperature core is arranged in the fiber to achieve temperature compensation.

[0027] In this embodiment, the seven-core optical fiber structure includes straight cores, twisted cores, acrylate, and polyimide coatings.

[0028] In this embodiment, the deformation calculation module is configured to receive and analyze the reflection spectrum signal of the fiber Bragg grating array in real time, and calculate core deformation information based on the reflection spectrum signal, which includes: The reflection spectrum signal is obtained based on the fiber Bragg grating array, the peak spectrum of the reflection spectrum signal is detected, the center wavelength of each grating and the corresponding signal-to-noise ratio are obtained, the temperature signal is obtained in real time, the center wavelength is corrected based on a preset temperature compensation model, the wavelength change is obtained, and the strain value of each core is calculated based on the wavelength change.

[0029] ​In the embodiment, the deformation calculation module realizes real-time analysis of the FBG reflection spectrum and output of the fiber core deformation information through the cooperation of hardware and software. The module includes an optical interface subunit, a spectrum demodulation and preprocessing subunit, a peak detection and signal quality evaluation subunit, a temperature acquisition and compensation subunit, a parameter storage and calibration subunit, and a data communication interface with the upper control unit and the three-dimensional reconstruction module.

[0030] In the embodiment, the optical interface subunit converts the reflected light from the multicore optical fiber into an electrical signal and transmits it to the spectrum demodulation and preprocessing subunit; the spectrum demodulation and preprocessing subunit performs bandpass or low-pass filtering, baseline correction and denoising processing on the input signal to improve the detectability of the spectral peak.

[0031] In the embodiment, the peak detection and signal quality evaluation subunit performs spectral peak positioning and peak position tracking based on real-time spectral data, extracts the center wavelength and reflection intensity of each grating, and calculates the corresponding signal-to-noise ratio or loss of lock flag; abnormal channels are marked by the subunit and trigger interpolation or redundancy strategy; In the embodiment, the temperature acquisition and compensation subunit acquires temperature information through an external temperature sensor or a special reference temperature grating, and corrects the center wavelength of each working grating according to the preset temperature compensation model to obtain the true wavelength change.

[0032] In the embodiment, the parameter storage and calibration subunit saves the factory-calibrated sensitivity coefficient, baseline center wavelength and geometric correction parameters, and provides online calibration, baseline update and self-checking functions during operation. The wavelength change after temperature compensation is converted into the strain value of each fiber core as the fiber core deformation information, accompanied by signal quality indicators and time stamps, and is transmitted in real time to the three-dimensional reconstruction module through a high-speed data interface for subsequent curvature calculation and shape reconstruction. The deformation calculation module supports configurable sampling rate and data buffering strategy, and has the functions of loss of lock alarm, channel redundancy processing and long-term drift correction, to ensure real-time performance, robustness and measurement accuracy in a clinical environment.

[0033] In the embodiment, the three-dimensional reconstruction module is configured to acquire spiral distribution data of the multicore optical fiber sensor, calculate curvature and torsion rate of the endoscope based on the fiber core deformation information and the spiral distribution data, and perform three-dimensional reconstruction on the endoscope based on the curvature, torsion rate and a preset reconstruction algorithm to acquire a three-dimensional shape, including: acquiring spiral distribution data of the multicore optical fiber sensor, the spiral distribution data including pitch, spiral angle and azimuth angle of each fiber core relative to a center fiber core; correcting the strain value of each fiber core based on the spiral distribution data including pitch, spiral angle and azimuth angle of each fiber core relative to a center fiber core to acquire target fiber core strain distribution; construct a strain-curvature mapping relationship based on the target core strain distribution, and calculate local curvature and torsion of the endoscope based on the curvature mapping relationship; perform three-dimensional reconstruction on the endoscope based on the local curvature, torsion of the endoscope and a preset reconstruction algorithm, and obtain a three-dimensional shape.

[0034] In the embodiment, the three-dimensional reconstruction module is responsible for fusing the core strain information output by the deformation calculation module with the pre-recorded multi-core optical fiber spiral arrangement parameters, and then calculating the local curvature and torsion of the endoscope and completing the three-dimensional shape reconstruction.

[0035] In the embodiment, the three-dimensional reconstruction module includes a spiral parameter management subunit, a strain geometry correction subunit, a strain curvature mapping subunit, a curvature and torsion smoothing subunit, a spatial reconstruction subunit and a post-reconstruction processing and output interface.

