Medical lumen instrument disinfection method

Abstract

The invention provides a medical lumen instrument disinfection method which comprises the following steps: acquiring a three-dimensional digital model of a lumen instrument through scanning equipment, extracting lumen data and generating standardized structure parameters; constructing a docking interface template library according to the standardized structure parameters, and generating an initial interface model; loading an initial interface model and inner cavity data through fluid mechanics simulation software, simulating flow distribution of a sterilizing agent and determining flow blocking and leakage positions; geometric parameters of the initial interface model are adjusted according to flow blocking and leakage positions, and a docking system model with enhanced adaptability is generated; calculating the flow velocity and pressure parameters of a sterilizing agent according to the suitability-enhanced docking system model and the inner cavity data, and generating flow parameter configuration; the injection rate and the medium viscosity are adjusted according to the flow parameter configuration and the inner cavity attribute, and a sterilization parameter set is generated; and driving the docking system model to execute a sterilization operation through the sterilization parameter set, collecting coverage distribution data and generating an optimization control scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of medical device disinfection, and particularly to a disinfection method for medical lumen devices. Background Art

[0002] The sterilization and disinfection of medical devices is a crucial research direction in the field of modern medicine, directly related to patient safety and the effectiveness of infection control. With the widespread application of minimally invasive surgery and complex medical devices, ensuring the aseptic state of the devices has become a key link that cannot be ignored. Traditional sterilization methods, such as high-temperature and high-pressure steam or chemical immersion, can handle simple devices, but are often inadequate when dealing with complex lumen structures. The limitations of these methods are mainly reflected in the difficulty of reaching the narrow channels inside the devices, resulting in uneven distribution of the sterilizing agent and the persistent risk of residual pathogens. In addition, traditional technologies lack adaptability to different device shapes, often reducing the sterilization efficiency due to inaccurate docking, and even damaging the devices themselves.

[0003] In this field, the core challenges focus on how to ensure the uniform distribution and full action of the sterilizing agent in lumen devices. Especially for lumen devices such as endoscopes and catheters, due to their long and narrow inner cavities and various shapes, the precise docking of the flow channels during the sterilization process has become one of the technical difficulties. If the docking interface does not match the device channels, the flow of the sterilizing agent will be blocked, and even leakage may occur, affecting the deep sterilization effect. At the same time, the control of flow rate and pressure is also a key factor. Unoptimized parameters may lead to insufficient sterilization or over-action in some areas, making it difficult to balance efficiency and device safety. These technical factors have not been effectively solved, directly resulting in unique problems faced by existing methods in the sterilization of complex devices: how to achieve continuous and uniform coverage of the sterilizing agent throughout the lumen without changing the device structure.

[0004] Therefore, how to design a technology that can accurately dock with the channels of various lumen devices and ensure the uniform distribution of the sterilizing agent has become a key issue in improving the sterilization and disinfection effect of medical devices. This issue not only requires the docking system to have high adaptability and sealing performance, but also needs to optimize the flow parameters through intelligent means to cope with the diversity of lumen length and diameter. Only by overcoming this problem can the bottleneck of the traditional sterilization method's inability to reach the inner cavity be completely solved, providing a more reliable guarantee for medical safety. Summary of the Invention

[0005] The present invention provides a disinfection method for medical lumen instruments, mainly including: obtaining a three-dimensional digital model of the lumen instrument through a scanning device, extracting the inner lumen data and generating standardized structural parameters; constructing a docking interface template library according to the standardized structural parameters and generating an initial interface model; loading the initial interface model and the inner lumen data through a fluid dynamics simulation software, simulating the flow distribution of the sterilizing agent and determining the positions where the flow is blocked and leaked; adjusting the geometric parameters of the initial interface model according to the positions where the flow is blocked and leaked to generate a docking system model with enhanced adaptability; calculating the flow rate and pressure parameters of the sterilizing agent according to the docking system model with enhanced adaptability and the inner lumen data to generate a flow parameter configuration; adjusting the injection rate and the medium viscosity according to the flow parameter configuration and the inner lumen attributes to generate a set of sterilization parameters; driving the docking system model to perform a sterilization operation through the set of sterilization parameters, collecting coverage distribution data and generating an optimized control scheme.

[0006] Further, the step of obtaining a three-dimensional digital model of the lumen instrument through a scanning device, extracting the inner lumen data and generating standardized structural parameters includes: collecting the data of the lumen instrument through a scanning device to generate a three-dimensional digital model; using a stereoscopic geometry algorithm to process the three-dimensional digital model to extract the long and narrow and morphological data of the inner lumen; if the long and narrow degree of the inner lumen exceeds a preset threshold, adjusting the morphological data through a surface fitting technique to obtain optimized geometric features; generating a pore description according to the optimized geometric features, using a mesh generation technique to refine the structure and determine the spatial distribution; matching the pore description with a standardized parameter template to obtain standardized structural parameters; using a principal component analysis algorithm to identify the differences between the standardized structural parameters and the three-dimensional digital model, determining the feature adjustment direction and updating the model.

[0007] Further, the step of constructing a docking interface template library according to the standardized structural parameters and generating an initial interface model includes: obtaining standardized data according to the standardized structural parameters and determining the basis of the template library; extracting preset rules from the template library to judge the morphological characteristic classification; using a classification matching method for the morphological characteristics to generate an initial model framework; adjusting the docking parameters according to the initial model framework and the standardized structural parameters to obtain a docking optimized model; analyzing the interface features through the docking optimized model to determine the final interface structure; extracting the docking accuracy information from the final interface structure and judging whether it meets the requirements, and adjusting the classification matching parameters according to the judgment result to generate an optimized initial interface model.

[0008] Further, loading the initial interface model and the inner cavity data by a fluid dynamics simulation software, simulating the flow distribution of the sterilizing agent, and determining the positions of flow blockage and leakage includes: loading the initial interface model and the long and narrow inner cavity data by the fluid dynamics simulation software to obtain the preliminary flow distribution data of the sterilizing agent; processing the preliminary flow distribution data by using a simulation analysis technique to determine the changing trend of the flow distribution; if the changing trend shows that the flow velocity abnormally decreases, judging the distribution of the flow blockage positions by a fluid dynamics algorithm; obtaining the influence range of the blockage according to the flow blockage position distribution data; and determining the external leakage position distribution data by comparing the blockage influence range with an external leakage hypothesis and integrating it into the final distribution result.

[0009] Further, adjusting the geometric parameters of the initial interface model according to the flow blockage and leakage positions to generate a docking system model with enhanced adaptability includes: extracting the spatial distribution characteristics of the blocked area from the flow blockage and leakage positions; adjusting the geometric parameters of the initial interface model for the blocked area by using an iterative optimization algorithm to determine the optimized interface geometric shape; updating the initial interface model according to the optimized interface geometric shape to generate an interface model with enhanced adaptability; generating a preliminary docking system model by the interface model with enhanced adaptability and judging whether the adaptability meets a preset threshold; if the preset threshold is not met, adjusting the geometric parameters again by the iterative optimization algorithm to obtain an updated docking system model and verifying the optimized distribution.

