3D stereoscopic stoma measuring and automatic cutting device for stoma base plate

Abstract

This invention discloses a 3D stoma measurement and automatic stoma chassis cutting device, belonging to the field of medical device technology. It aims to solve the technical problems of large errors, low cutting efficiency, and poor adaptability in traditional manual measurement, which fail to meet the precise home care needs of enterostomy patients. The device includes: a 3D scanning module for performing three-dimensional scanning of the stoma skin surface and transmitting the three-dimensional scan data to the measurement module; and a data processing unit integrating a deep learning-based machine learning algorithm framework, using a U-Net network as the core model to perform deep optimization processing on the three-dimensional scan data acquired by the 3D scanning module. This invention, through high-precision modeling and contour recognition of the stoma's three-dimensional morphology, completely solves the deficiency of traditional methods in obtaining three-dimensional data, effectively reducing the risk of skin complications caused by cutting size deviations, and compensating for the shortcomings of traditional manual measurement, which leads to leakage and dermatitis due to errors.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and more specifically, to a 3D stoma measurement and stoma base automatic cutting device. Background Technology

[0002] In the field of medical assistive devices, the measurement and cutting of stoma baseplates in home care for enterostomy patients faces significant technical bottlenecks. Traditional methods rely on manual calipers or templates, which can only complete two-dimensional planar data acquisition and cannot obtain three-dimensional morphological information of the stoma. Clinical surveys show that more than 65% of stomas have complex morphological features such as irregular protrusions and tilt angles, and traditional measuring tools cannot accurately capture these details. Manual cutting or semi-automatic cutting machines require manual input of parameters, and are affected by factors such as operator experience and hand stability, resulting in an average error of 1.2-2.5mm between the cut baseplate and the stoma edge, which reduces the baseplate's sealing performance. According to authoritative clinical statistics, approximately 23% of contact dermatitis is caused by improper cutting, which not only increases patient suffering but may also lead to serious complications such as peristomal skin ulceration.

[0003] From an efficiency perspective, traditional manual stoma cutting takes 3-5 minutes per procedure, and the time cost is even higher when adjusting the cutting for complex stoma shapes. While some existing measuring devices incorporate digital elements, they lack a three-dimensional measurement compensation mechanism, making it difficult to adapt to stoma shapes with different diameters (15-50mm) and base curvatures, and even more difficult to accommodate various base models such as round, elliptical, and irregular shapes. With the increasing demands for efficiency and accuracy in home care settings, existing technologies can no longer meet patients' urgent needs for automated and personalized care. Therefore, we propose a 3D stoma measurement and automatic stoma base cutting device. Summary of the Invention

[0004] The purpose of this invention is to provide a 3D stoma measurement and stoma base plate automatic cutting device to solve the technical problems of large errors in traditional manual measurement, low efficiency in base plate cutting, poor adaptability, and difficulty in meeting the needs of precise home care for enterostomy patients.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a 3D stereoscopic stoma measurement and stoma base automatic cutting device, comprising:

[0006] A 3D scanning module is used to perform a three-dimensional scan of the stoma skin surface and transmit the three-dimensional scan data of the stoma skin surface to the measurement module;

[0007] The data processing unit integrates a deep learning-based machine learning algorithm framework, using the U-Net network as the core model to perform deep optimization processing on the 3D scanning data acquired by the 3D scanning module.

[0008] The measurement module is used to perform stoma measurement based on the received three-dimensional scanning data, obtain stoma measurement data and trimming measurement data, and transmit the stoma measurement data and trimming measurement data to the stoma chassis model and the trimming module respectively;

[0009] The stoma chassis model is used to generate stoma chassis models of different sizes and styles based on the stoma measurement data output by the measurement module.

[0010] The trimming module is used to trim the stoma base according to the received trimming measurement data and the stoma base model to obtain the trimmed stoma base.

[0011] The calibration module is used to calibrate the stoma base after it has been cut by the cutting module based on the measurement data of the folds of the stoma skin and the measurement data of the deformation of the stoma base caused by the human body's compression and friction.

[0012] The feedback module is used to provide feedback on the stoma base after calibration by the calibration module;

[0013] The user module is used to receive the ostomy chassis model calibrated by the calibration module from the feedback module.

