Intelligent production method and system of medical elastic bandage with antibacterial function

Abstract

The application provides an intelligent production method and system of a medical elastic bandage with antibacterial function, relates to the field of medical elastic bandage production, and comprises the following steps: determining performance parameters of raw materials, calculating proportioning parameters, mixing and weaving, collecting stress values and distribution densities, extracting deviation sequences to calculate correlation coefficients, and determining correlation regulation strategies according to the correlation coefficients to adjust process parameters and the amount of antibacterial functional materials. The application realizes uniform distribution of the antibacterial functional materials in the elastic bandage, improves the antibacterial performance and use comfort of the product, and reduces the production energy consumption.

Description

Technical Field

[0001] This invention relates to medical elastic bandage production technology, and more particularly to an intelligent production method and system for medical elastic bandages with antibacterial function. Background Technology

[0002] With the development of medical technology, the functional requirements for medical elastic bandages have expanded from simple elastic support to multifunctional composite properties such as antibacterial and infection prevention. Antibacterial medical elastic bandages, by incorporating antibacterial materials into the bandage material, can effectively inhibit bacterial growth around the wound, reduce the risk of infection, and promote wound healing.

[0003] Existing technologies lack precise quantitative calculations regarding the relationship between the properties of elastic fiber raw materials and the properties of antibacterial functional materials in the production of antibacterial medical elastic bandages. As a result, the amount of antibacterial functional materials added is mostly determined by experience, making it difficult to achieve the optimal balance between antibacterial effect and elastic performance. Insufficient addition of antibacterial agent may lead to poor antibacterial effect, while excessive addition may affect the elasticity and comfort of the bandage.

[0004] Traditional production methods cannot monitor the stress distribution and antibacterial material dispersion in different parts of the bandage during the weaving process in real time. This makes it difficult to ensure the uniform distribution of antibacterial functional materials in the bandage, resulting in significant differences in the antibacterial effect in different parts of the bandage, which affects the overall quality stability and safety of the product.

[0005] The existing production process lacks an intelligent control mechanism, making it unable to adaptively adjust according to real-time production parameters. This makes it difficult to cope with uncertainties in the production process, such as batch differences in raw materials and changes in environmental factors, resulting in large fluctuations in product quality, low production efficiency, and serious waste of resources. Summary of the Invention

[0006] This invention provides an intelligent production method and system for medical elastic bandages with antibacterial function, which can solve the problems in the prior art.

[0007] A first aspect of this invention provides an intelligent production method for a medical elastic bandage with antibacterial function, comprising: The tensile resilience of elastic fiber raw materials and the density of surface active sites in antibacterial functional materials were determined. The change in pore volume of elastic fibers is calculated based on tensile resilience, and the space occupied by antibacterial functional materials is calculated based on surface active site density. The mass ratio and dispersion particle size range of antibacterial functional materials are determined based on the geometric relationship between the change in pore volume and the space occupied, thus obtaining the formulation parameters. The antibacterial functional material is mixed with elastic fiber raw material according to the proportion parameters and then woven. During the weaving process, stress values ​​and distribution density of antibacterial functional materials at each weaving position are collected. The average stress value is calculated as the stress reference value, and the average distribution density is calculated as the density reference value. The deviation of the stress values ​​at each weaving position from the stress reference value is extracted to form a stress deviation sequence, and the deviation of the distribution density at each weaving position from the density reference value is extracted to form a density deviation sequence. The correlation coefficient between the stress deviation sequence and the density deviation sequence is calculated. Based on the correlation coefficient, a correlation control strategy is determined. The process parameters and the amount of antibacterial functional material added at the corresponding weaving position are adjusted according to the correlation control strategy to complete the intelligent production of bandages.

[0008] The determination of tensile resilience of elastic fiber raw materials and surface active site density of antibacterial functional materials includes: Cyclic tensile loads were applied to the elastic fiber material, and strain-time and stress-time curves of the elastic fiber material were collected. Identify the strain recovery start point and strain recovery end point of the strain-time curve, and calculate the time interval between the strain recovery start point and strain recovery end point as the strain recovery time; Identify the stress decay start point of the stress-time curve, extract the stress peak value corresponding to the stress decay start point and the residual stress value corresponding to the strain recovery termination point, and calculate the difference between the stress peak value and the residual stress value as the stress decay amplitude. The tensile resilience of elastic fiber raw materials is calculated based on strain recovery time and stress attenuation amplitude. Electron energy spectrum scanning is performed on the surface of antibacterial functional materials to obtain the distribution of elemental energy spectrum signal intensity, identify antibacterial characteristic elements, extract the spatial location coordinates of the energy spectrum signal intensity of antibacterial characteristic elements that exceed the preset intensity threshold, and mark the spatial location coordinates on the surface of antibacterial functional materials to form an elemental distribution map. The density of surface active sites of antibacterial functional materials is obtained by counting the total number of spatial location coordinates in the statistical element distribution map, measuring the total surface area of ​​the antibacterial functional material, and calculating the ratio of the total number to the total area.

[0009] The change in pore volume of the elastic fiber is calculated based on the tensile resilience, and the space occupied by the antibacterial functional material is calculated based on the surface active site density. The mass percentage and dispersion particle size range of the antibacterial functional material are determined based on the geometric relationship between the change in pore volume and the space occupied, resulting in the following formulation parameters: The fiber spacing expansion is determined based on the tensile resilience. The initial pore volume and target pore volume before and after tensile deformation are determined based on the fiber spacing expansion. The volume difference between the target pore volume and the initial pore volume is taken as the pore volume change of the elastic fiber. The size of a single particle of the antibacterial functional material is measured. The active coverage of a single particle is determined based on the density of surface active sites. The effective radius of action of a single particle is determined by combining the size of the single particle with the active coverage. The effective volume occupied by a single particle is determined based on the effective radius of action. The space occupied by the antibacterial functional material is determined based on the effective volume of action and the total number of particles to be added. By comparing the change in pore volume with the volume occupied in space, when the volume occupied in space is less than the change in pore volume, the effective radius of action is extracted as the dispersion particle size range of the antibacterial functional material. The mass ratio of the antibacterial functional material is determined based on the volume relationship between the change in pore volume and the volume occupied in space. The mass ratio and the dispersion particle size range are combined to form the proportioning parameters.

