Manufacturing method and system for customizable medical dressing

Abstract

The invention discloses a manufacturing method and system of a customizable medical dressing, and belongs to the technical field of medical instrument manufacturing. In order to solve the problems that an existing medical dressing is difficult to fit a complex body surface area and the customization efficiency is low, the technical means of rapid three-dimensional scanning, flexible demand modeling, paper folding pattern generation, laser engraving processing and the like are mainly adopted, automatic identification of different body surface contours, flexible scoring and dynamic generation of structural patterns are achieved, and the customization efficiency is high. And precise engraving and cutting of the dressing are completed through an automatically-controlled laser system. According to the method, rapid personalized customization of the medical dressing can be realized, and the fitting degree and the covering precision are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical device manufacturing, and in particular relates to a preparation method and system of a customizable medical dressing. Background Art

[0002] Existing medical dressings are usually standard products that are difficult to fully fit the human body surface, especially in areas with complex contours such as joints and face. This results in the dressing often failing to achieve uniform contact with wounds of different shapes in actual use, and is prone to problems such as falling off and poor sealing. Although a variety of prefabricated dressing products have appeared on the market, it is still difficult to meet the high degree of customization caused by individual differences, especially when it comes to precise coverage of wounds in special areas.

[0003] There are three main traditional coping methods: First, use multiple small-sized dressings to cover complex parts, but this method is cumbersome and has poor sealing; second, use elastic materials to prepare dressings, and use the elasticity of the material itself to adapt to the curvature of the body surface, but the adaptability range is limited; third, manually cut the shape of the dressing to adapt to individual differences, but this method has low production accuracy and is time-consuming. In summary, the existing technologies have failed to effectively solve the key problems of medical dressings in personalization and rapid customization, and there are still obvious deficiencies in coverage accuracy, fitting performance, operation efficiency and processing automation. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for making a customizable medical dressing based on precision machining and origami structure design, so as to solve the problems of low fit of existing medical dressings in different body surface areas, manual dependence on design, low machining efficiency, and poor sports adaptability.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] A method for making a customizable medical dressing comprises the following steps:

[0007] 1) Perform a three-dimensional scan on the target surface to obtain three-dimensional scanning data, including the shape, size, structure, color, etc. of the target area;

[0008] 2) Based on the acquired 3D scanning data, the local geometric features and physiological dynamics of the target area are extracted and modeled, and the shape and crease pattern adapted to the target area are generated according to the modeling;

[0009] 3) Convert the shape and crease pattern into executable files or codes, and use precision processing technology (such as laser engraving technology) to make medical dressings.

[0010] Further, the specific steps of obtaining the three-dimensional scan data in step 1) include: scanning and collecting the original point cloud data and the corresponding optical texture information, generating a complete three-dimensional surface model through filtering, registration, and reconstruction, dividing the surface model into several sub-regions based on a segmentation algorithm, and calculating the local geometric features and dynamic physiological features of each sub-region as the three-dimensional scan data to be used.

[0011] Further, the steps of extracting the local geometric features and physiological dynamics of the target region and modeling them in step 2) include: extracting the curvature features of the geometric shape of the target region, the surface tension distribution features, and the deformation features caused by predicted motion, and establishing a spatial model describing the local flexibility requirements.

[0012] Further, the curvature features of the geometric shape of the target region, the surface tension distribution features, and the deformation features caused by predicted motion are the curvature tensor, the tension tensor, and the motility tensor; the method of establishing a spatial model describing the local flexibility requirements is: performing weighted fusion on the curvature tensor, the tension tensor, and the motility tensor to obtain a dynamic flexibility tensor field as the spatial model describing the local flexibility requirements.