[0036] In the embodiment, first, the spiral parameter management subunit reads or queries the spiral distribution data of the multi-core optical fiber sensor, including the pitch, spiral angle and azimuth angle of each core relative to the central core and other geometric parameters, and loads the corresponding geometric correction parameters according to the factory calibration or on-site calibration results; then, the strain geometry correction subunit performs coordinate rotation and geometric correction on the strain values of each core from the deformation calculation module according to the above spiral parameters, compensates for the angle deviation caused by spiral arrangement and torsion, and outputs the strain distribution sequence of the target core. Next, the strain curvature mapping subunit maps the strain into local curvature and torsion according to the target core strain distribution and the preloaded mechanical model, and submits the obtained curvature and torsion sequence to the curvature and torsion smoothing subunit for noise suppression and numerical stabilization processing, so as to reduce measurement noise and integral accumulation error.

[0037] In the embodiment, the spatial reconstruction subunit obtains three-dimensional discrete coordinate points by using the spatial curve integral method or the segmented rigid body chain model based on the smoothed curvature and torsion sequence, combining the initial pose and boundary constraints of the endoscope, and solving along the axial direction; and performs continuous processing on the discrete points by using a spline or fitting algorithm to generate a three-dimensional centerline model; then, the post-reconstruction processing subunit combines the centerline and the outer diameter parameter of the scope to generate a three-dimensional surface model, and calculates the reconstruction confidence, local error estimation and timestamp information.

[0038] In the embodiment, the three-dimensional reconstruction module is configured to perform three-dimensional reconstruction on the endoscope based on the local curvature, torsion of the endoscope and a preset reconstruction algorithm, and obtain a three-dimensional shape, and includes: perform segmented coordinate reconstruction by using the spatial curve integral method based on the local curvature, torsion of the endoscope, and obtain a three-dimensional discrete spatial coordinate set of the endoscope; The three-dimensional discrete space coordinate set is continuously processed based on a preset fitting algorithm to generate a three-dimensional center line model of the endoscope. A three-dimensional shape of the endoscope is established based on the three-dimensional center line model and an outer diameter parameter of a scope of the endoscope.

[0039] In the embodiment, the three-dimensional reconstruction module serves as a calculation core of the endoscope recognition device 102 and is specially used for converting the local curvature and torsion sequence from the curvature and torsion calculation unit into a three-dimensional geometric representation of the endoscope.

[0040] In the embodiment, the three-dimensional reconstruction module is composed of a piecewise integration subunit, a fitting and continuous subunit, a surface construction subunit and a reconstruction quality evaluation subunit.

[0041] In the embodiment, the piecewise integration subunit takes the configured initial posture and boundary constraint as a reference, adopts a spatial curve integration method to perform piecewise numerical integration on the input curvature and torsion, and obtains a discrete three-dimensional coordinate point set of the endoscope axis by piecewise solution; in the integration process, the subunit can adaptively adjust the integration step length according to the real-time sampling interval and the piecewise length to balance the accuracy and real-time performance, and has the detection and short-time re-integration rollback capability for abnormal mutation points to suppress the error amplification caused by sudden noise.

[0042] In the embodiment, the fitting and continuous subunit applies a preset fitting algorithm, such as a cubic spline, a Bezier curve or other smooth interpolation and least square fitting method, to the discrete coordinate point set to perform curve smoothing and continuous processing, and generates a continuous three-dimensional center line model, while performing statistics on the fitting residual and reporting the local error distribution to the reconstruction quality evaluation subunit.

[0043] In the embodiment, the surface construction subunit generates a three-dimensional surface model or a tubular mesh for visualization and collision and contact analysis based on the obtained three-dimensional center line and in combination with the outer diameter of the scope, the cross-sectional shape parameter and the necessary geometric offset, and supports multiple output formats for calling by a visualization device and a recognition module.

[0044] In the embodiment, the reconstruction quality evaluation subunit calculates the reconstruction confidence and local uncertainty based on the fitting residual, the curvature consistency, the signal quality index and the historical trajectory continuity, and triggers online correction or alarm prompt when the confidence is lower than a threshold.