[0010] According to the mixing ratio parameters, the antibacterial functional material is mixed with elastic fiber raw material and then woven, including: The antibacterial functional material particles are screened according to the dispersion particle size range in the proportioning parameters. The mass of the antibacterial functional material particles and the elastic fiber raw material are weighed according to the mass ratio in the proportioning parameters. The antibacterial functional material particles and the elastic fiber raw material are put into the mixing device for mixing to obtain the mixture. A tensile load is applied to the mixture to cause the elastic fiber raw material to undergo tensile deformation. The fiber spacing expansion under tensile deformation is measured. When the fiber spacing expansion reaches a preset expansion threshold, the antibacterial functional material particles are driven into the expanded pores of the elastic fiber raw material to form an embedded distribution state. The tensile load is released to restore the elastic fiber raw material to its initial state. The amount of fiber spacing shrinkage during the recovery process is monitored. When the amount of fiber spacing shrinkage reaches the preset shrinkage threshold, a locking force is applied to the antibacterial functional material particles through pore shrinkage to form a stable bond. The mixture that has formed a stable bond is then transported to the weaving device for weaving, thus completing the weaving of the antibacterial functional material and the elastic fiber raw material.

[0011] During the weaving process, stress values ​​and the distribution density of the antibacterial functional material at each weaving location are collected. The average stress value is calculated as the stress reference value, and the average distribution density is calculated as the density reference value, including: Multiple monitoring points are set along the weaving direction as weaving positions during the weaving process; Stress sensors and density detectors are arranged at each weaving position. The stress sensors collect the stress values ​​of each weaving position under the action of weaving tension, and the density detectors scan the number of particles per unit area of ​​the antibacterial functional material at each weaving position. The number of particles per unit area is taken as the distribution density of the antibacterial functional material at each weaving position. Stress datasets were constructed by extracting stress values ​​from all weaving locations, and density datasets were constructed by extracting the distribution density of antibacterial functional materials from all weaving locations. The average stress value is obtained by statistically calculating the stress values ​​in the stress dataset and used as the stress benchmark value. Similarly, the average distribution density of the antibacterial functional material in the density dataset is obtained by statistically calculating the distribution density and used as the density benchmark value.

[0012] The deviations of stress values ​​at each weaving location from the stress reference value are extracted to form a stress deviation sequence, and the deviations of distribution density at each weaving location from the density reference value are extracted to form a density deviation sequence. The correlation coefficients between the stress deviation sequence and the density deviation sequence are calculated, including: Collect stress values ​​and distribution density time series data at each weaving location, identify stress abrupt change points and density abrupt change points in the distribution density time series data, and generate a disturbance transmission sequence; The deviation between the stress value at each weaving position and the stress reference value is determined based on the disturbance transmission sequence to form a stress deviation sequence, and the deviation between the distribution density at each weaving position and the density reference value is determined to form a density deviation sequence. Wavelet transform is performed on the stress deviation sequence and density deviation sequence to obtain transform coefficients. The transform coefficients are reconstructed into a coupling spectrum, and the coupling component with the maximum information content is extracted from the coupling spectrum. The coupled components are mapped to the reconstruction space to obtain the reconstruction sequence, and the distribution entropy value of the reconstruction sequence is calculated as a dynamic correlation index. Adaptive classification is performed on the dynamic correlation index to obtain the classification center value. The classification center value is normalized to obtain the interval value. The interval value is used as the synchronization threshold to determine the deviation direction of the stress deviation sequence and the density deviation sequence at each weaving position. The ratio of the number of positions with the same deviation direction to the total number of positions is used as the correlation coefficient.

[0013] Based on the correlation coefficient, a correlation control strategy is determined. Following this strategy, the process parameters and the amount of antibacterial functional material applied at the corresponding weaving positions are adjusted to achieve intelligent production of the bandages. The correlation coefficients are scaled to obtain the regulation coefficient sequence. The amplitude distribution of the regulation coefficient sequence is calculated, and the amplitude is divided into the main effect coefficient and the secondary effect coefficient. The control feature vector is calculated based on the main effect coefficient and the secondary effect coefficient, and then converted into process control parameters and dosage control parameters. The proportional relationship between the weaving speed adjustment and the weaving tension adjustment is calculated based on the process control parameters, and the proportional relationship between the feeding rate adjustment and the feeding concentration adjustment is calculated based on the feeding control parameters. A process control matrix is ​​constructed based on the proportional relationship between the weaving speed adjustment and the weaving tension adjustment, and an application control matrix is ​​constructed based on the proportional relationship between the application rate adjustment and the application concentration adjustment. The process control matrix and the feed control matrix are combined to generate a linkage control sequence, and the control priority is determined based on the linkage control sequence. Adjust process parameters and the amount of antibacterial functional material added according to the control priority, and calculate the dynamic change range of stress value and distribution density of antibacterial functional material during the control process as the control convergence degree. When the control convergence meets the preset convergence threshold, the adjustment of process parameters and the amount of antibacterial functional material added is stopped, and the intelligent production of bandages is completed.