[0013] Further, the steps of calculating the curvature tensor include:

[0014] Obtaining the three-dimensional scan data of the target body surface region, including the spatial coordinates of each surface point;

[0015] For each grid point or patch, calculating its local normal vector to form a continuous normal vector field;

[0016] At each sampling point, selecting its neighboring points and fitting a local quadratic surface by means of the least squares method, etc.;

[0017] Taking the derivative of the local fitted surface and calculating the principal curvature and its corresponding direction;

[0018] Encoding the principal curvature values and their directions into a two-dimensional symmetric matrix, i.e., the curvature tensor, to describe the bending characteristics of the local surface at that point.

[0019] Further, the steps of calculating the tension tensor include:

[0020] Based on the three-dimensional scan data and the stretching direction of the surrounding tissues, identifying the potential deformation or tension directions on the body surface;

[0021] Referring to the topological relationship between the target region and the neighboring regions and inferring the potential force paths in combination with the physiological structure;

[0022] Defining a vector representing the surface tension direction and magnitude at each point and establishing a stretching vector field according to the local surface stretching and shrinking trends;

[0023] Convert the stretching vector field into a two-dimensional tension tensor matrix to represent the tension distribution and coupling strength in each direction of the local surface.

[0024] Further, the steps of calculating the motility tensor include:

[0025] Calibrate the high-motion regions according to the human anatomical structure, or estimate the activity intensity of different regions by combining historical scans or clinical data to identify the high-frequency motion regions;

[0026] Evaluate the displacement range and frequency of the target region under typical actions, and count the motion amplitude and frequency of the region;

[0027] Establish vectors representing the local motion trend at each sampling point, including motion direction, amplitude, and frequency information, and construct a motion vector field;

[0028] Encode the motion vector field into a two-dimensional motility tensor matrix to describe the motion trend and change intensity of the point in different directions.

[0029] Further, the steps of generating the shape and crease pattern adapted to the target region according to the modeling in step 2) include: Analyze the spatial distribution of the flexibility requirements in the target region based on the established spatial model, perform flexible region division to distinguish regions with different flexibility levels; Based on the flexible region division results, model and regulate the structural form and local crease pattern to generate the shape and crease pattern adapted to the target region.

[0030] Further, the method of performing flexible region division is: Divide the region into high-flexibility region, medium-flexibility region, and low-flexibility region according to two set thresholds of size.

[0031] Further, the methods of modeling and regulating the structural form and local crease pattern include: Construct an optimization objective function for flexible matching and stress control according to the divided flexible regions and the dynamic flexibility tensor field, and generate appropriate shapes and crease patterns by iteratively optimizing the crease parameters; This optimization objective function is jointly composed of the flexible matching error and the stress distribution, and is specifically expressed as:

[0032]

[0033] Among them, is the flexible matching error, is the ideal flexibility tensor field, w1, w2 are weights, Stress is the stress distribution function, ρ is the crease density, d is the crease depth, Δv is the crease offset, and Ω is the optimization region.

[0034] Further, Stress is calculated by finite element analysis:

[0035] Stress = ∫ Ω E:∈dΩ

[0036] Among them, E is the material elastic tensor, ∈ is the strain tensor, and Ω is the optimization region.

[0037] Furthermore, the crease parameters are iteratively updated based on the gradient descent method, and finally, high-density interleaved, gradient, or simple-structured crease patterns are generated according to different flexible regions.

[0038] A production system for a customizable medical dressing, used to implement the above production method, includes:

[0039] A three-dimensional scanning module, used to perform three-dimensional scanning on the target body surface to obtain three-dimensional scanning data, including the shape, size, structure, color, etc. of the target region;

[0040] A pattern generation and processing module, used to extract the local geometric features and physiological dynamics of the target region based on the obtained three-dimensional scanning data and perform modeling, and generate a shape and crease pattern adapted to the target region according to the modeling;

[0041] A precision machining system, used to produce a medical dressing according to the generated shape and crease pattern by using precision machining techniques (such as laser engraving techniques);

[0042] A material conveying system, used for the storage, disinfection, and conveyance of medical dressing materials, and to complete the cutting of finished products;

[0043] A control system, used to coordinate the operations among the three-dimensional scanning module, the pattern generation and processing module, the precision machining system, and the material conveying system.