[0014] This invention utilizes a 3D scanning module combined with the U-Net network algorithm in the data processing unit to achieve high-precision modeling and contour recognition of the stoma's three-dimensional morphology, completely overcoming the limitations of traditional methods in acquiring three-dimensional data. Coupled with an adaptive safety boundary algorithm, it addresses the dynamic size changes caused by stoma type variations and patient activities in the background technology. By dynamically adjusting the cutting diameter based on stoma type and activity level, the roundness error of the cutting is reduced from 15-20% in manual cutting to ≤5%, directly improving the geometric fit between the baseplate and the stoma. This effectively reduces the risk of skin complications caused by cutting size deviations, compensating for the shortcomings of traditional manual measurement methods that lead to leakage and dermatitis due to errors.

[0015] Preferably, the 3D scanning module includes:

[0016] A 3D camera is used to perform three-dimensional scanning of the stoma skin surface to obtain depth data of the stoma skin surface;

[0017] A force sensor is used to acquire measurement data of wrinkles on the surface of the stoma skin and deformation measurement data of the stoma base plate caused by compression and friction by human skin. The force sensor is in contact with the surface of the stoma skin.

[0018] Preferably, the data processing unit performs depth optimization processing on the 3D scanning data acquired by the 3D scanning module, including the following steps:

[0019] A1: Normalize the original 3D scan data and map the coordinate values ​​to the [-1,1] interval. At the same time, data augmentation techniques are used to expand the training dataset through random rotation, scaling and flipping operations.

[0020] A2: A weighted cross-entropy loss function is introduced to address the imbalance between the stoma region and the background. The formula is:

[0021] ;

[0022] In the formula, Total number of pixels For real labels, To predict probabilities for the model, and These are the weight coefficients for positive and negative samples, respectively;

[0023] A3: Perform morphological operations on the segmentation results output by the network, and remove noise points and smooth the contour edges through erosion and dilation algorithms.

[0024] Preferably, the trimming measurement data incorporates an adaptive safety boundary algorithm, taking into account dynamic changes in the stoma, and the formula is:

[0025] ;

[0026] In the formula, To cut the diameter, To measure the diameter of the stoma, As the basic value for the safety boundary, This is the stoma type coefficient. This represents the activity level coefficient.

[0027] Preferably, the stoma chassis model incorporates a shape similarity algorithm to calculate the similarity between the generated model and historically well-adapted models, using the following formula:

[0028] ;

[0029] In the formula, To score the similarity, and These represent the number of feature points in the two models, respectively. and For the corresponding feature points, This is the feature point matching function.

[0030] Preferably, the cutting module includes:

[0031] A pre-cutting device is used to pre-cut the stoma base according to the received cutting measurement data and the stoma base model to obtain a pre-cut stoma base;

[0032] The hot melt cutting module is used to hot melt cut the pre-cut stoma chassis to obtain the stoma chassis product;

[0033] The hot melt cutting module includes:

[0034] The temperature control module is used to control the temperature of the hot melt cutting tool based on the received cutting measurement data and the chassis material;

[0035] A thermomelting cutting tool is used to perform thermomelting cutting on the stoma base plate according to the temperature control module to obtain the thermomelting cutting result. The thermomelting cutting tool is preferably a ceramic heating head.

[0036] Preferably, the temperature control module controls the temperature of the thermoforming cutting tool, and introduces a dynamic temperature compensation formula:

[0037] ;

[0038] In the formula, For cutting temperature, The melting point of the material. The thickness coefficient of the material. This is the cutting speed compensation coefficient. The environmental temperature influence coefficient. This is the difference between the current ambient temperature and the standard temperature.

[0039] Preferably, the calibration module includes:

[0040] The pre-cutting control module is used to control the pre-cutting device based on the measurement data of the wrinkles on the surface of the stoma skin and the measurement data of the deformation of the stoma base caused by the squeezing and friction of the human skin, and to calibrate and cut the stoma base after it is cut by the cutting module.

[0041] The hot melt cutting control module is used to control the hot melt cutting module to perform hot melt cutting on the calibrated stoma chassis to obtain the calibrated stoma chassis product.

[0042] Preferably, the calibration module performs calibration control on the pre-cutting and hot melt cutting processes, introducing a calibration error correction algorithm, the formula of which is:

[0043] ;

[0044] In the formula, For calibration correction amount, For the first The error value of this measurement. This is the average error value. For the number of times measured.

[0045] Preferably, the user module includes a chassis calibration model and a calibration model library for calibrating the stoma chassis after trimming by the trimming module based on the chassis calibration model, the calibration model library including:

[0046] A stoma skin surface wrinkle calibration model is used to calibrate the stoma base plate cut by the cutting module based on the measurement data of the stoma skin surface wrinkles.