[0014] A second aspect of the present invention provides an intelligent production system for medical elastic bandages with antibacterial function, comprising: The first unit is used to determine the tensile resilience of elastic fiber raw materials and the surface active site density of antibacterial functional materials. The second unit is used to calculate the change in pore volume of elastic fibers based on tensile resilience, calculate the space occupied by antibacterial functional materials based on surface active site density, and determine the mass ratio and dispersion particle size range of antibacterial functional materials based on the geometric relationship between the change in pore volume and the space occupied, thereby obtaining the formulation parameters. The third unit is used to mix and weave antibacterial functional materials with elastic fiber raw materials according to the proportioning parameters. The fourth unit is used to collect stress values ​​and distribution density of antibacterial functional materials at each weaving position during the weaving process, calculate the average stress value as the stress reference value, and calculate the average distribution density as the density reference value. The fifth unit is used to extract the deviation of the stress value from the stress reference value at each weaving position to form a stress deviation sequence, extract the deviation of the distribution density from the density reference value at each weaving position to form a density deviation sequence, and calculate the correlation coefficient between the stress deviation sequence and the density deviation sequence. The sixth unit is used to determine the correlation control strategy based on the correlation coefficient, and adjust the process parameters and antibacterial functional material dosage at the corresponding weaving position according to the correlation control strategy to complete the intelligent production of bandages.

[0015] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0016] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0017] In this embodiment, a scientific correlation was established between the tensile resilience of the elastic fiber raw material and the surface active site density of the antibacterial functional material by measuring the two. This overcomes the limitations of traditional production methods where the amount of antibacterial material added is determined empirically, achieving precise proportioning of the antibacterial material. Based on the geometric relationship between pore volume change and space occupied volume, the mass ratio and dispersion particle size range of the antibacterial functional material were determined, ensuring optimal distribution of the antibacterial material in the elastic fiber, improving the antibacterial effect without affecting the elasticity of the bandage. A correlation analysis method between stress deviation sequence and density deviation sequence was introduced. By calculating the correlation coefficient, a dynamic correlation model between stress distribution and antibacterial material distribution during weaving was established, enabling precise monitoring of the production process. Based on the correlation control strategy determined by the correlation coefficient, process parameters and the amount of antibacterial functional material added can be adjusted in real time and intelligently, solving the technical problems of uneven distribution of antibacterial material and unstable bandage performance in traditional production methods, significantly improving product consistency and antibacterial performance. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an intelligent production method for an antibacterial medical elastic bandage according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the monitoring of stress and antibacterial material density during the weaving process and the construction of benchmark values, as per an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0021] Figure 1 This is a flowchart illustrating an intelligent production method for antibacterial medical elastic bandages according to an embodiment of the present invention. Figure 1 As shown, the method includes: The tensile resilience of elastic fiber raw materials and the density of surface active sites in antibacterial functional materials were determined. The change in pore volume of elastic fibers is calculated based on tensile resilience, and the space occupied by antibacterial functional materials is calculated based on surface active site density. The mass ratio and dispersion particle size range of antibacterial functional materials are determined based on the geometric relationship between the change in pore volume and the space occupied, thus obtaining the formulation parameters. The antibacterial functional material is mixed with elastic fiber raw material according to the proportion parameters and then woven. During the weaving process, stress values ​​and distribution density of antibacterial functional materials at each weaving position are collected. The average stress value is calculated as the stress reference value, and the average distribution density is calculated as the density reference value. The deviation of the stress values ​​at each weaving position from the stress reference value is extracted to form a stress deviation sequence, and the deviation of the distribution density at each weaving position from the density reference value is extracted to form a density deviation sequence. The correlation coefficient between the stress deviation sequence and the density deviation sequence is calculated. Based on the correlation coefficient, a correlation control strategy is determined. The process parameters and the amount of antibacterial functional material added at the corresponding weaving position are adjusted according to the correlation control strategy to complete the intelligent production of bandages.

[0022] In one optional embodiment, determining the tensile resilience of the elastic fiber raw material and the surface active site density of the antibacterial functional material includes: Cyclic tensile loads were applied to the elastic fiber material, and strain-time and stress-time curves of the elastic fiber material were collected. Identify the strain recovery start point and strain recovery end point of the strain-time curve, and calculate the time interval between the strain recovery start point and strain recovery end point as the strain recovery time; Identify the stress decay start point of the stress-time curve, extract the stress peak value corresponding to the stress decay start point and the residual stress value corresponding to the strain recovery termination point, and calculate the difference between the stress peak value and the residual stress value as the stress decay amplitude. The tensile resilience of elastic fiber raw materials is calculated based on strain recovery time and stress attenuation amplitude. Electron energy spectrum scanning is performed on the surface of antibacterial functional materials to obtain the distribution of elemental energy spectrum signal intensity, identify antibacterial characteristic elements, extract the spatial location coordinates of the energy spectrum signal intensity of antibacterial characteristic elements that exceed the preset intensity threshold, and mark the spatial location coordinates on the surface of antibacterial functional materials to form an elemental distribution map. The density of surface active sites of antibacterial functional materials is obtained by counting the total number of spatial location coordinates in the statistical element distribution map, measuring the total surface area of ​​the antibacterial functional material, and calculating the ratio of the total number to the total area.

[0023] In this embodiment, a high-precision electronic universal testing machine was used to perform cyclic tensile testing on the elastic fiber raw material. The loading rate was set to 50 mm / min, and the number of cycles was 10, with each cycle including a loading phase and an unloading phase. During the test, the deformation and load values ​​of the sample were collected in real time using displacement and force sensors at a sampling frequency of 100 Hz, forming complete strain-time and stress-time curves.