[0044] The beneficial effects achieved by the present invention are as follows:

[0045] 1. The present invention realizes the rapid personalized customization of medical dressings. The whole process from three-dimensional scanning, pattern generation to laser processing and output of finished products is completed within 40 - 60 seconds or less, significantly improving the customization efficiency and being applicable to clinical rapid application scenarios.

[0046] 2. The present invention automatically constructs a dressing shape and crease pattern that highly matches the body surface contour through three-dimensional curved surface scanning, a flexible scoring function, and a dynamic crease generation mechanism, avoiding manual intervention and improving the automation and accuracy of structural design.

[0047] 3. The present invention introduces a dynamic flexible tensor field model, realizes flexible demand modeling by comprehensively considering the curvature, tension, and motility characteristics of the target body surface, and combines topology optimization technology to generate a non-uniform and continuously transitional crease structure pattern, improving the local compliance and overall mechanical response consistency of the dressing.

[0048] 4. The processing system of the present invention has functions of laser power control, multi-channel scanning, air flow assistance and visual positioning, can adapt to different types of medical materials, ensure multi-level processing depth and high-precision positioning of shapes and crease patterns, and improve the flexibility and stability of shape and pattern processing.

[0049] 5. The dressing obtained by the processing of the present invention has strong adhesion and good movement adaptability, can effectively cover complex or active areas (such as joints, facial contours), reduce the phenomena of peeling, displacement or wrinkling of the dressing caused by movements, and improve the wound protection effect and use stability.

[0050] 6. The present invention reduces the operation complexity of medical staff and material waste, simplifies the design and production process of personalized dressings, and improves clinical operation efficiency.

[0051] 7. The present invention improves the coverage, adhesion and wearing comfort of the dressing, helps to reduce the risks of wound leakage, contamination and cross-infection, and enhances the patient's treatment compliance and rehabilitation experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flowchart of a method for manufacturing a customizable medical dressing provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] In order to make the technical features and advantages or technical effects in the above technical solutions of the present invention more obvious and understandable, the following will be described in detail through embodiments and in conjunction with the drawings.

[0054] I. A Method for Manufacturing a Customizable Medical Dressing

[0055] The embodiment of the present invention provides a method for manufacturing a customizable medical dressing, and its process is as Figure 1 shown, and specifically includes the following steps:

[0056] Step 1: Collect and process three-dimensional data of the body surface

[0057] Perform three-dimensional scanning on the target body surface to collect original point cloud data and corresponding optical texture information; filter, register and reconstruct the scanned data to generate a complete three-dimensional surface model; use a segmentation algorithm to divide the surface model into several sub-regions Ri; calculate and record the local geometric features (such as curvature) and dynamic physiological features (such as surface tension and movement frequency) of each sub-region as the basis for subsequent flexible modeling.

[0058] Step 2: Generate shapes and crease patterns adapted to the target area

[0059] Based on the obtained three-dimensional scanning data, extract the local geometric features and physiological dynamics of the target area and perform modeling, and generate shapes and crease patterns adapted to the target area according to the modeling.

[0060] As a preferred embodiment, a spatial model describing local flexibility requirements can be established by extracting the curvature features of the geometric shape of the target area, the surface tension distribution features, and the deformation features caused by predicted motion. Analyze the spatial distribution of flexibility requirements in the target area according to the established spatial model, and perform flexible area division to distinguish different flexible level areas; based on the flexible area division results, model and regulate the structural form and local crease patterns to generate the shape and crease patterns adapted to the target area.