[0045] In the embodiment, the three-dimensional reconstruction module receives the curvature and torsion input at a real-time rate through a standardized data interface and outputs the discrete coordinates, the continuous center line, the three-dimensional surface and the reconstruction quality index, so as to ensure that the accuracy requirement is met in the clinical operation while the system delay and robustness are taken into account.

[0046] In the embodiment, the visualization device 103 is a man-machine interaction and presentation subsystem of the loop recognition device 102, which is used to present the three-dimensional reconstruction result, the curvature and torsion information, the pressure distribution and the loop recognition conclusion to the clinical operator in an intuitive, real-time and interactive manner.

[0047] Please refer to Figure 2 , Figure 2 A flowchart of a control method of an endoscope auxiliary system provided by an embodiment of the present application is shown.

[0048] The embodiment of the present application provides a control method of an endoscope auxiliary system, which is applied to the endoscope auxiliary system, and the endoscope auxiliary control system comprises a multi-core optical fiber sensor, a loop recognition module and a visualization module, and specifically comprises steps 201 to 203. In step 201, the multi-core optical fiber sensor is arranged in a spiral shape along the endoscope body in the axial direction of the endoscope body; and a column of fiber Bragg grating arrays arranged in the longitudinal direction is written in the side core or the outer core of the multi-core optical fiber sensor. In the embodiment, in order to realize high-precision shape sensing of the full length of the endoscope body, the multi-core optical fiber sensor is embedded and arranged in the spiral shape along the axial direction of the endoscope body in the lumen of the endoscope body. The multi-core optical fiber is preferably a seven-core structure, in which a column of fiber Bragg grating (FBG) arrays arranged in the longitudinal direction is written in the side core or the outer core, so as to ensure high sensitivity response to bending; in order to realize compensation for temperature cross-sensitivity, at least one reference temperature grating or special temperature core is arranged in the optical fiber in parallel. The writing of the FBG preferably adopts femtosecond laser direct writing technology, which can directly write the fiber core after removing the thin layer coating or reserving the polymer coating, so as to ensure the thermal stability and long-term reliability of the grating at the clinical sterilization temperature.

[0049] In the embodiment, in order to balance the space constraint and the torsion measurement capability, the optical fiber is arranged in the spiral shape along the axial direction in the endoscope body, the pitch and the spiral angle are determined according to the diameter of the endoscope body and the required torsion sensitivity, and the optical fiber is fixed to the inner wall of the endoscope body through the fixed groove, the elastic bonding or the thin polymer coating layer, so as to ensure that the position and the azimuth angle of the optical fiber do not significantly drift during the bending and repeated plugging of the endoscope body.

[0050] In the embodiment, the grating pitch, i.e. the FBG sampling interval, is set according to the shape reconstruction accuracy requirement, for example, in a preferred range of 5-20 mm, and in the spectral domain, the center wavelength stagger and appropriate reflection bandwidth are designed to avoid the grating reflection spectrum overlapping. One end of the sensing optical fiber is connected to an external spectral demodulation unit (light source and demodulator) through a micro fiber connector, and a stress release structure and a shielding sleeve are used at the connection to ensure the reliability of the electro-optical interface and the clinical detachability; a circulator or a coupler is provided in the optical path to complete the transmission and reception optical isolation. The entire sensing section is covered with inert film or biocompatible material to meet the smooth appearance of the scope, sterilization requirements and biological safety, while maintaining the required flexibility and minimum outer diameter increment.

[0051] In the embodiment, the fiber geometric position (core azimuth angle, pitch), grating baseline center wavelength and sensitivity coefficient are calibrated at the factory and written into the storage for online calibration during operation; during clinical use, the spectral demodulation unit reads out the FBG in real time at a configurable sampling rate, and the deformation calculation module completes the twist compensation and curvature solving based on the strain sequence from the side core or the outer core combined with the spiral geometric parameters, thereby providing reliable basic data for three-dimensional reconstruction and loop identification.