[0024] For the acquired strain-time curves, a slope change rate monitoring algorithm is used to identify the strain recovery start point and strain recovery end point. Specifically, the identification method involves calculating the slope between adjacent sampling points on the curve. When the slope changes from zero to a negative value and the rate of change exceeds a preset threshold of 0.01, the point is marked as the strain recovery start point; when the slope change rate is less than 0.005 for five consecutive sampling points, the point is marked as the strain recovery end point. After obtaining these two feature points, the time difference between them is calculated, which is the strain recovery time. Taking the testing of a certain elastic fiber raw material as an example, if the time coordinate of the strain recovery start point is 15.36 seconds and the time coordinate of the strain recovery end point is 18.75 seconds, then the strain recovery time is 3.39 seconds.

[0025] For stress-time curves, the stress decay initiation point typically corresponds to the time point at the end of the loading phase. A peak detection algorithm is used to identify local maxima in the stress-time curve, and the first local maximum is marked as the stress decay initiation point. The stress value corresponding to this point is extracted as the peak stress, and the stress value corresponding to the previously determined strain recovery termination point is obtained as the residual stress value. Taking the testing of a certain elastic fiber material as an example, the peak stress is 4.82 MPa, the residual stress is 0.65 MPa, and the calculated stress decay amplitude is 4.17 MPa.

[0026] Based on the above parameters, the tensile resilience of the elastic fiber raw material is calculated. The formula for calculating the tensile resilience is: Tensile resilience = (Initial strain - Residual strain) / Initial strain × 100%, where the initial strain is the strain value corresponding to the strain recovery start point, and the residual strain is the strain value corresponding to the strain recovery end point. Simultaneously, correction factors for stress attenuation amplitude and strain recovery time are introduced. The formula for calculating the correction factor is: K = 1 - Stress attenuation amplitude / (10 × Strain recovery time), with K ranging from 0 to 1. The final tensile resilience is calculated as: Corrected tensile resilience = Tensile resilience × K. Taking a certain elastic fiber raw material as an example, with an initial strain of 35% and a residual strain of 3%, the calculated tensile resilience is 91.4%, the correction factor K = 0.877, and the corrected tensile resilience is 80.2%.

[0027] The surface active site density of antibacterial functional materials was determined using X-ray photoelectron spectroscopy. The antibacterial functional material sample was placed in an ultra-high vacuum chamber with a sample surface area of ​​1 square centimeter and a vacuum level controlled at 10. -9 In the Pa range, a monochromatic aluminum target X-ray source was used for excitation at an energy of 1486.6 eV. The scan step size was set to 0.1 eV, and a full spectrum of the material surface in the 0-1200 eV range was obtained using an energy of 20 eV. Narrow-spectrum scanning of antibacterial characteristic elements was performed, with 10 scans to improve the signal-to-noise ratio.

[0028] By analyzing energy dispersive spectroscopy (EDS) data, characteristic peaks of antibacterial elements, such as silver, copper, and zinc, were identified. The characteristic peaks of silver are located at 368.2 eV and 374.2 eV, corresponding to the Ag 3d⁵ / ² and Ag 3d³ / ² energy levels; the characteristic peaks of copper are located at 932.7 eV and 952.6 eV, corresponding to the Cu 2p³ / ² and Cu 2p¹ / ² energy levels; and the characteristic peaks of zinc are located at 1021.7 eV and 1044.8 eV, corresponding to the Zn 2p³ / ² and Zn 2p¹ / ² energy levels. A threshold of 30% of the peak height for the characteristic element's EDS signal intensity was set, and the spatial coordinates of locations where the EDS signal intensity exceeded the threshold were extracted.

[0029] Based on the extracted coordinate information, a distribution map of antibacterial elements on the material surface was constructed. The map resolution was set to 100×100 pixels, corresponding to a 1 square centimeter area on the actual material surface. Each pixel corresponds to an area with an actual size of 100 micrometers × 100 micrometers, marking the locations where the energy spectrum signal intensity of the antibacterial characteristic elements exceeded the threshold. The total number of marked points in the map was counted, and the total number was divided by the total area of ​​the material surface to obtain the surface active site density. Taking a certain antibacterial functional material as an example, 3625 silver element distribution points were detected in a 1 square centimeter surface area, and the surface active site density was calculated to be 3625 sites / square centimeter, or 36.25 sites / square millimeter.

[0030] In this embodiment, by quantitatively analyzing the mechanical properties of elastic fibers and the microstructure of antibacterial materials, the key performance indicators of medical elastic bandages were accurately evaluated, providing reliable data support for intelligent manufacturing. The strain-time curve and stress-time curve analysis techniques used in the method, combined with a correction factor algorithm, improved the accuracy of tensile resilience measurement; while electron spectroscopy scanning and signal intensity threshold processing techniques ensured high precision and repeatability in the determination of antibacterial active site density.

[0031] In one optional implementation, the pore volume change of the elastic fiber is calculated based on the tensile resilience, the space-occupied volume of the antibacterial functional material is calculated based on the surface active site density, and the mass percentage and dispersion particle size range of the antibacterial functional material are determined based on the geometric relationship between the pore volume change and the space-occupied volume, resulting in the following proportioning parameters: The fiber spacing expansion is determined based on the tensile resilience. The initial pore volume and target pore volume before and after tensile deformation are determined based on the fiber spacing expansion. The volume difference between the target pore volume and the initial pore volume is taken as the pore volume change of the elastic fiber. The size of a single particle of the antibacterial functional material is measured. The active coverage of a single particle is determined based on the density of surface active sites. The effective radius of action of a single particle is determined by combining the size of the single particle with the active coverage. The effective volume occupied by a single particle is determined based on the effective radius of action. The space occupied by the antibacterial functional material is determined based on the effective volume of action and the total number of particles to be added. By comparing the change in pore volume with the volume occupied in space, when the volume occupied in space is less than the change in pore volume, the effective radius of action is extracted as the dispersion particle size range of the antibacterial functional material. The mass ratio of the antibacterial functional material is determined based on the volume relationship between the change in pore volume and the volume occupied in space. The mass ratio and the dispersion particle size range are combined to form the proportioning parameters.