[0061] As an even more preferred embodiment, the curvature features of the geometric shape of the target area, the surface tension distribution features, and the deformation features caused by predicted motion can specifically be the curvature tensor, the tension tensor, and the motility tensor; by performing weighted fusion on the curvature tensor, the tension tensor, and the motility tensor, a dynamic flexibility tensor field is obtained. Then, according to the divided flexible areas and the dynamic flexibility tensor field, an optimization objective function for flexible matching and stress control is constructed, and the crease parameters are iteratively optimized to generate appropriate shapes and crease patterns. The specific calculation steps are as follows:

[0062] Step 2-1: Calculate the dynamic flexibility tensor field

[0063] Calculate the curvature tensor, the tension tensor, and the motility tensor based on the data obtained in step one, and finally calculate the dynamic flexibility tensor field. The specific steps are as follows

[0064] 1. Calculate the curvature tensor K(x,y,z):

[0065] The steps for calculating the curvature tensor include: obtaining the three-dimensional scan data of the target body surface area, including the spatial coordinates of each surface point; for each grid point or patch, calculating its local normal vector to form a continuous normal vector field; at each sampling point, selecting its neighboring points and fitting a local quadratic surface by means of the least squares method, etc.; differentiating the local fitted surface to calculate the principal curvatures and their corresponding directions; encoding the principal curvature values and their directions as a two-dimensional symmetric matrix, that is, the curvature tensor, which is used to describe the bending characteristics of the local surface at this point. The specific calculation process is as follows:

[0066] Assume that the surface is represented by the parametric equation r(u,v), and calculate the Gaussian curvature K according to the first fundamental form (E,F,G) and the second fundamental form (L,M,N) G and the mean curvature K M :

[0067]

[0068] Among them, E, F, and G are the coefficients of the first fundamental form, which are used to calculate the intrinsic metric of the surface; L, M, and N are the coefficients of the second fundamental form, which are used to characterize the curvature.

[0069] Construct the curvature tensor to describe local geometric flexibility:

[0070]

[0071] where \(I\) is the identity tensor, an identity tensor of dimension \(3\times3\); \(n\) is the unit normal vector representing the normal direction of the point; is the outer product tensor, representing the projection of the tensor in the normal direction.

[0072] 2. Calculate the tension tensor \(T(x,y,z)\): s (x,y,z):

[0073] The steps to calculate the tension tensor include: based on the three-dimensional scan data and the stretching direction of the surrounding tissue, identify the potential deformation or tension direction on the body surface; refer to the topological relationship between the target area and the adjacent area, and infer the potential force path in combination with the physiological structure; define a vector representing the surface tension direction and magnitude at each point, and establish a stretching vector field according to the local surface stretching and contraction trends; convert this stretching vector field into a two-dimensional tension tensor matrix, representing the tension distribution and coupling strength in each direction of the local surface. The specific calculation process is as follows:

[0074] Use the divergence of the normal vector field to approximately represent the surface tension:

[0075]

[0076] where \(w\) i is the distance weight, is the normal vector of the neighboring point, is the divergence of the normal vector field.

[0077] Construct the tension tensor to represent the surface tension state within the region:

[0078]

[0079] where \(P\) is the principal stress direction tensor obtained by the principal value decomposition of the stress field; is the prestress component, used to simulate the initial tension state of the tissue at rest; \(\sigma_0\) is the prestress constant, representing the magnitude of the static initial tension of the tissue.

[0080] 3. Calculate the motility tensor \(M(x,y,z)\):

[0081] The steps for calculating the motility tensor include: calibrating high-motion regions according to human anatomical structures, or estimating the activity intensities of different regions by combining historical scans or clinical data to identify high-frequency motion regions; evaluating the displacement range and frequency of the target region under typical actions, and statistically analyzing the motion amplitude and frequency of the region; establishing vectors representing local motion trends at each sampling point, including motion direction, amplitude, and frequency information, to construct a motion vector field; encoding the motion vector field into a two-dimensional motility tensor matrix to describe the motion trends and change intensities of the point in different directions. The specific calculation process is as follows:

[0082] According to historical scan data or motion capture data, calculate the local principal motion directions v1, v2, where v1, v2 are the direction components of the local motion frequency, unit vectors and orthogonal; and calculate the principal motion amplitudes as m1, m2, respectively representing the motion intensities in the v1, v2 directions. Among them, the fitting formulas for m1, m2 are, for example:

[0083]

[0084] Among them, Δr i is the displacement component within time Δt.