[0047] The stoma base deformation calibration model is used to calibrate the stoma base cut by the cutting module based on the deformation measurement data generated by the stoma base being squeezed and rubbed by human skin.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] 1. This invention utilizes a 3D scanning module combined with the U-Net network algorithm in the data processing unit to achieve high-precision modeling and contour recognition of the stoma's three-dimensional morphology, completely overcoming the limitations of traditional methods in acquiring three-dimensional data. Coupled with an adaptive safety boundary algorithm, it addresses the dynamic size changes caused by stoma type variations and patient activities in the background technology. By dynamically adjusting the cutting diameter based on stoma type and activity level, the roundness error of the cutting is reduced from 15-20% in manual cutting to ≤5%, directly improving the geometric matching between the base and the stoma. This effectively reduces the risk of skin complications caused by cutting size deviations, compensating for the shortcomings of traditional manual measurement methods that lead to leakage and dermatitis due to errors.

[0050] 2. This invention also utilizes a fully automated "scan-modeling-cutting" process, combined with a thermal melting cutting module and a dynamic temperature compensation algorithm. This addresses the issues of diverse chassis materials, varying thicknesses, and the impact of ambient temperature changes on cutting results in the background technology. Cutting parameters are automatically matched based on the actual material characteristics, thickness, and ambient temperature. This solution not only avoids the edge burrs problem of manual cutting and reduces physical irritation to the patient's skin caused by rough edges, but also reduces cutting time by more than 90%, significantly improving operational efficiency and completely solving the pain points of low efficiency and excessive manual intervention in traditional methods.

[0051] 3. This invention also utilizes a shape similarity algorithm to compare the feature point matching degree of historically well-fitting models, generating a personalized chassis model. This addresses the problem of the "one-size-fits-all" cutting method in the prior art failing to adapt to individual differences. Simultaneously, the calibration module combines skin fold and chassis deformation data obtained from a force sensor, dynamically adjusting cutting parameters through a calibration error correction algorithm, enabling real-time response to dynamic changes in the patient's skin. This innovation allows the device to adapt to over 90% of common stoma shapes and chassis models, further improving fit accuracy and sealing during long-term use. It effectively solves the problems of frequent chassis replacements, increased patient financial burden, and nursing difficulties caused by poor adaptability in the prior art. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the overall architecture of the present invention. Detailed Implementation

[0053] Example 1: As Figure 1 As shown, the present invention relates to a 3D stoma measurement and stoma chassis automatic cutting device, which includes a 3D scanning module, a data processing unit, a measurement module, a stoma chassis model, a cutting module, a calibration module, a feedback module and a user module;

[0054] The 3D scanning module is used to perform a three-dimensional scan of the stoma skin surface and transmit the three-dimensional scan data of the stoma skin surface to the measurement module;

[0055] In an embodiment of the present invention, the 3D scanning module includes a 3D camera and multiple force sensors;

[0056] The 3D camera is used to perform three-dimensional scanning of the stoma skin surface to obtain depth data of the stoma skin surface. The 3D camera adopts a miniature structured light scanning head (wavelength 850nm) and can complete stoma three-dimensional modeling in 0.1s within a distance of 5cm with an accuracy of ±0.2mm.

[0057] The force sensor contacts the surface of the stoma skin to acquire measurement data of wrinkles on the surface of the stoma skin and measurement data of deformation of the stoma base caused by compression and friction by the human skin.

[0058] The data processing unit integrates a deep learning-based machine learning algorithm framework, using the U-Net network as the core model to perform deep optimization processing on the three-dimensional scanning data acquired by the 3D scanning module.

[0059] The U-Net network employs an encoder-decoder symmetrical structure, extracting features through path contraction and recovering spatial information through path expansion, making it particularly suitable for accurate segmentation of target regions in medical images. In the stoma contour recognition task, this network performs semantic segmentation on the depth data of the stoma skin surface:

[0060] In an embodiment of the present invention, the data processing unit performs depth optimization processing on the three-dimensional scanning data acquired by the 3D scanning module, including the following steps:

[0061] A1: Data preprocessing: Normalize the original 3D scan data and map the coordinate values ​​to the [-1,1] interval. At the same time, data augmentation techniques are used to expand the training dataset through random rotation, scaling and flipping operations to improve the model's generalization ability.