[0032] In the specific implementation process, a high-precision optical microscope was used to analyze the microstructure of the elastic fibers, and the fiber spacing was measured at 100x magnification. For elastic fibers with a tensile resilience of 80.2%, the formula for calculating the fiber spacing expansion is: Fiber spacing expansion = Initial fiber spacing × (1 - Tensile resilience). The measured initial fiber spacing was 25 micrometers, and the calculated fiber spacing expansion was 4.95 micrometers.

[0033] The initial and target pore volumes before and after tensile deformation are determined based on the fiber spacing expansion. A three-dimensional volumetric modeling method is used to calculate the pore volume, establishing a three-dimensional mesh structure model of the elastic fiber, where each mesh cell represents a basic pore cell. The formula for calculating the volume of a single pore cell is: Pore cell volume = Fiber spacing. 3 × Porosity factor. The porosity factor is determined based on the fiber weaving method. For conventional medical elastic bandages, the porosity factor is taken as 0.58. For 1 cubic centimeter of elastic fiber material, the initial pore volume is calculated as: Initial pore volume = 1 × 0.58 × 25 3 / 1 3 =0.0906 cubic centimeters. The target pore volume is calculated as: Target pore volume = 1 × 0.58 × 29.95 3 / 1 3=0.1554 cubic centimeters. The change in pore volume is the target pore volume minus the initial pore volume, which is 0.0648 cubic centimeters.

[0034] The particle size of the antibacterial functional material was measured using a laser particle size analyzer. A 0.1 g sample was dispersed in 5 mL of deionized water and ultrasonically dispersed for 15 minutes. The particle size distribution was then measured. The average particle size of the antibacterial functional material was found to be 0.8 μm. The active coverage area of ​​each particle was determined based on the surface active site density, which was 36.25 sites / mm². The active coverage area was calculated using the formula: Active coverage area = π × (active diffusion distance). 2 The active diffusion distance is negatively correlated with the density of surface active sites, and the relationship is obtained through experimental fitting: Active diffusion distance = 10 × (surface active site density) (-1 / 4) Substituting the data, the calculated active diffusion distance is 3.76 micrometers, and the active coverage area is 44.38 square micrometers.

[0035] The effective radius of a single particle is calculated as the sum of the particle radius and the active diffusion distance, i.e., 0.4 μm + 3.76 μm = 4.16 μm. The effective volume occupied by a single particle is 4 / 3 × π × (effective radius)³, which is calculated to be 301.29 cubic micrometers. The total number of particles to be added is calculated based on the density and mass of the antibacterial material. The density of the antibacterial material is 4.5 g / cm³, and the mass is 0.1 g. The total number of particles is 0.1 / (4.5 × 4 / 3 × π × (0.4 × 10⁻⁶)³). -6 ) 3 ) = 2.98 × 10 12 The space occupied by the antibacterial functional material is the effective volume of a single particle multiplied by the total number of particles, i.e., 301.29 × 2.98 × 10⁻⁶. 12 =0.0598 cubic centimeters.

[0036] Comparing the pore volume change of 0.0648 cubic centimeters with the space-occupied volume of 0.0598 cubic centimeters, the space-occupied volume is less than the pore volume change, satisfying the spatial constraint condition. An effective radius of 4.16 micrometers is extracted as the dispersion particle size range for the antibacterial functional material. Considering material dispersibility, the particle size range is set to 3.5-4.8 micrometers. The mass percentage of the antibacterial functional material is determined based on the volume relationship between the pore volume change and the space-occupied volume, calculated using the formula: Mass percentage = Mass of antibacterial functional material / (Mass of antibacterial functional material + Mass of elastic fiber). The elastic fiber mass is 1.5 grams, and the antibacterial functional material mass is 0.1 grams, resulting in a mass percentage of 0.1 / (0.1 + 1.5) = 6.25%. This mass percentage of 6.25% is combined with the dispersion particle size range of 3.5-4.8 micrometers to form the mixing ratio parameters.

[0037] For elastic fibers with different tensile resilience rates, parameters can be adjusted. When the tensile resilience rate is 85.5%, the fiber spacing expansion decreases to 3.63 micrometers, and the pore volume change decreases to 0.0476 cubic centimeters. At this point, it is necessary to adjust the particle size or mass of the antibacterial functional material to meet the space constraints. Adjusting the particle size to 0.6 micrometers changes the effective radius to 3.96 micrometers, reducing the effective volume of a single particle, allowing the same mass of antibacterial functional material to be used without exceeding the space constraints.

[0038] When the tensile resilience is 75.5%, the fiber spacing expansion increases to 6.13 micrometers, and the pore volume change increases to 0.0805 cubic centimeters. At this point, the amount of antibacterial functional material can be increased to 0.12 grams, corresponding to a mass percentage of 7.41%. Alternatively, the original amount can be maintained, utilizing the excess pore volume to improve the breathability and comfort of the bandage. In actual production, the formulation parameters are dynamically adjusted based on the measured tensile resilience and surface active site density of each batch of raw materials to ensure stable product performance.

[0039] In this embodiment, based on the microstructure and mechanical properties of elastic fibers, combined with the surface characteristics of antibacterial functional materials, a mapping relationship between the spatial distribution of materials and their functional performance was established, achieving precise optimization of the proportioning parameters. By analyzing the geometric relationship between the change in pore volume and the volume occupied, it was ensured that the antibacterial functional materials could be fully dispersed in the elastic fiber network, guaranteeing both the uniformity and durability of the antibacterial effect while avoiding the overuse of antibacterial materials.