[0085] Construct a motion tensor to represent the regional motion frequency:

[0086]

[0087] 4. Calculate the Dynamic Flexibility Tensor Field (DFTF):

[0088] Calculate the Dynamic Flexibility Tensor Field based on the curvature tensor, tension tensor, and motility tensor calculated above to comprehensively describe the local flexibility requirements:

[0089] F flex (x, y, z) = ω1·K(x, y, z) + ω2·T s (x, y, z) + ω3·M(x, y, z)

[0090] Among them, ω1, ω2, ω3 are weight coefficients that can be adjusted according to experience.

[0091] Step 2-2: Analyze the flexibility changes and perform zoning

[0092] 1. Calculate the gradient of the Dynamic Flexibility Tensor Field indicating the change rate of the flexibility requirements:

[0093]

[0094] Among them, the high-gradient regions (such as joint edges) are divided into "high-flexibility zones", indicating that the flexibility requirements change drastically and a high-density, staggered crease structure is required. The low-gradient regions (such as flat skin) are divided into "low-flexibility zones", indicating that the flexibility requirements are stable and a low-density, simple crease structure is suitable.

[0095] 2. Calculate the gradient magnitude And classify regions according to two thresholds τ1 and τ2:

[0096] a. High-flexibility zone Requires a complex staggered crease structure.

[0097] b. Medium-flexibility zone Requires a gradient crease structure.

[0098] c. Low-flexibility zone Only requires simple structure support.

[0099] Step 2-3: Generate the shape and crease pattern based on topology optimization

[0100] Using the topology optimization algorithm, according to the dynamic flexibility tensor field F flex and the gradient Adaptively generate the shape and crease structure pattern, and the specific steps are as follows:

[0101] 1. Initialize the structural unit:

[0102] Select the basic structural unit (such as hexagon, wavy crease) from the pattern library, and set the initial crease parameters, including the crease density ρ (the number of creases per unit area), the crease depth d (the recess degree corresponding to crease processing), and the crease offset Δv (the offset of the crease center line, used to form an asymmetric structure).

[0103] 2. Construct the optimization objective function:

[0104] The optimization objective function J is an index function for measuring flexibility matching and structural stress:

[0105]

[0106] Among them, is the flexibility matching error, is the ideal flexibility tensor field; w1, w2 are weights; Stress is the stress distribution function, which can be calculated by finite element analysis:

[0107]

[0108] Among them, E is the material elastic tensor, ∈ is the strain tensor, and Ω is the optimization region.

[0109] 3. Perform iterative optimization:

[0110] Using the gradient descent method, iteratively adjust the crease parameters (ρ, d, Δv): the density ρ is positively correlated with ||F flex ||, and the offset Δv is adjusted dynamically along the gradient direction while the depth d is gradually corrected according to the local stress.

[0111] 4. Generate the crease detail structure:

[0112] In the highly flexible region, introduce an asymmetric staggered offset along the principal direction of the tensor, in the form of:

[0113]

[0114] where k is the offset adjustment coefficient, which can be adjusted according to the material properties or actual test results. For example, a larger k value is used for soft materials.

[0115] In the moderately flexible region, use the fractal interpolation technique to generate a gradient unit to ensure the structural continuity.

[0116] Step 3: Output the processing instructions and drive the processing equipment

[0117] 1. Convert the optimized generated shape and crease pattern into CAD format (such as DXF) or processing control code (such as G-code);

[0118] 2. Attach processing parameters to each crease path: depth, width, crease angle, etc.;

[0119] 3. Input the path instructions into the precision processing system, and cooperate with the material conveying module to complete the precise processing of the dressing pattern;

[0120] 4. After the processing is completed, automatically cut to form a flexible dressing that can fit the individual body surface contour.