[0010] Further, calculating the flow velocity and pressure parameters of the sterilizing agent according to the docking system model with enhanced adaptability and the inner cavity data to generate a flow parameter configuration includes: fusing the long and narrow and bifurcated structure data of the inner cavity by the docking system model with enhanced adaptability to determine the distribution of the pore characteristics; calculating the dynamic change of the sterilizing agent according to the pore characteristic distribution to obtain the initial value of the flow velocity range and the initial value of the pressure threshold; generating basic control parameters by using a data fusion technique in combination with the initial value of the flow velocity range and the initial value of the pressure threshold; adjusting the basic control parameters by a flow control algorithm to determine the optimized value of the flow velocity range; if the optimized value of the flow velocity range exceeds the preset threshold, correcting the pressure threshold by model calculation to generate an optimized value of the pressure control and forming a complete flow parameter configuration.

[0011] Further, configure the injection rate and medium viscosity according to the flow parameters and adjust the inner cavity attributes to generate a sterilization parameter set, including: obtaining initial configuration data through the configuration of the flow parameters and determining the reference value of the flow parameters; calculating the flow distribution characteristics according to the inner cavity inlet width attribute; if the injection rate exceeds the preset threshold, adjusting the rate value according to the flow distribution characteristics to determine the optimized injection rate; extracting the viscosity adjustment range from the medium viscosity and the flow distribution characteristics to determine the adapted viscosity value; generating a preliminary parameter set using the optimized injection rate and the adapted viscosity value; optimizing the preliminary parameter set through the support vector machine algorithm to generate the final sterilization parameters and verifying the consistency with the initial configuration.

[0012] Further, drive the docking system model to perform sterilization operations through the sterilization parameter set, collect coverage distribution data, and generate an optimized control plan, including: driving the docking system model to perform sterilant injection through the sterilization parameter set to generate an initial operation instruction; obtaining the sterilant injection distribution characteristics from the docking system model to determine the real-time adjustment parameters; using real-time monitoring equipment to record the change trend of the coverage distribution and obtaining the sterilization state data inside the pore; if abnormal fluctuations in the coverage distribution are detected, generating corrected distribution data by adjusting the sterilant injection amount; extracting the boundary characteristics of the defective area from the corrected distribution data, judging the insufficient sterilization coverage range, and generating defective area data; determining supplementary injection parameters according to the defective area data and performing secondary injection.

[0013] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: The present invention discloses a method for precise sterilization of lumen instruments. The method scans to obtain the three-dimensional model of the lumen instrument, extracts the inner cavity characteristics and generates standardized structure parameters, and constructs an interface model for precise docking. The interface design is optimized by fluid mechanics simulation to determine the sterilant flow parameters. By dynamically adjusting the parameter configuration, the uniform distribution of the sterilant in the complex lumen is realized. The present invention also adopts real-time monitoring and local flow field enhancement technologies to solve the problem of uneven sterilization coverage, and adjusts the flow rate control range according to the pressure distribution data, and finally generates a sterilization operation parameter configuration applicable to various lumen structures. This method can effectively solve the problem of uneven distribution of traditional sterilization technologies in complex lumen instruments, improve the sterilization effect and safety, and is of great significance for improving the sterilization quality of medical devices. Description of the Drawings

[0014] Figure 1 It is a flowchart of the present invention.

[0015] Figure 2 It is a schematic diagram of the present invention. Detailed Embodiments

[0016] To enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this specification without making creative efforts shall fall within the scope of protection of this specification.

[0017] As Figure 1-2 , this embodiment may specifically include: S101. Obtain the three-dimensional digital model of the lumen instrument from the scanning device, extract the data of the long and narrow degree and various shapes of the inner lumen, generate an accurate geometric feature description of the instrument channel, and determine the standardized structure parameters.

[0018] Collect the data of the lumen instrument through the scanning device to generate a three-dimensional digital model and obtain the initial structure expression. Use the stereoscopic geometry algorithm to process the three-dimensional digital model, extract the long and narrow and shape data of the inner lumen, and determine the preliminary feature information. If the long and narrow degree of the inner lumen exceeds the preset threshold, adjust the shape data through the surface fitting technology to obtain the optimized geometric features. Generate the channel description according to the optimized geometric features, use the mesh division technology to refine the structure, and determine the accurate spatial distribution. Match the channel description with the standardized parameter template to judge whether it meets the preset specifications and obtain the standardized structure parameters. Obtain the difference data between the standardized structure parameters and the initial three-dimensional digital model, use the principal component analysis algorithm to identify the key change trends, and determine the final feature adjustment direction. Update the digital model according to the final feature adjustment direction to generate a channel description of the lumen instrument that meets the standardized requirements. Collect the surface texture data of the lumen instrument through the scanning device, use the image processing technology to extract various shape features, and obtain the supplementary geometric feature description. Adjust the channel description according to the supplementary geometric feature description, use the data fusion technology to integrate the long and narrow degree and shape data, and determine the extended standardized structure parameters.

[0019] Specifically, the data of the lumen instrument is collected by a scanning device to generate a three-dimensional point cloud model. The Poisson reconstruction algorithm is used to convert the point cloud data into a three-dimensional digital model to obtain an initial structural expression. The stereoscopic geometry algorithm is used to extract features from the three-dimensional digital model, and the narrowness and morphological data of the inner cavity are calculated through curvature analysis and cross-section measurement to determine the preliminary feature information. If the narrowness of the inner cavity exceeds a preset threshold (such as the aspect ratio is greater than 10:1), the NURBS surface fitting technology is used to smooth the morphological data to obtain optimized geometric features. According to the optimized geometric features, a pore description is generated, and the Delaunay triangulation algorithm is used to mesh the pore structure to determine the accurate spatial distribution. The pore description is matched with a standardized parameter template (such as the ISO standard), and the Euclidean distance algorithm is used to determine whether the pore meets the preset specifications to obtain the standardized structural parameters. The difference data between the standardized structural parameters and the initial three-dimensional digital model is obtained, and the principal component analysis algorithm (PCA) is used to reduce the dimension of the difference data to identify the key change trends and determine the final feature adjustment direction. The digital model is updated according to the final feature adjustment direction, and the B-spline interpolation algorithm is used to adjust the pore geometry to generate a pore description of the lumen instrument that meets the standardized requirements. The surface texture data of the lumen instrument collected by the scanning device is used, and the Canny edge detection algorithm is used to extract various morphological features to obtain supplementary geometric feature descriptions. The pore description is adjusted according to the supplementary geometric feature descriptions, and the weighted average data fusion technology is used to integrate the narrowness and morphological data to determine the extended standardized structural parameters.