[0052] Step 202: controlling the loop identification module to receive and analyze the reflection spectrum signal of the fiber Bragg grating array in real time, constructing a three-dimensional shape of the endoscope scope based on the reflection spectrum signal, and identifying whether the scope forms a loop based on the three-dimensional shape, obtaining a loop result; In the embodiment, the control loop identification module receives and analyzes the reflection spectrum signal of the fiber Bragg grating array in real time, constructs a three-dimensional shape of the endoscope scope based on the reflection spectrum signal, and identifies whether the scope forms a loop based on the three-dimensional shape, obtaining a loop result, including: receiving and analyzing the reflection spectrum signal of the fiber Bragg grating array in real time, and calculating core deformation information based on the reflection spectrum signal; obtaining spiral distribution data of the multicore optical fiber sensor, calculating the curvature of the endoscope based on the core deformation information and the spiral distribution data, and performing three-dimensional reconstruction on the endoscope based on the endoscope curvature and a preset reconstruction algorithm, obtaining a three-dimensional shape; identifying the loop type and loop degree of the endoscope based on a preset identification model and the three-dimensional shape.

[0053] In the embodiment, first, the system receives the reflection spectrum of the FBG array on the multicore optical fiber in real time and performs spectrum peak detection, peak tracking and baseline correction, while evaluating the signal-to-noise ratio and lock loss state of each channel; then, the spectrum peak is temperature compensated based on a reference temperature grating or an external temperature sensor, and the corrected spectrum shift is converted into the strain sequence of each core as the core deformation information.

[0054] In this embodiment, then, the system reads the registered helical arrangement parameters such as pitch, helix angle and core azimuth angle, performs coordinate rotation and twist compensation on the original strain to obtain the target core strain distribution, and calculates the local curvature and torsion rate sequence along the lens axis based on the preset strain and curvature mapping relationship; to ensure numerical stability, the curvature and torsion rate sequence is processed by interpolation, spline smoothing or Kalman filtering to suppress noise and integral accumulation error.

[0055] In this embodiment, subsequently, the system uses a preset reconstruction algorithm, such as spatial curve integration or segmented rigid body chain model, to integrate along the axis to obtain a three-dimensional discrete coordinate set, and forms a continuous three-dimensional center line and lens surface model through fitting and continuous processing. After reconstruction, the three-dimensional shape and related mechanical quantities, local curvature peak value, torsion rate, contact pressure distribution, etc., are input into a pre-trained identification model, which outputs the determination of whether a loop exists, the classification of loop type and the severity score, along with the identification confidence. According to the identification result and the safety threshold, the system generates a visual prompt and a step-by-step resolution suggestion.

[0056] In this embodiment, the system can also send limited execution instructions to the mechanical assistance device, while triggering an alarm and suspending automatic action when high risk or insufficient confidence is detected. The entire process records the original spectral data, intermediate quantities, reconstruction results and identification decisions from beginning to end for postoperative playback, quality control and continuous iterative training and performance optimization of the identification model.

[0057] In this embodiment, the identification model preferably adopts a deep learning structure, with a hybrid architecture combining convolutional neural networks and long short-term memory networks as the core, the former for extracting spatial morphological features and the latter for capturing the dynamic changes of bending along the time sequence.

[0058] In this embodiment, based on common endoscope loop type identification, including typical morphologies such as N-shaped ring, alpha ring and reverse alpha ring, the identification model is first trained. A large amount of bending shape and strain distribution data of endoscopes in different operating states are collected by a multi-core fiber Bragg grating (FBG) sensor array, and the corresponding loop type is labeled synchronously as a training sample set. Then, feature extraction is performed on the sample data, including local curvature sequence, torsion rate change rate, spatial morphological vector and overall bending trend parameters, etc., to construct the training data feature space, thereby supervising the training of the training model. Through the supervised training process, the model learns the differences in curvature distribution patterns and deformation dynamic features of different loop types, so that in the inference stage it can automatically identify whether the endoscope forms a loop and the type of the loop based on the real-time acquired FBG signal reconstruction shape. The identification model structure has high precision and real-time performance, which can significantly improve the intelligent level and robustness of endoscope loop determination.

[0059] In the embodiment, the reflection spectrum signal of the fiber Bragg grating array is received and analyzed in real time, and the fiber core deformation information is calculated based on the reflection spectrum signal, including: Based on the reflection spectrum signal obtained from the fiber Bragg grating array, peak spectrum detection is performed on the reflection spectrum signal to obtain the center wavelength of each grating and the corresponding signal-to-noise ratio; A temperature signal is obtained in real time, the center wavelength is corrected based on a preset temperature compensation model, a wavelength change amount is obtained, and the strain value of each fiber core is calculated based on the wavelength change amount.