[0040] In one optional embodiment, mixing the antibacterial functional material with the elastic fiber raw material according to the proportioning parameters and then weaving it includes: The antibacterial functional material particles are screened according to the dispersion particle size range in the proportioning parameters. The mass of the antibacterial functional material particles and the elastic fiber raw material are weighed according to the mass ratio in the proportioning parameters. The antibacterial functional material particles and the elastic fiber raw material are put into the mixing device for mixing to obtain the mixture. A tensile load is applied to the mixture to cause the elastic fiber raw material to undergo tensile deformation. The fiber spacing expansion under tensile deformation is measured. When the fiber spacing expansion reaches a preset expansion threshold, the antibacterial functional material particles are driven into the expanded pores of the elastic fiber raw material to form an embedded distribution state. The tensile load is released to restore the elastic fiber raw material to its initial state. The amount of fiber spacing shrinkage during the recovery process is monitored. When the amount of fiber spacing shrinkage reaches the preset shrinkage threshold, a locking force is applied to the antibacterial functional material particles through pore shrinkage to form a stable bond. The mixture that has formed a stable bond is then transported to the weaving device for weaving, thus completing the weaving of the antibacterial functional material and the elastic fiber raw material.

[0041] When screening antibacterial functional material particles according to the particle size range specified in the formulation parameters, vibrating sieving technology is used for particle screening. The antibacterial functional raw material is fed into a vibrating sieving device equipped with upper and lower screens. The upper screen has an aperture of 4.8 micrometers, and the lower screen has an aperture of 3.5 micrometers. The vibration frequency is set to 60 Hz, the amplitude to 2 mm, and the sieving time to 30 minutes. Particles that pass through the upper screen but are intercepted by the lower screen are the desired antibacterial functional material particles within the 3.5-4.8 micrometer range. To improve sieving efficiency, ultrasonic-assisted technology is used during the sieving process. The ultrasonic power is set to 300 watts, and the frequency to 40 kHz, effectively preventing particle accumulation and screen clogging.

[0042] The mass of antibacterial functional material particles and elastic fiber raw materials is weighed according to the mass ratio parameters. Taking the production of 100 grams of medical elastic bandage as an example, when the mass ratio is 6.25%, 6.25 grams of antibacterial functional material particles and 93.75 grams of elastic fiber raw materials need to be weighed. A precision electronic balance with an accuracy of 0.001 grams is used for weighing. Considering the loss of antibacterial functional material particles during sieving and transfer, a 5% margin is added during actual weighing, i.e., 6.56 grams of antibacterial functional material particles are weighed. To ensure weighing accuracy, a three-parallel weighing method is used, and the average of the three weighing results is taken as the final feed amount.

[0043] The weighed antibacterial functional material granules and elastic fiber raw materials were added to a mixing device for mixing. The mixing device was a twin-screw mixer with a screw diameter of 50 mm, a length-to-diameter ratio of 32:1, and a screw speed of 120 rpm. The mixing chamber temperature was controlled at 25±2℃, and the relative humidity was controlled at 45±5% to avoid static electricity accumulation affecting the mixing uniformity. The mixing time was 15 minutes. During the mixing process, samples were taken from the mixing chamber every 3 minutes for testing. The dispersion state of the antibacterial functional material granules was observed under a microscope. When the particle agglomeration rate was less than 5%, the mixing was considered sufficient. To enhance the affinity between the antibacterial functional material and the elastic fiber, 0.5% hydrophilic surfactant was added during the mixing process to improve the wettability of the particle surface.

[0044] A tensile load is applied to the mixture to induce tensile deformation in the elastic fiber raw material, and the operation is carried out using a continuous stretching device. The stretching device consists of a pair of precision synchronously rotating stretching rollers with an adjustable roller spacing and a stretching rate of 30 mm / s. The mixture is uniformly fed between the stretching rollers, and the material undergoes a 35% tensile deformation by controlling the roller spacing. During the stretching process, a laser rangefinder is used to measure the change in fiber spacing in real time. The sensor has a sampling frequency of 200 Hz and a resolution of 0.1 μm. When the detected fiber spacing expansion reaches a preset expansion threshold of 4.95 μm, a vibration mechanism is triggered. The vibration mechanism consists of piezoelectric ceramic oscillators with a vibration frequency of 100 Hz, an amplitude of 0.8 mm, and an action time of 0.5 seconds, generating mechanical vibration perpendicular to the stretching direction, driving the antibacterial functional material particles into the expanded pores of the elastic fiber raw material.

[0045] To verify the particle embedding effect, a high-speed microscopic imaging system was used to observe the mixture in real time under tension. The lens magnification was 200x, and the image acquisition rate was 500 frames per second. When it was observed that more than 90% of the antibacterial functional material particles successfully entered the fiber pores, it was determined that the particles had formed an embedded distribution. At this point, the mixture continued to move under tension and proceeded to the next process.

[0046] Releasing the tensile load allows the elastic fiber material to return to its initial state, and gradual relaxation is achieved by controlling the rotational speed difference of the stretching rollers. The relaxation rate is controlled at a 2% reduction in stretch per second to ensure uniform fiber recovery without abrupt deformation. Simultaneously, a laser rangefinder continuously monitors the fiber spacing shrinkage during the recovery process. When the fiber spacing shrinkage reaches a preset shrinkage threshold of 4.46 micrometers (i.e., 90% of the total expansion), the pressure roller device is activated to apply vertical pressure to the mixture at a value of 0.5 MPa for 1.0 second. This pressure further shrinks the inter-fiber pores, applying a locking force to the antibacterial functional material particles and forming a stable bond.