[0020] S102. According to the standardized structural parameters, a preset docking interface template library is constructed, and classification matching is performed for various morphological characteristics to generate an initial interface model that accurately docks with the instrument pore.

[0021] The data of the lumen instrument is collected by a scanning device to generate a three-dimensional digital model to obtain an initial structural expression. The stereoscopic geometry algorithm is used to process the three-dimensional digital model to extract the narrowness and morphological data of the inner cavity to determine the preliminary feature information. According to the preliminary feature information, a pore description is generated, and the mesh division technology is used to refine the structure to determine the accurate spatial distribution. The pore description is matched with a standardized parameter template to determine whether it meets the preset specifications to obtain the standardized structural parameters. According to the standardized structural parameters, a preset docking interface template library is constructed, and various morphological characteristics are classified to determine the basis of the template library. The preset rules are extracted from the template library to determine the morphological characteristic classification to obtain the basis for classification matching. The classification matching method is used to process the basis for classification matching to generate an initial model framework to determine the initial interface structure. The docking parameters between the initial interface structure and the instrument pore data are obtained, and the docking parameters are adjusted to obtain a docking optimization model. The interface model features are analyzed through the docking optimization model, and the docking accuracy information is extracted to determine the final interface structure.

[0022] Specifically, the data of the lumen instrument is collected by a scanning device with a resolution of 0.1 mm, and a three-dimensional digital model is generated using a point cloud reconstruction algorithm to obtain an initial structural expression. The three-dimensional digital model is processed using a stereoscopic geometry algorithm to extract the data of the narrowness degree and morphology of the inner cavity. If the aspect ratio of the inner cavity is greater than 10, it is determined as a narrow structure, and the preliminary feature information is determined. According to the preliminary feature information, a pore description is generated, and the Delaunay triangulation algorithm is used for mesh division to divide the pore into mesh units with a side length of 0.5 mm to determine the precise spatial distribution. The pore description is matched with a standardized parameter template. If the mesh unit density is between 100 and 200 per cubic millimeter, it is determined to meet the preset specifications, and the standardized structural parameters are obtained. According to the standardized structural parameters, a preset docking interface template library is constructed, and the various characteristics of the inner cavity diameter in the range of 1 to 5 mm are classified to determine the basis of the template library. The preset rules are extracted from the template library. If the inner cavity diameter is less than 3 mm and the bifurcation angle is greater than 30 degrees, it is determined as a complex morphology classification, and the classification matching basis is obtained. The classification matching method is used to process the classification matching basis, and the K-means clustering algorithm is used to divide the pores with similar morphologies into 5 categories to generate an initial model framework and determine the initial interface structure. The docking parameters of the initial interface structure and the instrument pore data are obtained, and the least squares method is used to adjust the interface diameter deviation within 0.1 mm to obtain a docking optimization model. The interface model features are analyzed through the docking optimization model, and the docking accuracy information is extracted. If the gap between the interface and the pore is less than 0.05 mm, it is determined as a high-precision docking, and the final interface structure is determined.

[0023] S103. Through a fluid dynamics simulation software, the initial interface model and the lumen narrow data are loaded to simulate the flow distribution of the sterilizing agent in the pore to determine the position distribution data of the flow blockage and external leakage.

[0024] Load the initial interface model and the data of the long and narrow lumen through a fluid dynamics simulation software, simulate the flow distribution of the sterilant in the channels, and determine the preliminary flow distribution data. Use simulation analysis techniques to process the preliminary flow distribution data, obtain the changing trend of the flow velocity, and judge the specific position distribution of the flow blockage. Based on the flow blockage position distribution data, obtain the blockage influence range related to the characteristics of the long and narrow lumen, and get the spatial distribution characteristics of the blocked area. By comparing the blockage influence range with the external leakage hypothesis, integrate the position distribution data of the external leakage, and determine the preliminary leakage distribution result. Use data loading techniques to process the position distribution data of the flow blockage and the external leakage, generate comprehensive distribution data, and obtain the final distribution determination result. According to the final distribution determination result, extract the spatial characteristics of the flow-blocked area, and determine the boundary data of the blocked area. Use an iterative optimization algorithm to process the boundary data of the blocked area, adjust the geometric parameters of the initial interface model, and obtain an interface model with enhanced adaptability. If the adaptability of the interface model with enhanced adaptability does not reach the preset threshold, use the iterative optimization algorithm to adjust the geometric parameters again to obtain an updated docking system model. Analyze the changing trend of the blocked area based on the updated docking system model, obtain the optimized distribution of the flow blockage and the external leakage, and determine the final distribution characteristics.

[0025] Specifically, load the initial interface model and the data of the long and narrow lumen through a fluid dynamics simulation software, simulate the flow distribution of the sterilant in the channels, calculate the flow velocity and pressure distribution using the finite volume method, and obtain the preliminary flow distribution data. Use simulation analysis techniques to process the preliminary flow distribution data, calculate the gradient change of the flow velocity, identify the areas where the flow velocity is lower than 0.5 m / s, and judge the specific position distribution of the flow blockage. Based on the flow blockage position distribution data, combine the geometric parameters of the long and narrow lumen characteristics, use the region growing algorithm to calculate the blockage influence range, and obtain the spatial distribution characteristics of the blocked area. By comparing the blockage influence range with the external leakage hypothesis, use the boundary matching algorithm to integrate the position distribution data of the external leakage, and determine the preliminary leakage distribution result. Use data loading techniques to process the position distribution data of the flow blockage and the external leakage, generate comprehensive distribution data through the weighted fusion algorithm, and obtain the final distribution determination result. According to the final distribution determination result, extract the spatial characteristics of the flow-blocked area, use the clustering analysis algorithm to determine the boundary data of the blocked area. Use an iterative optimization algorithm to process the boundary data of the blocked area, use the genetic algorithm to adjust the geometric parameters of the initial interface model, optimize the interface diameter to 3 mm, and obtain an interface model with enhanced adaptability. If the adaptability of the interface model with enhanced adaptability does not reach the preset threshold, use the iterative optimization algorithm to adjust the geometric parameters again, optimize the interface curvature to 0.1, and obtain an updated docking system model. Analyze the changing trend of the blocked area based on the updated docking system model, use the dynamic grid reconstruction technology to obtain the optimized distribution of the flow blockage and the external leakage, and determine the final distribution characteristics.

[0026] S104. Extract the flow - blocked area from the position - distribution data of flow blockage and external leakage, and use an iterative optimization algorithm to adjust the geometric parameters of the initial interface model to generate a docking system model with enhanced adaptability.