[0121] II. A production system for customizable medical dressings

[0122] To implement the above method, an embodiment of the present invention further provides a production system for customizable medical dressings, including:

[0123] 1. A three-dimensional scanning module, used to execute method step 1 to obtain the three-dimensional scanning data of the target body surface area, including the shape, size, structure, color, etc. of the target area. As a preferred implementation method, this three-dimensional scanning module includes a structured light three-dimensional scanner or similar device, which can perform non-contact scanning on the target body surface (such as wounds, joints, bone prominences, etc.) to collect the original point cloud data and optical texture information. The scanning time of this module does not exceed 10 seconds.

[0124] 2. A pattern generation processing module, which is used to execute method step 2, extract local geometric features and physiological dynamics of the target area based on three-dimensional scan data and perform modeling, and generate a shape and crease pattern adapted to the target area according to the modeling. As a preferred implementation, this pattern generation processing module can complete flexible tensor field modeling, flexible area partitioning, and crease pattern generation. This module integrates calculation algorithms for curvature tensors, tension tensors, and motility tensors, supports tensor weighted fusion, gradient analysis, area partitioning, and topology optimization processes, and can generate personalized shapes and crease patterns within 10 seconds.

[0125] 3. A precision machining system, which is used to execute method step 3 to achieve precise machining of the dressing shape and pattern. As an alternative implementation, the precision machining system can include the following components:

[0126] Laser component: A CO2 laser, fiber laser, or laser diode can be selected, with a power range of 3W to 50W, a spot diameter not greater than 0.5mm, a positioning accuracy not lower than ±0.01mm, and a machining accuracy not lower than ±0.05mm;

[0127] Variable power control module: Supports engraving at different depths in the same pattern;

[0128] Multi-channel scanning control module: Achieves precise control of the machining depth through multiple low-power scans;

[0129] Airflow assistance system: Prevents thermal damage and clears residues in a timely manner;

[0130] Vision positioning system: Ensures accurate alignment of the pattern with the material positioning.

[0131] The laser parameters can be automatically adjusted according to the physical properties of different medical dressing materials (such as silicone film, polyurethane film, hydrocolloid, etc.).

[0132] 4. A material conveying system, which is used for the storage, disinfection, and conveyance of medical dressing materials and completes the cutting of finished products. As an alternative implementation, this system includes:

[0133] Material storage compartment: Used to store unprocessed dressing materials and maintain a sterile state;

[0134] Ultraviolet disinfection unit: Performs real-time sterilization treatment on the material surface;

[0135] Precision conveying mechanism: Ensures smooth conveyance of the material to the machining area;

[0136] Tension control module: Ensures the flatness of the material during machining;

[0137] Automatic cutting device: Precisely separates the processed dressing from the raw material.

[0138] 5. A control system is used to overall coordinate the operation of the above modules. The control system adopts an embedded computing platform and has the following functions:

[0139] Device initialization and calibration management;

[0140] Receiving, parsing and modeling of 3D scan data;

[0141] Execution of pattern generation and crease optimization algorithms;

[0142] Automatic configuration of laser parameters and control of processing instructions;

[0143] Synchronous management of material transportation and cutting process;

[0144] User interface interaction, parameter setting and support for remote data processing.

[0145] This control system supports network connection and can perform data backup, dressing usage record management and subsequent statistical analysis.