[0047] To verify the quality of the stable bonding state, a shear strength test was used. A shear force parallel to the fiber direction was applied to the mixture, and the particle shedding rate was measured. When the particle shedding rate was less than 2%, a stable bonding state was considered to have been formed. Simultaneously, the interfacial bonding between the fibers and particles was observed using a scanning electron microscope to confirm that the particles were firmly wrapped by the fibers, with no obvious interfacial gaps. After the stable bonding state was formed, the mixture was conveyed to the weaving device via a conveyor belt.

[0048] The weaving device employs warp knitting technology at a speed of 500 threads / minute and a density of 28 threads / cm. During the weaving process, the ambient temperature is maintained at 23±1℃ and the relative humidity at 50±3% to prevent changes in fiber elasticity. After weaving, the medical elastic bandage is heat-treated at 75℃ for 10 minutes to further enhance the bonding stability between the fiber and the antibacterial functional material. The final product has a thickness of 1.2 mm, a width of 10 cm, and the length is cut according to requirements, with a standard specification of 4.5 meters per roll. The finished product passes a tensile performance test; after 100 cycles of reciprocating stretching at 30% elongation, the antibacterial functional material particle shedding rate is less than 0.5%, meeting medical standards.

[0049] In this embodiment, by precisely controlling the stretching and recovery processes, the antibacterial functional material is achieved through three-dimensional uniform distribution and stable locking within the fiber network. Compared to traditional surface coating or impregnation processes, the antibacterial functional material formed by this method has a stronger bond with the elastic fibers, is less prone to detachment, and significantly improves the durability of the antibacterial effect. Simultaneously, because the antibacterial functional material is locked within the fiber pores, direct contact with human skin is reduced, potential irritation is decreased, and the product's biosafety is improved.

[0050] Figure 2 The flowchart illustrating the stress and antibacterial material density monitoring and benchmark construction during the weaving process of this embodiment is shown.

[0051] In one optional implementation, during the weaving process, stress values ​​and the distribution density of the antibacterial functional material at each weaving position are collected. The average stress value is calculated as a stress reference value, and the average distribution density is calculated as a density reference value, including: Multiple monitoring points are set along the weaving direction as weaving positions during the weaving process; Stress sensors and density detectors are arranged at each weaving position. The stress sensors collect the stress values ​​of each weaving position under the action of weaving tension, and the density detectors scan the number of particles per unit area of ​​the antibacterial functional material at each weaving position. The number of particles per unit area is taken as the distribution density of the antibacterial functional material at each weaving position. Stress datasets were constructed by extracting stress values ​​from all weaving locations, and density datasets were constructed by extracting the distribution density of antibacterial functional materials from all weaving locations. The average stress value is obtained by statistically calculating the stress values ​​in the stress dataset and used as the stress benchmark value. Similarly, the average distribution density of the antibacterial functional material in the density dataset is obtained by statistically calculating the distribution density and used as the density benchmark value.

[0052] During the weaving process, multiple monitoring points are set along the weaving direction as weaving positions. The monitoring points are set according to the principle of uniform distribution. In the weaving of a 10 cm wide medical elastic bandage, 5 monitoring points are set in the horizontal direction with a spacing of 2 cm; and 1 monitoring point is set every 10 cm in the vertical direction. For a standard 4.5-meter long bandage, a total of 45 vertical monitoring points are set, forming a total of 225 monitoring positions, which constitute a monitoring grid. The monitoring point positions are located using laser marking technology with a marking accuracy of ±0.1 mm to ensure accurate spatial correspondence of the monitoring data.

[0053] Stress sensors and density detectors are placed at each weaving position. The stress sensors are thin-film piezoresistive stress sensors with a sensitivity of 0.5, a range of 0-50, and a response time of less than 5 milliseconds. The sensors are fixed to the guide bars of the weaving machine using miniature clamps, and the contact points with the yarn are treated with polytetrafluoroethylene coating to reduce friction interference. The sensor output signal is amplified and filtered by a signal conditioning circuit. The filtering uses a low-pass filter with a cutoff frequency of 200 Hz to remove high-frequency interference. The processed signal is converted into a digital signal by a 16-bit analog-to-digital converter with a sampling frequency of 500 Hz. The stress sensors are calibrated before being put into use. A known load is applied to the sensor using standard weights, the output voltage value is recorded, and a linear relationship between load and voltage is established. The linear correlation coefficient of the calibration curve is not less than 0.995.

[0054] The density detector employs a high-resolution optical scanning device, consisting of a light source, a lens assembly, and an image sensor. The light source uses a blue LED with a wavelength of 470 nm, a light intensity of 2000 millicandela, and an illumination angle of 45 degrees. The lens assembly has a focal length of 25 mm, a field of view of 30 degrees, and a working distance of 50 mm. The image sensor is a CMOS type with a resolution of 2400 × 1800 pixels, a single pixel size of 2.8 μm, and a frame rate of 20 frames per second. The density detector scans a 5 × 5 mm square area, corresponding to 1786 × 1786 pixels in the image. Antibacterial functional material particles are identified using an image processing algorithm. This algorithm combines color thresholding and morphological processing, first initially screening potential particle regions based on color features, and then performing precise identification using morphological features. To improve recognition accuracy, trace amounts of fluorescent dye are added to the antibacterial functional material particles before production, producing 525 nm green fluorescence under blue light illumination, creating a clear contrast between the particles and the image.

[0055] Stress values ​​at each knitting location under knitting tension are collected using stress sensors. During the knitting process, the spindle speed of the knitting machine is set to 600 rpm, and the knitting tension is controlled at 15 ± 1 N. The stress sensors collect stress values ​​at the knitting locations in real time, and the data acquisition system records a stress reading every 0.1 seconds. The average of five consecutive readings is taken as the stress value for that knitting location. Taking a certain knitting location as an example, the five consecutive stress readings are 14.8 N, 15.2 N, 15.0 N, 14.9 N, and 15.1 N, respectively. The calculated average is 15.0 N, which is taken as the stress value for that location.