[0027] Load the initial interface model and the data of the long and narrow lumen through a fluid mechanics simulation software to simulate the flow distribution of the sterilant in the channels, and determine the position - distribution data of flow blockage and external leakage. According to the position - distribution data of flow blockage and external leakage, use simulation analysis technology to extract the flow - blocked area and obtain the preliminary distribution characteristics of the blocked area. Process the preliminary distribution characteristics of the blocked area through an iterative optimization algorithm, adjust the geometric parameters of the initial interface model, and generate an interface geometric shape with enhanced adaptability. Update the initial interface model according to the interface geometric shape with enhanced adaptability to obtain a preliminary model of the docking system with enhanced adaptability. Analyze the preliminary model of the docking system with enhanced adaptability through position - distribution data, extract the blocked areas of flow blockage and external leakage, and obtain the spatial distribution characteristics of the blocked area. Use an iterative optimization algorithm to adjust the geometric parameters according to the spatial distribution characteristics of the blocked area, and determine the optimized interface geometric shape. Generate an updated docking system model according to the optimized interface geometric shape, and judge whether the adaptability meets the preset threshold through the flow - distribution data. If the adaptability does not reach the preset threshold, readjust the geometric parameters through an iterative optimization algorithm to obtain a docking system model with enhanced adaptability. Analyze the optimized distribution of flow blockage and external leakage through the docking system model with enhanced adaptability to determine the final docking system model.

[0028] Specifically, the initial interface model and the narrowness and length of the lumen are loaded through the fluid mechanics simulation software to simulate the flow distribution of the sterilant in the channel, obtain the flow rate range of 0.5 to 1.2 meters per second, the pressure range of 50 to 120 kilopascals, and determine the location distribution data of flow blockage and external leakage. According to the location distribution data of flow blockage and external leakage, the flow obstruction area is extracted by simulation analysis technology to obtain the preliminary distribution characteristics of the obstructed area, for example, the flow rate drops to 0.3 meters per second at a lumen length of 30 mm. The preliminary distribution characteristics of the obstructed area are processed by the iterative optimization algorithm, the geometric parameters of the initial interface model are adjusted, the lumen diameter is optimized from 2 mm to 2.5 mm, and the interface geometry with enhanced adaptability is generated. The initial interface model is updated according to the interface geometry with enhanced adaptability to obtain the preliminary model of the docking system with enhanced adaptability, and its flow rate range is increased to 0.6 to 1.3 meters per second. The preliminary model of the docking system with enhanced adaptability was analyzed by position distribution data, and the obstructed areas of flow obstruction and external leakage were extracted to obtain the spatial distribution characteristics of the obstructed areas, such as the pressure fluctuation range of ±10 kPa at the bifurcation of the lumen. The geometric parameters were adjusted according to the spatial distribution characteristics of the obstructed areas by using an iterative optimization algorithm, and the bifurcation angle was optimized from 45 degrees to 30 degrees to determine the optimized interface geometry. The updated docking system model was generated according to the optimized interface geometry, and the flow distribution data was used to determine whether the adaptability met the preset threshold, such as the flow rate was stable at 0.8 to 1.1 meters per second. If the adaptability did not reach the preset threshold, the geometric parameters were adjusted again by the iterative optimization algorithm, and the lumen length was shortened from 30 mm to 25 mm to obtain the docking system model with enhanced adaptability. The optimized distribution of flow obstruction and external leakage was analyzed by the docking system model with enhanced adaptability, and the final docking system model was determined, with a flow rate range of 0.9 to 1.1 meters per second and a pressure range of 60 to 100 kPa.

[0029] S105. According to the docking system model with enhanced adaptability, combined with the narrow and long characteristics of the lumen and the bifurcation structure data, the flow rate control range and pressure control threshold of the sterilant in the channel are calculated to generate an initial flow parameter configuration.

[0030] According to the docking system model with enhanced adaptability, integrating the characteristics of long and narrow lumens and bifurcation structure data, calculate the initial values of the flow velocity range and the initial value of the pressure threshold of the sterilant in the pore. Use data fusion technology to process the initial values of the flow velocity range and the pressure threshold to generate the basic control parameters of the docking system. Adjust the basic control parameters through a flow control algorithm to determine the optimized value of the flow velocity range under enhanced adaptability. If the optimized value of the flow velocity range exceeds the preset threshold, correct the pressure threshold through model calculation to obtain the optimized pressure control value. Based on the optimized pressure control value and the optimized flow velocity range, generate a complete configuration of the initial parameters to obtain a set of flow parameters. Verify the set of flow parameters through the docking system, judge the parameter consistency, and obtain the final flow parameter configuration. Use a fluid mechanics simulation software to load the final flow parameter configuration and the long and narrow lumen data to simulate the flow distribution of the sterilant in the pore and determine the preliminary flow distribution data. Process the preliminary flow distribution data through simulation analysis technology to obtain the change trend of the flow distribution and obtain the judgment result of abnormally reduced flow velocity. According to the judgment result of abnormally reduced flow velocity, use a fluid mechanics algorithm to analyze the change trend and determine the specific location distribution data of the flow blockage.

[0031] Specifically, according to the docking system model with enhanced adaptability, integrating the characteristics of long and narrow lumens and bifurcation structure data, use the hydrodynamic equation to calculate that the initial value of the flow velocity range of the sterilant in the pore is 0.2 to 0.5 meters per second, and the initial value of the pressure threshold is 100 to 150 kPa. Use data fusion technology to process the initial values of the flow velocity range and the pressure threshold, and generate the basic control parameters of the docking system through a weighted average algorithm, including a flow velocity control coefficient of 0.35 and a pressure adjustment factor of 1.2. Adjust the basic control parameters through a flow control algorithm, and use a PID controller to optimize the flow velocity range to 0.25 to 0.45 meters per second. If the optimized value of the flow velocity range exceeds the preset threshold of 0.5 meters per second, correct the pressure threshold to 130 to 160 kPa through the Newton iteration method to obtain the optimized pressure control value. Based on the optimized pressure control value and the optimized flow velocity range, generate a complete configuration of the initial parameters, including the flow velocity control interval and the pressure adjustment range, to obtain a set of flow parameters. Verify the set of flow parameters through the docking system, use the least squares method to judge the parameter consistency, and obtain the final flow parameter configuration of a flow velocity of 0.3 to 0.4 meters per second and a pressure of 140 to 155 kPa. Use a fluid mechanics simulation software to load the final flow parameter configuration and the long and narrow lumen data to simulate the flow distribution of the sterilant in the pore and determine the preliminary flow distribution data, including the flow velocity and pressure values of each section. Process the preliminary flow distribution data through simulation analysis technology to obtain the change trend of the flow distribution and obtain the judgment result that the flow velocity abnormally decreases to 0.15 meters per second in the third section of the pore. According to the judgment result of abnormally reduced flow velocity, use the finite element analysis method to analyze the change trend and determine that the specific location distribution data of the flow blockage is at the second bifurcation of the third section of the pore, and the blockage influence range is 5 mm.