[0062] A2: Network Training: A weighted cross-entropy loss function is introduced to address the imbalance between the ostomy region (positive sample) and the background (negative sample). The formula is as follows:

[0063] ;

[0064] In the formula, Total number of pixels For true labels (0 or 1). To predict probabilities for the model, and These represent the weights for positive and negative samples, respectively, and the weights are dynamically adjusted based on the number of samples in each class in the training dataset. The training process uses the Adam optimizer with an initial learning rate of 0.001. A cosine annealing learning rate scheduling strategy is used to prevent overfitting, and the model is ultimately trained by minimizing the segmentation loss function. This enables high-precision automatic recognition of the stoma root contour;

[0065] A3: Post-processing optimization: Morphological operations are performed on the segmentation results output by the network. Noise points are removed and contour edges are smoothed through erosion and dilation algorithms to further improve recognition accuracy.

[0066] The measurement module is used to perform stoma measurement based on the received three-dimensional scanning data to obtain stoma measurement data (diameter at the narrowest point of the stoma, major and minor axes of the elliptical stoma) and cutting measurement data (safety boundary dimensions), and transmits the stoma measurement data and cutting measurement data to the stoma chassis model and the cutting module respectively;

[0067] In an embodiment of the present invention, the trimming measurement data incorporates an adaptive safety boundary algorithm, taking into account factors related to dynamic changes in the stoma (patient activity level), as shown in the formula:

[0068] ;

[0069] In the formula, To cut the diameter, To measure the diameter of the stoma, The baseline value for the safety boundary is 1 mm. The stoma type coefficient is 1 for ileostomy and 1.5 for colostomy. The activity level coefficient is 1 for resting state, 1.2 for mild activity, 1.4 for moderate activity, and 1.6 for severe activity.

[0070] The stoma chassis model is used to generate stoma chassis models of different sizes and styles based on the stoma measurement data output by the measurement module.

[0071] In embodiments of the present invention, a shape similarity algorithm is introduced to calculate the similarity between the generated model and historically well-adapted models, using the following formula:

[0072] ;

[0073] In the formula, To score the similarity, and These represent the number of feature points in the two models, respectively. and For the corresponding feature points, The feature point matching function (1 for successful matching, 0 for failure) prioritizes models with high similarity for subsequent processing.

[0074] The cutting module is used to cut the stoma base according to the received cutting measurement data and the stoma base model to obtain the cut stoma base.

[0075] In an embodiment of the present invention, the cutting module includes a pre-cutting device and a hot melt cutting module;

[0076] The pre-cutting device is used to pre-cut the stoma base according to the received cutting measurement data and the stoma base model to obtain the pre-cut stoma base.

[0077] The hot melt cutting module is used to hot melt cut the pre-cut stoma chassis to obtain the stoma chassis product;

[0078] The hot melt cutting module includes a hot melt cutting tool and a temperature control module for controlling the temperature of the hot melt cutting tool.

[0079] The temperature control module is used to control the temperature of the hot melt cutting tool based on the received cutting measurement data and the chassis material (PU or hydrocolloid) (the temperature controllable range is 200-400℃).

[0080] The temperature control module controls the temperature of the thermoforming cutting tool and incorporates a dynamic temperature compensation formula:

[0081] ;

[0082] In the formula, For cutting temperature, The melting point of the material is 220℃ for PU and 250℃ for hydrocolloid. This is the material thickness coefficient (0.5-1.5, with the coefficient increasing by 0.1 for every 0.1mm increase in thickness). This is the cutting speed compensation coefficient (the coefficient decreases by 0.05 for every 1 mm / s increase in cutting speed). The coefficient represents the influence of ambient temperature (the coefficient increases by 0.02 for every 1°C increase). This represents the difference between the current ambient temperature and the standard temperature (25℃). The ceramic heated blade cuts along a predetermined trajectory (cutting power 20W, speed 5mm / s), achieving precise cutting with rounded edges to avoid damage to the stoma mucosa.

[0083] The hot melt cutting tool is used to perform hot melt cutting on the stoma chassis according to the temperature control module to obtain the hot melt cutting result. The hot melt cutting tool is preferably a ceramic heating head.

[0084] The calibration module is used to calibrate the stoma base after it has been cut by the cutting module based on the measurement data of the folds of the stoma skin and the measurement data of the deformation of the stoma base caused by the human body's squeezing and friction.

[0085] In an embodiment of the present invention, the calibration module includes a pre-cutting control module and a hot melt cutting control module;

[0086] The pre-cutting control module is used to control the pre-cutting device based on the measurement data of the wrinkles on the surface of the stoma skin and the measurement data of the deformation of the stoma base caused by the squeezing and friction of the human skin, and to calibrate and cut the stoma base after it is cut by the cutting module.