[0060] In the embodiment, the FBG reflection spectrum signal is analyzed in real time in a real-time pipeline manner during runtime, and fiber core deformation information that can be used for subsequent shape reconstruction is generated. First, the reflection spectrum data from the multi-core fiber Bragg grating array is periodically received at a preset sampling rate, and peak detection and peak tracking are performed on the original spectrum line to extract the center wavelength of each grating at each time and the corresponding reflection intensity and signal-to-noise ratio. Then, baseline correction, noise suppression and abnormal channel determination are performed on each spectrum line, and interpolation or redundant channel replacement strategies are enabled for lost or low signal-to-noise ratio channels to ensure data continuity. Moreover, a reference temperature signal is collected in real time, which is input into a preset temperature compensation model to correct the center wavelength of each grating to obtain the true wavelength change amount. Based on the temperature-compensated wavelength change amount and the sensitivity parameters obtained from factory calibration or online calibration, the strain time sequence of each fiber core is converted according to the predetermined mapping rule.

[0061] In the embodiment, during the entire processing process, the system attaches a time stamp and a quality evaluation index, such as signal-to-noise ratio, peak stability and residual error after compensation, to each measurement value, and performs filtering and denoising on the obtained strain sequence to output reliable fiber core deformation information for curvature calculation and three-dimensional reconstruction. At the same time, the original spectrum data, correction parameters and intermediate results are recorded and archived for online monitoring, postoperative playback and model retraining.

[0062] In the embodiment, based on the reflection spectrum signal obtained from the fiber Bragg grating array, peak spectrum detection and real-time temperature correction are adopted, which not only improves the accuracy of strain calculation, but also effectively eliminates the influence of temperature change on the reflection spectrum signal. In this way, the system can accurately calculate the strain value of each fiber in real time and accurately feedback the deformation of the endoscope, thereby further improving the accuracy of endoscope shape reconstruction, greatly enhancing the real-time monitoring capability in complex environments, and effectively supporting the accurate operation of doctors in high-risk scenarios.

[0063] In the embodiment, the spiral distribution data of the multicore fiber sensor is acquired, the curvature of the endoscope is calculated based on the fiber core deformation information and the spiral distribution data, and the endoscope is three-dimensionally reconstructed based on the endoscope curvature and a preset reconstruction algorithm to acquire a three-dimensional shape, including: The spiral distribution data of the multicore fiber sensor is acquired, and the spiral distribution data includes a pitch, a spiral angle, and an azimuth angle of each fiber core relative to a center fiber core. The strain values of the fiber cores are corrected based on the spiral distribution data including the pitch, the spiral angle, and the azimuth angle of each fiber core relative to the center fiber core to acquire a target fiber core strain distribution. A strain-curvature mapping relationship is constructed based on the target fiber core strain distribution, and the local curvature and torsion of the endoscope are calculated based on the curvature mapping relationship. The endoscope is three-dimensionally reconstructed based on the local curvature, the torsion of the endoscope, and a preset reconstruction algorithm to acquire a three-dimensional shape.

[0064] In the embodiment, the system first calls the spiral distribution data of the multicore fiber sensor from a sensor parameter database, and the data includes a pitch, a spiral angle, and an azimuth angle of each fiber core relative to a center fiber core, which are used to describe the spatial arrangement characteristics of the multicore fiber in the endoscope body. Then, the system matches the fiber core strain information calculated in the previous step with the above-mentioned geometric parameters, corrects the strain values under different azimuth angles and pitch conditions according to the spiral distribution data, and thus obtains a target fiber core strain distribution that is corrected in space and geometry. Next, the system converts the corrected target fiber core strain distribution into the local curvature and torsion information of the endoscope along the axial direction based on the strain-curvature mapping relationship established in the calibration stage, and realizes the mapping from the local strain field to the macroscopic morphological parameters.

[0065] In the embodiment, the spiral distribution data of the multicore fiber sensor is accurately acquired, and the curvature is calculated in combination with the fiber core deformation information, so that the three-dimensional morphology of the endoscope can be accurately reconstructed efficiently. Through the correction of the pitch, the spiral angle, and the azimuth angle of each fiber core relative to the center fiber core, the system can dynamically adjust the strain distribution of each fiber core, so as to more accurately calculate the local curvature and torsion of the endoscope, provide high-precision three-dimensional morphology reconstruction based on the endoscope curvature and deformation information, and effectively make up for the deficiencies of traditional reconstruction methods in dynamicity and real-time performance.