[0032] S106. Configure according to the initial flow parameters and the properties of the pore inlet width, adjust the injection rate and the medium viscosity, and generate a set of sterilization parameters adapted to the flow distribution.

[0033] Obtain the initial configuration data through the preset flow parameters to determine the reference value of the flow parameters. Calculate the flow distribution characteristics based on the pore width and the inlet properties to obtain the basis for distribution adaptation. If the injection rate exceeds the preset threshold, adjust the rate value in combination with the flow distribution characteristics, and judge the optimized injection rate. Extract the viscosity adjustment range from the medium viscosity and the flow distribution characteristics to determine the adapted viscosity value. Generate a preliminary set of parameters using the adjusted injection rate and viscosity value to obtain the initial draft of the sterilization parameters. Optimize the preliminary set of parameters through the support vector machine algorithm to obtain the final sterilization parameters. Verify the consistency between the flow distribution and the initial configuration according to the final sterilization parameters to determine the adaptation result. Adjust the dynamic control range of the injection rate in combination with the pore bifurcation structure and the narrow and long characteristics to obtain the operating parameters for full pore coverage. Generate a set of sterilization parameters adapted to the flow distribution through the operating parameters to determine the final configuration data.

[0034] Specifically, obtain the initial configuration data through the preset flow parameters. For example, set the initial flow rate to 5 mL / min and the pressure to 0.1 MPa to determine the reference value of the flow parameters. Calculate the flow distribution characteristics based on the pore width and the inlet properties. For example, the pore width is 0.5 mm, and use the Navier-Stokes equation to simulate the flow distribution to obtain the basis for distribution adaptation. If the injection rate exceeds the preset threshold, for example, the threshold is 10 mL / min, adjust the rate value in combination with the flow distribution characteristics, and use the bisection method to gradually approach the optimal rate, and judge that the optimized injection rate is 8 mL / min. Extract the viscosity adjustment range from the medium viscosity and the flow distribution characteristics. For example, the viscosity range is 1.0 - 2.0 mPa·s, and fit the adaptation curve by the least squares method to determine the adapted viscosity value to be 1.5 mPa·s. Generate a preliminary set of parameters using the adjusted injection rate and viscosity value. For example, set the flow rate to 8 mL / min and the viscosity to 1.5 mPa·s to obtain the initial draft of the sterilization parameters. Optimize the preliminary set of parameters through the support vector machine algorithm, set the kernel function to RBF, and the penalty coefficient C to 1.0 to obtain the final sterilization parameters. Verify the consistency between the flow distribution and the initial configuration according to the final sterilization parameters, and use the residual analysis method to calculate that the error is less than 5% to determine the adaptation result. Adjust the dynamic control range of the injection rate in combination with the pore bifurcation structure and the narrow and long characteristics. For example, the bifurcation width is 0.3 mm, and use the PID control algorithm to adjust the rate to obtain the operating parameters for full pore coverage. Generate a set of sterilization parameters adapted to the flow distribution through the operating parameters. For example, set the dynamic flow rate range to 6 - 10 mL / min to determine the final configuration data.

[0035] S107. In the simulation of the sterilization parameter set, if the uniformity of the sterilant distribution is lower than the preset 90% threshold, the flow rate control range and the pressure control threshold are adjusted by the gradient descent algorithm with uniformity as the objective function to generate a dynamic adjustment parameter configuration; if the uniformity is higher than the threshold, the sterilization parameter set is directly adopted.

[0036] By collecting sterilant distribution data, calculating the uniformity value, and obtaining the uniformity evaluation result. If the uniformity evaluation result is lower than the preset 90% threshold, the flow rate control range and the pressure control threshold are obtained, input into the gradient descent algorithm, and the dynamic adjustment parameter configuration is determined. The flow rate control range is adjusted through the dynamic adjustment parameter configuration to generate updated flow rate distribution data. The pressure control threshold is adjusted through the dynamic adjustment parameter configuration to generate updated pressure distribution data. The sterilant distribution characteristics are extracted from the updated flow rate distribution data and pressure distribution data to determine whether the uniformity is higher than the preset threshold. If the uniformity is still lower than the preset threshold, the gradient descent algorithm is repeatedly executed to obtain a new dynamic adjustment parameter configuration. The final sterilization parameter set is determined through the flow rate distribution data and pressure distribution data after multiple adjustments. According to the docking system model with enhanced adaptability, the tube cavity narrow and long characteristics and the bifurcation structure data are integrated, and the flow rate control range and the pressure control threshold of the sterilant in the pore are calculated to obtain the initial flow parameter configuration. The initial flow parameter configuration is verified through the docking system to judge the parameter consistency and obtain the final flow parameter configuration.

[0037] Specifically, by collecting the distribution data of the sterilizing agent, the spatial distribution statistical analysis algorithm is used to calculate the distribution uniformity value. For example, the uniformity evaluation result calculated by the coefficient of variation method is 85%. If the uniformity evaluation result is lower than the preset threshold of 90%, the current flow rate control range of 0.5 - 1.5 m / s and the pressure control threshold of 50 - 100 kPa are obtained, and input into the gradient descent algorithm. With uniformity as the objective function, the dynamic adjustment parameter configuration is determined through iterative calculation as the flow rate control range of 0.6 - 1.4 m / s and the pressure control threshold of 60 - 90 kPa. The flow rate control range is adjusted through the dynamic adjustment parameter configuration to generate updated flow rate distribution data. For example, the flow rates at different positions in the lumen are 0.8 m / s, 1.2 m / s, and 1.0 m / s respectively. The pressure control threshold is adjusted through the dynamic adjustment parameter configuration to generate updated pressure distribution data. For example, the pressures at different positions in the lumen are 70 kPa, 85 kPa, and 75 kPa respectively. The sterilizing agent distribution characteristics are extracted from the updated flow rate distribution data and pressure distribution data, and the uniformity is judged to be 88% using the distribution uniformity calculation formula. If the uniformity is still lower than the preset threshold, the gradient descent algorithm is repeatedly executed to obtain a new dynamic adjustment parameter configuration as the flow rate control range of 0.7 - 1.3 m / s and the pressure control threshold of 65 - 85 kPa. Through the flow rate distribution data and pressure distribution data after multiple adjustments, the final sterilization parameter set is determined as the flow rate control range of 0.7 - 1.3 m / s and the pressure control threshold of 65 - 85 kPa. According to the docking system model with enhanced adaptability, by integrating the long and narrow characteristics of the lumen and the bifurcation structure data, the finite element analysis method is used to calculate the flow rate control range of the sterilizing agent in the pore channel as 0.5 - 1.5 m / s and the pressure control threshold as 50 - 100 kPa to obtain the initial flow parameter configuration. The initial flow parameter configuration is verified through the docking system, and the parameter consistency detection algorithm is used to judge the parameter consistency to obtain the final flow parameter configuration as the flow rate control range of 0.7 - 1.3 m / s and the pressure control threshold of 65 - 85 kPa.