[0146] The operation process of this system is as follows:

[0147] 1. The operator starts the control system to complete system self-check and initialization;

[0148] 2. The operator aligns the 3D scan module with the target body surface area for scanning to collect 3D data (about 10 seconds or less);

[0149] 3. The pattern generation processing module completes flexible modeling, area partitioning and crease pattern generation based on the scan data (about 10 seconds or less);

[0150] 4. The control system automatically configures precision processing parameters (such as laser processing parameters) according to the dressing material type;

[0151] 5. The precision processing system completes the precise engraving and cutting of the crease pattern on the material (about 30 seconds or less);

[0152] 6. The material transportation system outputs the customized dressing after processing.

[0153] The entire process from scanning to the output of the finished dressing takes about 40 - 60 seconds, which is suitable for clinical rapid response scenarios.

[0154] III. Application Examples:

[0155] Example 1: Personalized customization of wound dressings for the hand joint area

[0156] For the hand joint area, the system collects the complex curved surface data of this part through a three-dimensional scanning module, and the pattern generation and processing module generates a matching dressing shape and crease pattern based on the scanning results. In the said pattern, the areas with frequent joint activities are designed with a high-density, staggered crease structure to enhance the local flexibility and ductility of the dressing; the relatively stable peripheral areas adopt a low-density, simplified crease structure to improve the overall fitting degree and processing efficiency. After laser engraving by the precision processing system, the obtained customized dressing can dynamically adapt to the flexion and extension of the joint, effectively avoiding edge warping or local peeling during movement.

[0157] Example 2: Personalized customization of dressings for complex facial contour areas

[0158] For areas on the face with complex micro-curvature features (such as the nose wings, around the eyes, etc.), the system uses a high-resolution scanning mode to obtain the facial surface detail information. The pattern generation and processing module combines tensor modeling and gradient analysis to automatically generate a dressing shape and crease pattern that conforms to the facial contour, ensuring that a high-density crease structure is configured in areas with drastic changes in flexibility requirements, and an appropriate low-density structure is configured in the contour transition areas. After laser engraving, the obtained dressing can precisely fit the facial surface, avoiding problems such as wrinkling, air pockets or detachment that occur in traditional dressings in such areas, and improving the wearing comfort and stability.

[0159] Although the present invention has been disclosed above by way of examples, it is not intended to limit the present invention. Any appropriate modifications or equivalent replacements made by those of ordinary skill in the art to the technical solutions of the present invention shall be covered within the protection scope of the present invention. The protection scope of the present invention shall be subject to that defined by the claims.

Claims

1. A manufacturing method of a customizable medical dressing, characterized in that, It includes the following steps: 1) Perform three-dimensional scanning on the target body surface to obtain three-dimensional scanning data, including the shape, size, structure, and color of the target area; 2) Based on the obtained three-dimensional scanning data, extract the local geometric features and physiological dynamics of the target area and perform modeling, and generate a shape and crease pattern adapted to the target area according to the modeling; 3) Make a medical dressing according to the shape and crease pattern.

2. The manufacturing method according to claim 1, characterized in that, The specific steps for obtaining three-dimensional scanning data in step 1) include: scanning and collecting the original point cloud data and the corresponding optical texture information, generating a complete three-dimensional surface model through filtering, registration, and reconstruction, dividing the surface model into several sub-regions based on a segmentation algorithm, and calculating the local geometric features and dynamic physiological features of each sub-region as the three-dimensional scanning data to be used.

3. The manufacturing method according to claim 1, characterized in that, The steps for extracting the local geometric features and physiological dynamics of the target area and performing modeling in step 2) include: extracting the curvature features of the geometric shape of the target area, the surface tension distribution features, and the deformation features caused by predicted movement, and establishing a spatial model for describing the local flexibility requirements.

4. The manufacturing method according to claim 1, wherein The curvature features of the geometric shape of the target area, the surface tension distribution features, and the deformation features caused by predicted movement are the curvature tensor, the tension tensor, and the motility tensor; the method for establishing a spatial model for describing the local flexibility requirements is: performing weighted fusion on the curvature tensor, the tension tensor, and the motility tensor to obtain a dynamic flexibility tensor field as the spatial model for describing the local flexibility requirements.