[0066] In the embodiment, the spiral distribution data of the multicore fiber sensor is acquired, and the spiral distribution data includes a pitch, a spiral angle, and an azimuth angle of each fiber core relative to a center fiber core. Based on the local curvature and the torsion of the endoscope, a spatial curve integration method is used for segment-by-segment coordinate reconstruction to acquire a three-dimensional discrete spatial coordinate set of the endoscope. The three-dimensional discrete space coordinate set is continuously processed based on a preset fitting algorithm to generate a three-dimensional center line model of the endoscope. A three-dimensional shape of the endoscope is established based on the three-dimensional center line model and an outer diameter parameter of the endoscope.

[0067] Please refer to Figure 3 , Figure 3 A three-dimensional shape structure schematic diagram of an endoscope auxiliary system control method provided in an embodiment of the present application.

[0068] In the embodiment, a pre-established strain and curvature mapping model is used to convert a target fiber core strain distribution into a local curvature and torsion sequence along the axial direction of the scope; to improve numerical robustness and suppress measurement noise, the curvature and torsion sequence is subjected to interpolation and filtering processing to obtain smooth input data. Based on the smoothed curvature and torsion, the system adopts a preset reconstruction algorithm to perform piecewise integration or piecewise splicing along the axial direction of the optical fiber, and gradually solves to obtain discrete three-dimensional coordinate points of the endoscope in space; then, fitting and continuous processing are applied to the discrete coordinate points to generate a continuous three-dimensional center line model, and a three-dimensional surface model that can be used for visualization and identification is constructed in combination with the outer diameter parameter of the endoscope.

[0069] In the embodiment, by using piecewise coordinate reconstruction by spatial curve integration method and continuous processing by preset fitting algorithm, the three-dimensional discrete coordinate set of the endoscope can be effectively converted into a smooth and continuous three-dimensional center line model. Not only the fitting accuracy of the three-dimensional shape is improved, but also more realistic modeling of the shape can be performed in combination with the outer diameter parameter of the endoscope. By generating a continuous three-dimensional center line model and in combination with the scope parameter, the three-dimensional structure of the endoscope can be accurately reproduced, thereby further improving the visualization effect and operation flexibility of the endoscope in actual operation. More intuitive and accurate shape information of the endoscope can be provided to the operator, the operation process is optimized, and the error is reduced.

[0070] Step 203: based on the loop forming result, the visualization module is driven to visualize the three-dimensional shape and the loop forming result.

[0071] In the embodiment, after the reconstruction of the three-dimensional shape of the endoscope and the identification of the loop are completed, the system drives the visualization module to perform dynamic rendering processing by taking the loop result output by the identification module as a trigger signal. First, the system generates a three-dimensional centerline model of the endoscope according to the spatial coordinate data output by the three-dimensional reconstruction module, and reconstructs a complete three-dimensional morphology in combination with the outer diameter parameter of the scope. Subsequently, the system performs highlighting or color labeling processing on the corresponding region in the three-dimensional model according to the loop type, loop position and loop degree information contained in the loop identification result, to realize intuitive display of the morphological features and risk areas. At the same time, the system can perform animation playback or real-time update of the deformation of the endoscope in the operation process according to the time sequence data, so that the operator can intuitively observe the spatial trend and loop evolution process of the endoscope in the three-dimensional view. Finally, the visualization module presents the three-dimensional shape and loop result in the form of a graphical interface, providing real-time morphological monitoring and operation feedback for the operator, and significantly improving the safety and controllability of endoscopic examination and interventional operation.

[0072] The present application can monitor the deformation and curvature change of the endoscope scope in real time by using the multi-core optical fiber sensor, which is installed in the scope in a spiral shape along the axial direction of the scope, and using the reflection spectrum signal of the fiber Bragg grating array. These fiber Bragg grating arrays reflect the test light signal and produce spectral changes, reflecting the small changes in the position and morphology of the optical fiber. The time sequence center wavelength, reflection intensity and signal-to-noise ratio data in the reflection spectrum signal can accurately capture the strain change of each optical fiber after efficient demodulation and analysis. By reconstructing the three-dimensional shape based on the reflection spectrum signal, the system can identify whether the scope has undergone deformation phenomena such as loop in real time, and can convert the complex endoscope morphology change into real-time visual feedback, helping the operator to obtain the morphology information of the endoscope in real time, not only enhancing the operator's perception of the operation state of the endoscope, but also reducing the risk of examination failure or missed diagnosis due to incorrect judgment of the scope morphology. In addition, through the combination of optical fiber sensors and spectral demodulation, more stable and reliable operation support is provided in the case where the traditional endoscope cannot accurately monitor the deformation, improving the efficiency, safety and diagnostic accuracy of endoscopic examination.