[0038] S108. According to the dynamic adjustment parameter configuration, drive the docking system model to perform the sterilizing agent injection operation, record the distribution data of the sterilization coverage in the pore channel in the real-time monitoring device, and generate the sterilization coverage defect area data.

[0039] According to the dynamic adjustment parameter configuration, drive the docking system model to perform the sterilant injection operation, and record the sterilization coverage distribution data in the channels of the real-time monitoring device. Through the distribution data in the real-time monitoring device, analyze the coverage range of the sterilant injection, and generate the sterilization coverage defect area data. Extract the boundary features from the defect area data, judge the area range of insufficient sterilization coverage, and obtain the distribution characteristics of the defect area. According to the distribution characteristics, calculate the sterilant concentration distribution in the defect area, and determine the operation parameters for supplementary injection. Through the operation parameters of the supplementary injection, drive the system model to perform the secondary sterilant injection, and obtain the updated coverage distribution data. Using the updated coverage distribution data, calculate the sterilant distribution uniformity value, and obtain the uniformity evaluation result. If the uniformity evaluation result is lower than the preset threshold, obtain the flow rate control range and the pressure control threshold, input the gradient descent algorithm, and determine the new dynamic adjustment parameter configuration. Through the new dynamic adjustment parameter configuration, adjust the flow rate control range and the pressure control threshold, and generate the updated flow rate distribution data and pressure distribution data. Extract the sterilant distribution characteristics from the updated flow rate distribution data and pressure distribution data, judge whether the uniformity is higher than the preset threshold, and obtain the final sterilization parameter set.

[0040] Specifically, according to the dynamic adjustment parameter configuration, drive the docking system model to perform the sterilant injection operation, inject the sterilant with parameters of a flow rate of 2.5 mL / min and a pressure of 0.3 MPa, and record the sterilization coverage distribution data in the pore channel through a real-time monitoring device. Analyze the distribution data in the real-time monitoring device, use an image processing algorithm to identify the coverage range, generate the sterilization coverage defect area data, and the proportion of the defect area is 15%. Extract the boundary features from the defect area data, use an edge detection algorithm to judge the range of the area with insufficient sterilization coverage, and obtain the distribution characteristics of the defect area, which is manifested as a local concentration lower than 0.8 mg / L. According to the distribution characteristics, use an interpolation algorithm to calculate the sterilant concentration distribution in the defect area, and determine the operation parameters for supplementary injection as a flow rate of 3.0 mL / min and a pressure of 0.35 MPa. Drive the system model to perform the secondary sterilant injection through the operation parameters of the supplementary injection, and obtain the updated coverage distribution data, and the proportion of the defect area drops to 5%. Use the updated coverage distribution data to calculate the sterilant distribution uniformity value, and use variance analysis to obtain the uniformity evaluation result of 0.12. If the uniformity evaluation result is lower than the preset threshold of 0.10, then obtain the flow rate control range of 2.0 - 4.0 mL / min and the pressure control threshold of 0.2 - 0.4 MPa, input the gradient descent algorithm, and determine the new dynamic adjustment parameter configuration as a flow rate of 3.2 mL / min and a pressure of 0.38 MPa. Adjust the flow rate control range and the pressure control threshold through the new dynamic adjustment parameter configuration, and generate the updated flow rate distribution data and pressure distribution data. Extract the sterilant distribution characteristics from the updated flow rate distribution data and pressure distribution data, use a clustering algorithm to judge whether the uniformity is higher than the preset threshold, and obtain the final sterilization parameter set as a flow rate of 3.2 mL / min and a pressure of 0.38 MPa.

[0041] S109. Extract the uncovered positions from the sterilization coverage defect area data, and use the flow field enhancement method of locally increasing the flow rate to adjust the flow rate control range of the corresponding area, and generate a uniformly distributed sterilant flow control scheme.

[0042] Extract the uncovered positions from the data of the sterilization coverage defect area, and generate the coordinate data of the uncovered positions. Obtain the local flow requirements according to the coordinate data of the uncovered positions, and determine the range of the flow enhancement area. Adjust the local flow in the flow enhancement area by using the flow field enhancement method to obtain the parameters of the flow velocity control range. Calculate the distribution characteristics through the parameters of the flow velocity control range, and generate the initial data of the uniform distribution model. Optimize the flow path of the sterilant according to the initial data of the uniform distribution model, and determine the control parameters of the flow path. Generate the sterilant distribution plan through the control parameters of the flow path to obtain the preliminary data of the distribution plan. If the deviation in the preliminary data of the distribution plan exceeds the preset threshold, correct the corresponding area through flow adjustment to obtain the corrected distribution data. Extract the boundary characteristics of the defect area from the corrected distribution data, and judge the characteristic value of the insufficient sterilization coverage range. Adjust the supplementary injection parameters according to the characteristic value of the insufficient sterilization coverage range, and generate the uniform distribution of the sterilant flow control plan.

[0043] Specifically, extract the uncovered positions from the data of the sterilization coverage defect area, and generate the coordinate matrix of the uncovered positions by using the coordinate extraction algorithm. Calculate the local flow requirements according to the coordinate matrix, and use the flow requirement analysis algorithm to determine that the boundary range of the flow enhancement area is a circular area with a radius of 50 mm. Use the flow field enhancement method to adjust the local flow through the computational fluid dynamics (CFD) model to obtain the parameters of the flow velocity control range from 0.2 to 0.5 m / s. Through the flow velocity control range parameters, use the Monte Carlo simulation to calculate the distribution characteristics, and generate the initial data of the uniform distribution model, where the standard deviation of the sterilant concentration is controlled within 0.1. According to the initial data of the uniform distribution model, use the path optimization algorithm to optimize the flow path of the sterilant, and determine that the control parameters of the flow path include a flow velocity of 0.3 m / s and an angle of 45 degrees. Generate the sterilant distribution plan through the control parameters of the flow path to obtain the preliminary data of the distribution plan, where the sterilant coverage rate exceeds 95%. If the deviation in the preliminary data of the distribution plan exceeds the preset threshold of 0.05, correct the corresponding area through the flow adjustment algorithm to obtain the corrected distribution data, and the deviation value is reduced below 0.02. Extract the boundary characteristics of the defect area from the corrected distribution data, and use the boundary detection algorithm to judge that the characteristic value of the insufficient sterilization coverage range is the area with a coverage rate lower than 90%. Adjust the supplementary injection parameters according to the characteristic value of the insufficient sterilization coverage range, and use the proportional integral derivative (PID) control algorithm to generate the uniform distribution of the sterilant flow control plan to ensure that the coverage rate finally reaches more than 98%.