[0087] The hot melt cutting control module is used to control the hot melt cutting module to perform hot melt cutting on the calibrated stoma chassis to obtain the calibrated stoma chassis product.

[0088] The calibration module performs calibration control on the pre-cutting and hot melt cutting processes, and introduces a calibration error correction algorithm, the formula of which is:

[0089] ;

[0090] In the formula, For calibration correction amount, For the first The error value of this measurement. This is the average error value. To determine the number of measurements, subsequent calibration parameters are adjusted based on the correction amount to improve the matching degree between the chassis and the stoma.

[0091] The feedback module is used to provide feedback on the stoma base after calibration by the calibration module;

[0092] The user module is used to receive the stoma chassis model calibrated by the calibration module from the feedback module;

[0093] In an embodiment of the present invention, the user module includes a chassis calibration model and a calibration model library for calibrating the stoma chassis after being cut by the cutting module based on the chassis calibration model.

[0094] The calibration model library includes a calibration model for stoma skin surface wrinkles and a calibration model for stoma chassis deformation.

[0095] The stoma skin surface wrinkle calibration model is used to calibrate the stoma base plate cut by the cutting module based on the measurement data of the stoma skin surface wrinkles.

[0096] The stoma chassis deformation calibration model is used to calibrate the stoma chassis cut by the cutting module based on the deformation measurement data generated by the stoma chassis being squeezed and rubbed by human skin.

[0097] Example 2: A 3D stereoscopic stoma measurement optimization method based on a model processing unit, comprising the following steps:

[0098] S1: Data Input and Model Generation: The three-dimensional scan data of the stoma skin surface with different parameters is input into the measurement module. The data processing unit optimizes the data through machine learning algorithms to obtain the corresponding stoma measurement data. The stoma chassis model generates the corresponding stoma chassis model based on the stoma measurement data.

[0099] S2: Matching and preliminary cutting: Match the stoma chassis model with the cutting measurement data, generate adaptation data based on the matching results, and cut out the preliminary stoma chassis based on the adaptation data through the pre-cutting equipment and the hot melt cutting module.

[0100] S3: Contact Area Analysis: Infrared spectroscopy is used to measure the initial contact area distribution between the stoma base and human skin, marking the contact area and transmitting the hardness data to the stoma base model. Contact area distribution data is measured during human movement, and the touched areas are removed to obtain the residual area of ​​the base. Force sensors are used to obtain the compressive force at different locations within the residual area. A contact pressure distribution evaluation formula is introduced:

[0101] ;

[0102] In the formula, For average contact pressure, For the first Pressure values ​​at each measurement point This represents the total number of measurement points used to assess the uniformity of contact pressure.

[0103] S4: Area Classification and Parameter Correction: By comparing the compressive pressure and maximum compressive pressure within the residual area of ​​the chassis, the area is divided into hard contact areas and soft contact areas; the height of the corresponding area after compression is detected to obtain the compression parameters; based on the humidity and pressure data of the contaminated area, the ratio of the humidity value to the maximum pressure value of the contaminated area is calculated, and the compression parameters corresponding to the hard contact area are corrected. An area hardness-pressure correlation algorithm is introduced:

[0104] ;

[0105] In the formula, is Corrected hardness value The measured hardness value, For correction factor, This is the local pressure value. To maximize the pressure value and improve the accuracy of regional classification;

[0106] S5: Parameter Processing and Model Correction: The average value of the extrusion parameters corresponding to the hard contact area and the humidity ratio of the contaminated area is processed to obtain the final hard contact area parameters. Combined with the determined user stoma chassis size and style, the user stoma chassis model is corrected to obtain the final user stoma chassis model.