5. The manufacturing method according to claim 4, characterized in that, The steps for calculating the curvature tensor include: Obtain the three-dimensional scanning data of the target body surface area, including the spatial coordinates of each surface point; For each grid point or patch, calculate its local normal vector to form a continuous normal vector field; At each sampling point, select its neighboring points and fit a local quadratic surface by means of the least squares method, etc.; Derive the local fitted surface and calculate the principal curvature and its corresponding direction; Encode the principal curvature values and their directions as a two-dimensional symmetric matrix, that is, the curvature tensor, for describing the bending characteristics of the local surface at this point.

6. The manufacturing method according to claim 4, wherein The steps for calculating the tension tensor include: Based on the three-dimensional scanning data and the stretching direction of the surrounding tissues, identify the potential deformation or tension directions on the body surface; Refer to the topological relationship between the target area and the neighboring areas, and infer the potential force paths in combination with the physiological structure; Define a vector representing the surface tension direction and magnitude at each point, and establish a stretching vector field according to the local surface stretching and shrinking trends; Convert the stretching vector field into a two-dimensional tension tensor matrix, representing the tension distribution and coupling strength in each direction of the local surface.

7. The manufacturing method according to claim 4, characterized in that The steps for calculating the motility tensor include: Calibrate the high-motion areas according to the human anatomical structure, or estimate the activity intensity of different areas by combining historical scans or clinical data to identify the high-frequency motion areas; Evaluate the displacement range and frequency of the target area under typical actions, and statistically analyze the regional motion amplitude and frequency; Establish a vector representing the local motion trend at each sampling point, including motion direction, amplitude, and frequency information, and construct a motion vector field; Encode the motion vector field as a two-dimensional motility tensor matrix for describing the motion trend and change intensity of this point in different directions.

8. The manufacturing method according to claim 4, characterized in that, The steps of generating a shape and crease pattern adapted to the target area based on modeling include: analyzing the spatial distribution of the flexibility requirements in the target area according to the established spatial model, and dividing the flexible area into a high-flexibility area, a medium-flexibility area, and a low-flexibility area based on two set size thresholds; based on the flexible area division result, modeling and regulating the structural form and local crease pattern to generate a shape and crease pattern adapted to the target area.

9. The manufacturing method according to claim 8, wherein The method of modeling and regulating the structural form and local crease pattern includes: constructing an optimization objective function for flexible matching and stress control according to the divided flexible area and the dynamic flexible tensor field, and generating a suitable shape and crease pattern by iteratively optimizing the crease parameters; this optimization objective function is jointly composed of the flexible matching error and the stress distribution, and is specifically expressed as: Among them, is the flexible matching error, is the ideal flexible tensor field, w1 and w2 are weights, Stress is the stress distribution function, ρ is the crease density, d is the crease depth, Δv is the crease offset, and Ω is the optimization region.

10. The manufacturing method according to claim 9, characterized in that, Iteratively update the crease parameters based on the gradient descent method, and finally generate crease patterns with high-density staggered, gradient, or simple structures for different flexible areas respectively.

11. A manufacturing system for a customizable medical dressing, which is used to implement the manufacturing method described in any one of claims 1-10, characterized in that, Include: A three-dimensional scanning module for three-dimensionally scanning the target body surface to obtain three-dimensional scanning data, including the shape, size, structure, and color of the target area; A pattern generation and processing module for extracting the local geometric features and physiological dynamics of the target area based on the obtained three-dimensional scanning data and performing modeling, and generating a shape and crease pattern adapted to the target area according to the modeling; A precision machining system for manufacturing a medical dressing according to the generated shape and crease pattern; A material conveying system for storing, disinfecting, and conveying medical dressing materials, and completing the cutting of finished products; A control system for coordinating the operations among the three-dimensional scanning module, the pattern generation and processing module, the precision machining system, and the material conveying system.

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