[0044] S1010. Run the docking system model through the uniform distribution of the sterilant flow control plan, collect the pressure distribution data on the surface and inside of the instrument, and if the pressure exceeds the pressure control threshold, reduce the upper limit of the flow velocity control range to generate the final combination of flow parameters.

[0045] Run the docking system model through a sterilant flow control scheme with uniform distribution, and collect the pressure distribution data on the surface and inner cavity of the instrument. According to the collected pressure distribution data, determine whether the pressure exceeds the pressure control threshold. If the pressure exceeds the pressure control threshold, reduce the upper limit of the flow rate control range and generate adjusted flow rate control range data. Based on the adjusted flow rate control range data, obtain the sterilant distribution data and calculate the distribution uniformity value. According to the distribution uniformity value, determine whether the uniformity evaluation result is lower than the preset threshold. If the uniformity evaluation result is lower than the preset threshold, obtain the flow rate control range and the pressure control threshold, input the gradient descent algorithm, and determine the dynamic adjustment parameter configuration. Adjust the flow rate control range through the dynamic adjustment parameter configuration and generate updated flow rate distribution data. According to the updated flow rate distribution data, extract the sterilant distribution characteristics and determine whether the uniformity is higher than the preset threshold. If the uniformity is still lower than the preset threshold, repeat the execution of the gradient descent algorithm to obtain a new dynamic adjustment parameter configuration.

[0046] Specifically, run the docking system model through a sterilant flow control scheme with uniform distribution, and use pressure sensors to collect the pressure distribution data on the surface and inner cavity of the instrument. For example, set 10 pressure monitoring points on the surface of the instrument to obtain a data set with a pressure value range of 50 kPa to 150 kPa. According to the collected pressure distribution data, determine whether the pressure at each monitoring point exceeds the preset pressure control threshold of 120 kPa. If the pressure value at a certain monitoring point reaches 130 kPa, reduce the upper limit of the flow rate control range, adjust the flow rate upper limit from 10 m / s to 8 m / s, and generate adjusted flow rate control range data. Based on the adjusted flow rate control range data, use a flow meter to obtain the sterilant distribution data, and calculate the distribution uniformity value by the analysis of variance method. For example, calculate that the uniformity variance is 0.15. According to the distribution uniformity value, determine whether the uniformity evaluation result is lower than the preset threshold of 0.10. If the uniformity evaluation result is 0.15, obtain the current flow rate control range of 8 m / s and the pressure control threshold of 120 kPa, input the gradient descent algorithm, set the learning rate to 0.01, and iterate 100 times to determine the dynamic adjustment parameter configuration as a flow rate control range of 7.5 m / s and a pressure control threshold of 115 kPa. Adjust the flow rate control range through the dynamic adjustment parameter configuration and generate updated flow rate distribution data. For example, the flow rate value is stable between 7.5 m / s and 7.8 m / s. According to the updated flow rate distribution data, extract the sterilant distribution characteristics, calculate the uniformity variance to be 0.08, and determine whether it is higher than the preset threshold of 0.10. If the uniformity variance is 0.12, repeat the execution of the gradient descent algorithm, set the learning rate to 0.005, and iterate 150 times to obtain a new dynamic adjustment parameter configuration as a flow rate control range of 7.2 m / s and a pressure control threshold of 110 kPa.

[0047] S1011. According to the final combination of flow parameters, combined with the pore channel bifurcation structure and the long and narrow characteristics, adjust the dynamic control range of the injection rate to generate a sterilization operation parameter configuration that ensures full pore channel coverage.

[0048] According to the final combination of flow parameters, combined with the pore channel bifurcation structure and the long and narrow characteristic data, calculate the initial value of the dynamic control range of the injection rate to obtain the initial rate range. Through the preset reference value of the flow parameters, fuse the pore channel width and the inlet attribute data, analyze the flow distribution characteristics, and determine the distribution adaptation basis. If the initial value of the injection rate exceeds the preset threshold, then adjust the rate range in combination with the flow distribution characteristics to judge the optimized injection rate value. According to the medium viscosity and the flow distribution characteristics, extract the viscosity adjustment range and determine the adapted viscosity value. Use the optimized injection rate value and the adapted viscosity value to generate a preliminary parameter set and obtain the initial draft of the sterilization parameters. Through the support vector machine algorithm, combined with the preliminary parameter set and the distribution adaptation basis, optimize the parameter set to obtain the final sterilization parameters. According to the final sterilization parameters, verify the consistency between the flow distribution and the initial configuration data to judge the adaptation result. Through the docking system model with enhanced adaptability, fuse the long and narrow characteristics of the lumen and the bifurcation structure data, calculate the sterilant flow rate control range and the pressure control threshold, and determine the initial flow parameter configuration. Use the flow control algorithm to adjust the initial flow parameter configuration to obtain the final flow parameter set.

[0049] Specifically, according to the final flow parameter combination, combining the pore channel bifurcation structure and the narrow and long characteristic data, the numerical simulation method is used to calculate the initial value of the dynamic control range of the injection rate. For example, under the conditions of a bifurcation angle of 45 degrees and an aspect ratio of 10:1, the initial rate range is obtained as 0.5 - 2.0 mL / min. Through the preset reference value of the flow parameters, integrating the pore channel width of 0.5 mm and the inlet attribute data, the finite element analysis tool is used to analyze the flow distribution characteristics, and the laminar flow state is determined as the basis for distribution adaptation. If the initial injection rate value of 2.5 mL / min exceeds the preset threshold of 2.0 mL / min, the rate range is adjusted to 1.8 - 2.0 mL / min in combination with the flow distribution characteristics, and the optimized injection rate value is judged to be 1.9 mL / min. According to the medium viscosity of 0.01 Pa·s and the flow distribution characteristics, the viscometer is used to measure and extract the viscosity adjustment range as 0.008 - 0.012 Pa·s, and the adapted viscosity value is determined as 0.01 Pa·s. Using the optimized injection rate value of 1.9 mL / min and the adapted viscosity value of 0.01 Pa·s, a preliminary parameter set is generated, and the initial draft of the sterilization parameters is obtained. Through the support vector machine algorithm, combining the preliminary parameter set and the distribution adaptation basis, the parameter set is optimized, and the final sterilization parameters are obtained as a flow rate of 1.92 mL / min and a pressure of 120 Pa. According to the final sterilization parameters, the consistency between the flow distribution and the initial configuration data is verified, and the adaptation result is judged to be a match. Through the docking system model with enhanced adaptability, integrating the narrow and long characteristics of the lumen and the bifurcation structure data, the sterilant flow rate control range is calculated as 1.8 - 2.0 mL / min and the pressure control threshold is 110 - 130 Pa, and the initial flow parameter configuration is determined. Using the flow control algorithm, the initial flow parameter configuration is adjusted to obtain the final flow parameter set as a flow rate of 1.92 mL / min and a pressure of 120 Pa.