[0073] The above specific embodiments further illustrate the purpose, technical solutions and advantages of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An endoscope-assisted system, characterized in that, Suitable for endoscopes, including: multi-core fiber optic sensors, loop formation recognition devices, and visualization devices; The multi-core fiber optic sensor is spirally distributed within the endoscope body along the axial direction of the endoscope body; the multi-core fiber optic sensor includes a column of fiber Bragg gratings arranged longitudinally, and the fiber Bragg grating array is disposed in the inner core or side core of the multi-core fiber optic sensor. The loop formation identification device is used to receive and analyze the reflection spectrum signal of the fiber Bragg grating array in real time, construct the three-dimensional shape of the endoscope body based on the reflection spectrum signal, and identify whether the endoscope body has formed a loop based on the three-dimensional shape to obtain the loop formation result. The visualization device is used to visualize the three-dimensional shape and loop formation results.

2. The endoscope-assisted system as described in claim 1, characterized in that, The loop formation identification device includes a deformation calculation module, a three-dimensional reconstruction module, and an identification module, including: The deformation calculation module is used to receive and analyze the reflection spectrum signal of the fiber Bragg grating array in real time, and calculate the core deformation information based on the reflection spectrum signal. The three-dimensional reconstruction module is used to acquire the helical distribution data of the multi-core fiber sensor, calculate the curvature and torsion of the endoscope based on the fiber core deformation information and the helical distribution data, and perform three-dimensional reconstruction of the endoscope based on the curvature, torsion and a preset reconstruction algorithm to obtain the three-dimensional shape. The recognition module is used to identify the loop type and degree of looping of the endoscope based on a preset recognition model and the three-dimensional shape.

3. The endoscope-assisted system as described in claim 2, characterized in that, The deformation calculation module is used to receive and analyze the reflection spectrum signal of the fiber Bragg grating array in real time, and calculate the core deformation information based on the reflection spectrum signal, including: The reflection spectrum signal is obtained based on the fiber Bragg grating array, and peak spectrum detection is performed on the reflection spectrum signal to obtain the center wavelength and corresponding signal-to-noise ratio of each grating. The temperature signal is acquired in real time, the center wavelength is corrected based on a preset temperature compensation model, the wavelength change is obtained, and the strain value of each fiber core is calculated based on the wavelength change.

4. An endoscope-assisted system as described in claim 3, characterized in that, The three-dimensional reconstruction module is used to acquire the helical distribution data of the multi-core fiber optic sensor, calculate the curvature and torsion of the endoscope based on the fiber core deformation information and the helical distribution data, and perform three-dimensional reconstruction of the endoscope based on the curvature, torsion, and a preset reconstruction algorithm to obtain the three-dimensional shape, including: Acquire the helical distribution data of the multi-core fiber optic sensor, the helical distribution data including pitch, helix angle and azimuth angle of each fiber core relative to the central fiber core; Based on the spiral distribution data, including pitch, helix angle, and azimuth angle of each fiber core relative to the central fiber core, the strain value of each fiber core is corrected to obtain the strain distribution of the target fiber core. Based on the strain distribution of the target fiber core, a strain-curvature mapping relationship is constructed, and the local curvature and torsion of the endoscope are calculated based on the curvature mapping relationship. The endoscope is reconstructed in three dimensions based on its local curvature, torsion, and a preset reconstruction algorithm to obtain its three-dimensional shape.

5. An endoscope-assisted system as described in claim 4, characterized in that, The three-dimensional reconstruction module is used to perform three-dimensional reconstruction of the endoscope based on the local curvature, torsion, and a preset reconstruction algorithm to obtain its three-dimensional shape, including: Based on the local curvature and torsion of the endoscope, the spatial curve integral method is used to reconstruct the coordinates piece by piece to obtain the three-dimensional discrete spatial coordinate set of the endoscope. The three-dimensional discrete space coordinate set is processed into a continuous form based on a preset fitting algorithm to generate a three-dimensional centerline model of the endoscope. The three-dimensional shape of the endoscope is established based on the three-dimensional centerline model combined with the external diameter parameters of the endoscope.