[0107] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. A 3D stoma measurement and stoma base automatic cutting device, characterized in that, include: The 3D scanning module is used to perform a three-dimensional scan of the stoma skin surface and transmit the three-dimensional scan data of the stoma skin surface to the measurement module; The data processing unit integrates a deep learning-based machine learning algorithm framework, using the U-Net network as the core model to perform deep optimization processing on the 3D scanning data acquired by the 3D scanning module, including the following steps: A1: Normalize the original 3D scan data and map the coordinate values ​​to the [-1,1] interval. At the same time, data augmentation techniques are used to expand the training dataset through random rotation, scaling and flipping operations. A2: A weighted cross-entropy loss function is introduced to address the imbalance between the stoma region and the background. The formula is: ； In the formula, Total number of pixels For real labels, To predict probabilities for the model, and These are the weight coefficients for positive and negative samples, respectively; A3: Perform morphological operations on the segmentation results output by the network, and remove noise points and smooth the contour edges through erosion and dilation algorithms; The measurement module performs stoma measurements based on the received 3D scan data, obtaining stoma measurement data and trimming measurement data. These data are then transmitted to the stoma chassis model and the trimming module, respectively. The trimming measurement data incorporates an adaptive safety boundary algorithm, considering dynamic changes in the stoma. The formula is as follows: ； In the formula, To cut the diameter, To measure the diameter of the stoma, As the basic value for the safety boundary, This is the stoma type coefficient. This represents the activity level coefficient. The stoma chassis model is used to generate stoma chassis models of different sizes and styles based on the stoma measurement data output by the measurement module. The trimming module is used to trim the stoma base according to the received trimming measurement data and the stoma base model to obtain the trimmed stoma base. The calibration module is used to calibrate the stoma baseplate after it has been cut by the cutting module, based on the measurement data of stoma skin folds and the deformation measurement data of the stoma baseplate caused by compression and friction by the human body. The calibration module performs calibration control on the pre-cutting and hot-melt cutting processes, and introduces a calibration error correction algorithm, the formula of which is: ； In the formula, For calibration correction amount, For the first The error value of this measurement. This is the average error value. For the number of measurements; The feedback module is used to provide feedback on the stoma base after calibration by the calibration module; The user module is used to receive the ostomy chassis model calibrated by the calibration module from the feedback module.

2. The 3D stoma measurement and stoma base automatic cutting device according to claim 1, characterized in that, The 3D scanning module includes: A 3D camera is used to perform three-dimensional scanning of the stoma skin surface to obtain depth data of the stoma skin surface; A force sensor is used to acquire measurement data of wrinkles on the surface of the stoma skin and deformation measurement data of the stoma base plate caused by compression and friction by human skin. The force sensor is in contact with the surface of the stoma skin.

3. The 3D stoma measurement and stoma base automatic cutting device according to claim 1, characterized in that, The stoma chassis model incorporates a shape similarity algorithm to calculate the similarity between the generated model and historically well-adapted models. The formula is as follows: ； In the formula, To score the similarity, and These represent the number of feature points in the two models, respectively. and For the corresponding feature points, This is the feature point matching function.

4. The 3D stoma measurement and stoma base automatic cutting device according to claim 1, characterized in that, The cropping module includes: A pre-cutting device is used to pre-cut the stoma base according to the received cutting measurement data and the stoma base model to obtain a pre-cut stoma base; The hot melt cutting module is used to hot melt cut the pre-cut stoma chassis to obtain the stoma chassis product; The hot melt cutting module includes: The temperature control module is used to control the temperature of the hot melt cutting based on the received cutting measurement data and the chassis material. The thermomelting cutting tool is used to perform thermomelting cutting on the stoma base according to the temperature control module to obtain the thermomelting cutting result. The thermomelting cutting tool is a ceramic heating cutter head.

5. The 3D stoma measurement and stoma base automatic cutting device according to claim 4, characterized in that, The temperature control module controls the temperature of the thermoforming cutting tool and introduces a dynamic temperature compensation formula: ； In the formula, For cutting temperature, The melting point of the material. The thickness coefficient of the material. This is the cutting speed compensation coefficient. The environmental temperature influence coefficient. This is the difference between the current ambient temperature and the standard temperature.

6. The 3D stoma measurement and stoma base automatic cutting device according to claim 1, characterized in that, The calibration module includes: The pre-cutting control module is used to control the pre-cutting device based on the measurement data of the wrinkles on the surface of the stoma skin and the measurement data of the deformation of the stoma base caused by the squeezing and friction of the human skin, and to calibrate and cut the stoma base after it is cut by the cutting module. The hot melt cutting control module is used to control the hot melt cutting module to perform hot melt cutting on the calibrated stoma chassis to obtain the calibrated stoma chassis product.

7. The 3D stoma measurement and stoma base automatic cutting device according to claim 1, characterized in that, The user module includes a chassis calibration model and a calibration model library for calibrating the stoma chassis after trimming by the trimming module based on the chassis calibration model. The calibration model library includes: A stoma skin surface wrinkle calibration model is used to calibrate the stoma base plate cut by the cutting module based on the measurement data of the stoma skin surface wrinkles. The stoma base deformation calibration model is used to calibrate the stoma base cut by the cutting module based on the deformation measurement data generated by the stoma base being squeezed and rubbed by human skin.

Images