[0050] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A disinfection method for a medical lumen instrument, characterized in that, Including: Obtain a three-dimensional digital model of the lumen instrument through a scanning device, extract the inner lumen data, and generate standardized structure parameters; Construct a docking interface template library according to the standardized structure parameters, and generate an initial interface model; Load the initial interface model and the inner lumen data through fluid mechanics simulation software, simulate the flow distribution of the sterilizing agent, and determine the positions of flow blockage and leakage; Adjust the geometric parameters of the initial interface model according to the positions of flow blockage and leakage, and generate a docking system model with enhanced adaptability; Calculate the flow rate and pressure parameters of the sterilizing agent according to the docking system model with enhanced adaptability and the inner lumen data, and generate a flow parameter configuration; Adjust the injection rate and medium viscosity according to the flow parameter configuration and the inner lumen attributes, and generate a sterilization parameter set; Drive the docking system model to perform a sterilization operation through the sterilization parameter set, collect coverage distribution data, and generate an optimized control scheme.

2. The disinfection method of the medical lumen instrument according to claim 1, wherein, Obtain a three-dimensional digital model of the lumen instrument through a scanning device, extract the inner lumen data, and generate standardized structure parameters, including: Collect lumen instrument data through a scanning device to generate a three-dimensional digital model; Adopt a stereoscopic geometry algorithm to process the three-dimensional digital model, and extract the inner lumen elongation and morphological data; If the elongation degree of the inner lumen exceeds a preset threshold, adjust the morphological data through a surface fitting technique to obtain optimized geometric features; Generate a pore description according to the optimized geometric features, adopt a mesh generation technique to refine the structure, and determine the spatial distribution; Match the pore description through a standardized parameter template to obtain standardized structure parameters; Adopt a principal component analysis algorithm to identify the differences between the standardized structure parameters and the three-dimensional digital model, determine the feature adjustment direction, and update the model.

3. The disinfection method of the medical lumen instrument according to claim 1, wherein, Construct a docking interface template library according to the standardized structure parameters, and generate an initial interface model, including: Obtain standardized data through the standardized structure parameters and determine the template library basis; Extract preset rules from the template library to judge the morphological characteristic classification; Adopt a classification matching method for the morphological characteristics to generate an initial model framework; Adjust the docking parameters according to the initial model framework and the standardized structure parameters to obtain a docking optimized model; Analyze the interface features through the docking optimized model to determine the final interface structure; Extract the docking accuracy information from the final interface structure and judge whether it meets the requirements, and adjust the classification matching parameters according to the judgment result to generate an optimized initial interface model.

4. The disinfection method of the medical lumen instrument according to claim 1, wherein, Load the initial interface model and the inner lumen data through fluid mechanics simulation software, simulate the flow distribution of the sterilizing agent, and determine the positions of flow blockage and leakage, including: Load the initial interface model and the inner lumen elongation data through fluid mechanics simulation software to obtain preliminary flow distribution data of the sterilizing agent; Adopt a simulation analysis technique to process the preliminary flow distribution data to determine the changing trend of the flow distribution; If the changing trend shows an abnormal decrease in the flow velocity, judge the distribution of the flow blockage positions through a fluid mechanics algorithm; Obtain the blockage influence range according to the flow blockage position distribution data; Determine the external leakage position distribution data by comparing the blockage influence range with the external leakage hypothesis, and integrate it into the final distribution result.

5. The disinfection method of the medical lumen instrument according to claim 1, wherein, Adjust the geometric parameters of the initial interface model according to the flow blockage and leakage positions to generate a docking system model with enhanced adaptability, including: Extract the spatial distribution characteristics of the blocked area through the flow blockage and leakage positions; Use an iterative optimization algorithm to adjust the geometric parameters of the initial interface model for the blocked area to determine the optimized interface geometric shape; Update the initial interface model according to the optimized interface geometric shape to generate an interface model with enhanced adaptability; Generate a preliminary docking system model through the interface model with enhanced adaptability and determine whether the adaptability meets the preset threshold; If the preset threshold is not met, adjust the geometric parameters again through the iterative optimization algorithm to obtain an updated docking system model and verify the optimized distribution.

6. The disinfection method of the medical lumen instrument according to claim 1, characterized in that, Calculate the sterilant flow rate and pressure parameters according to the docking system model with enhanced adaptability and the lumen data to generate a flow parameter configuration, including: Fuse the data of the long and narrow and bifurcated lumen structures through the docking system model with enhanced adaptability to determine the pore characteristic distribution; Calculate the dynamic change of the sterilant according to the pore characteristic distribution to obtain the initial value of the flow rate range and the initial value of the pressure threshold; Use data fusion technology to generate basic control parameters by combining the initial value of the flow rate range and the initial value of the pressure threshold; Adjust the basic control parameters through a flow control algorithm to determine the optimized value of the flow rate range; If the optimized value of the flow rate range exceeds the preset threshold, correct the pressure threshold through model calculation, generate an optimized value of the pressure control and form a complete flow parameter configuration.

7. The disinfection method of the medical lumen instrument according to claim 1, wherein, Adjust the injection rate and medium viscosity according to the flow parameter configuration and the lumen attributes to generate a sterilization parameter set, including: Obtain the initial configuration data through the flow parameter configuration and determine the reference value of the flow parameters; Calculate the flow distribution characteristics according to the lumen inlet width attribute; If the injection rate exceeds the preset threshold, adjust the rate value according to the flow distribution characteristics to determine the optimized injection rate; Extract the viscosity adjustment range from the medium viscosity and the flow distribution characteristics to determine the adapted viscosity value; Generate a preliminary parameter set by using the optimized injection rate and the adapted viscosity value; Optimize the preliminary parameter set through a support vector machine algorithm to generate the final sterilization parameters and verify the consistency with the initial configuration.

8. The disinfection method of the medical lumen instrument according to claim 1, characterized in that, Drive the docking system model to perform a sterilization operation through the sterilization parameter set, collect coverage distribution data and generate an optimized control plan, including: Drive the docking system model to inject the sterilant through the sterilization parameter set to generate an initial operation instruction; Obtain the sterilant injection distribution characteristics from the docking system model to determine the real-time adjustment parameters; Use a real-time monitoring device to record the change trend of the coverage distribution and obtain the sterilization state data in the pores; If abnormal fluctuations in the coverage distribution are detected, generate corrected distribution data by adjusting the sterilant injection amount; Extract the boundary characteristics of the defective area from the corrected distribution data, judge the range of insufficient sterilization coverage and generate defective area data; Determine supplementary injection parameters according to the defective area data and perform secondary injection.

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