6. A control method for an endoscope-assisted system, characterized in that, An endoscope-assisted system as described in any one of claims 1 to 5 is provided, wherein the endoscope-assisted control system comprises: a multi-core fiber optic sensor, a loop formation recognition module, and a visualization module; The multi-core fiber optic sensor is spirally distributed within the endoscope body along the axial direction of the endoscope body; a column of fiber Bragg grating arrays arranged longitudinally is written into the side core or outer core of the multi-core fiber optic sensor. The loop formation identification module receives and analyzes the reflection spectrum signal of the fiber Bragg grating array in real time, constructs the three-dimensional shape of the endoscope body based on the reflection spectrum signal, and identifies whether the endoscope body has loops based on the three-dimensional shape to obtain the loop formation result. The visualization module is driven by the loop formation results to visualize the three-dimensional shape and the loop formation results.

7. The control method for an endoscope-assisted system as described in claim 6, characterized in that, The loop formation identification module receives and analyzes the reflection spectrum signal of the fiber Bragg grating array in real time, constructs the three-dimensional shape of the endoscope body based on the reflection spectrum signal, and identifies whether a loop has formed in the endoscope body based on the three-dimensional shape, obtaining the loop formation result, including: The reflection spectrum signal of the fiber Bragg grating array is received and analyzed in real time, and the core deformation information is calculated based on the reflection spectrum signal. The spiral distribution data of the multi-core fiber optic sensor is acquired, the curvature of the endoscope is calculated based on the fiber core deformation information and the spiral distribution data, and the endoscope is reconstructed in three dimensions based on the endoscope curvature and a preset reconstruction algorithm to obtain the three-dimensional shape. Based on the preset recognition model and the three-dimensional shape, the loop type and degree of loop formation of the endoscope are identified.

8. The control method for an endoscope-assisted system as described in claim 7, characterized in that, The real-time reception and analysis of the reflection spectrum signal of the fiber Bragg grating array, and the calculation of fiber core deformation information based on the reflection spectrum signal, includes: The reflection spectrum signal is obtained based on the fiber Bragg grating array, and peak spectrum detection is performed on the reflection spectrum signal to obtain the center wavelength and corresponding signal-to-noise ratio of each grating. The temperature signal is acquired in real time, the center wavelength is corrected based on a preset temperature compensation model, the wavelength change is obtained, and the strain value of each fiber core is calculated based on the wavelength change.

9. The control method for an endoscope-assisted system as described in claim 8, characterized in that, The process involves acquiring the helical distribution data of the multi-core fiber optic sensor, calculating the curvature of the endoscope based on the fiber core deformation information and the helical distribution data, and performing three-dimensional reconstruction of the endoscope based on the endoscope curvature and a preset reconstruction algorithm to obtain its three-dimensional shape, including: Acquire the helical distribution data of the multi-core fiber optic sensor, the helical distribution data including pitch, helix angle and azimuth angle of each fiber core relative to the central fiber core; Based on the spiral distribution data, including pitch, helix angle, and azimuth angle of each fiber core relative to the central fiber core, the strain value of each fiber core is corrected to obtain the strain distribution of the target fiber core. Based on the strain distribution of the target fiber core, a strain-curvature mapping relationship is constructed, and the local curvature and torsion of the endoscope are calculated based on the curvature mapping relationship. The endoscope is reconstructed in three dimensions based on its local curvature, torsion, and a preset reconstruction algorithm to obtain its three-dimensional shape.

10. The control method for an endoscope-assisted system as described in claim 9, characterized in that, The method of performing three-dimensional reconstruction of the endoscope based on the local curvature, torsion, and a preset reconstruction algorithm to obtain the three-dimensional shape includes: Based on the local curvature and torsion of the endoscope, the spatial curve integral method is used to reconstruct the coordinates piece by piece to obtain the three-dimensional discrete spatial coordinate set of the endoscope. The three-dimensional discrete space coordinate set is processed into a continuous form based on a preset fitting algorithm to generate a three-dimensional centerline model of the endoscope. The three-dimensional shape of the endoscope is established based on the three-dimensional centerline model combined with the external diameter parameters of the endoscope.

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