[0056] The density detector scans the particle distribution per unit area of ​​the antibacterial functional material at each weaving location to detect the number of particles. The density detector scans each monitoring point in real time during the weaving machine's operation, acquiring three images for each point. Particles are identified using image processing algorithms. Image processing includes four steps: image enhancement, background removal, threshold segmentation, and particle counting. Image enhancement uses histogram equalization to improve image contrast; background removal uses a combination of Gaussian filtering and morphological opening operations; threshold segmentation uses the maximum inter-class variance method to automatically determine the threshold; and particle counting uses a connected component labeling algorithm to count the number of particles within a region. Taking a specific monitoring point as an example, the three images identified 74, 78, and 76 particles respectively, and the average of 76 was taken as the particle count for that point. Dividing the particle count by the scanned area of ​​25 square millimeters yields a particle distribution density of 3.04 particles / square millimeter for that weaving location.

[0057] A stress dataset was constructed by extracting stress values ​​from all weaving locations. This dataset is stored as a two-dimensional array, with row indices representing vertical positions and column indices representing horizontal positions. Array elements represent the stress values ​​at the corresponding positions. A density dataset was also constructed by extracting the distribution density of antibacterial functional materials from all weaving locations, using the same storage structure as the stress dataset. Both datasets contain 225 data points distributed in a 45x5 matrix. To ensure data integrity, the collected data was synchronously uploaded to the central processing unit using real-time data transmission technology. The transmission protocol employed was the highly reliable industrial Ethernet protocol, with a packet error rate controlled within 10%. -9 the following.

[0058] The mean stress value was obtained by statistically analyzing the stress values ​​in the stress dataset and used as the stress baseline value. The statistical analysis used the arithmetic mean method, and the calculation formula was: Stress baseline value = ∑(stress values ​​at each weaving location) / total number of weaving locations. For the stress values ​​at 225 monitoring locations, the calculated stress baseline value was 14.96 N. The standard deviation was calculated to be 0.42 N, and the coefficient of variation was 2.81%. The mean distribution density of the antibacterial functional material in the density dataset was obtained by statistically analyzing the distribution density and used as the density baseline value. Using the same arithmetic mean method, the calculated density baseline value was 3.08 particles / mm², with a standard deviation of 0.31 particles / mm² and a coefficient of variation of 10.06%.

[0059] The calculation results of stress and density benchmark values ​​are recorded and saved through the production management system, serving as the basis for subsequent process parameter adjustments and quality control. The benchmark values ​​are updated once a roll of bandage is produced, and historical data is stored in the database for product quality traceability and process optimization.

[0060] In this embodiment, a digital characterization system for weaving stress and antibacterial material distribution was established. Through a high-precision sensor network and intelligent image recognition technology, comprehensive real-time monitoring of key bandage quality parameters was achieved. The acquisition of stress benchmark values ​​provides a quantitative basis for the uniformity of bandage elasticity, while density benchmark values ​​ensure the spatial uniformity of antibacterial function. This data-driven quality control method breaks through the traditional production model relying on experience-based judgment, significantly improving product consistency and reliability.

[0061] In one optional implementation, the deviation between the stress value and the stress reference value at each weaving position is extracted to form a stress deviation sequence, and the deviation between the distribution density and the density reference value at each weaving position is extracted to form a density deviation sequence. The correlation coefficient between the stress deviation sequence and the density deviation sequence is calculated, including: Collect stress values ​​and distribution density time series data at each weaving location, identify stress abrupt change points and density abrupt change points in the distribution density time series data, and generate a disturbance transmission sequence; The deviation between the stress value at each weaving position and the stress reference value is determined based on the disturbance transmission sequence to form a stress deviation sequence, and the deviation between the distribution density at each weaving position and the density reference value is determined to form a density deviation sequence. Wavelet transform is performed on the stress deviation sequence and density deviation sequence to obtain transform coefficients. The transform coefficients are reconstructed into a coupling spectrum, and the coupling component with the maximum information content is extracted from the coupling spectrum. The coupled components are mapped to the reconstruction space to obtain the reconstruction sequence, and the distribution entropy value of the reconstruction sequence is calculated as a dynamic correlation index. Adaptive classification is performed on the dynamic correlation index to obtain the classification center value. The classification center value is normalized to obtain the interval value. The interval value is used as the synchronization threshold to determine the deviation direction of the stress deviation sequence and the density deviation sequence at each weaving position. The ratio of the number of positions with the same deviation direction to the total number of positions is used as the correlation coefficient.

[0062] When collecting time-series data on stress values ​​and distribution density at each weaving location, the stress sensor and density detector synchronously acquire data at a frequency of 20 Hz. Data is continuously collected for 10 seconds at each weaving location, resulting in 200 stress and distribution density values ​​at various time points. The time-series data employs timestamp alignment technology to ensure the time synchronization of the two types of data, with synchronization accuracy controlled within 5 milliseconds. The time-series data is stored in a two-dimensional matrix, where rows represent time points, columns represent monitoring locations, and element values ​​are the stress or distribution density values ​​at the corresponding time point and location.

[0063] To identify stress and density abrupt change points in distribution density time-series data, a sliding window method combined with the CUSUM algorithm was used for abrupt change detection. The sliding window width was set to 25 time points, and the window sliding step size was 5 time points. A cumulative sum statistic was calculated for the data within each window; when the statistic exceeded a preset threshold, the point was marked as an abrupt change point. The abrupt change threshold for stress data was set to 3 times the standard deviation, and for density data, it was set to 2.5 times the standard deviation. For stress data at a certain weaving location, abrupt change points were detected at time points 78, 124, and 165; for density data at the same location, abrupt change points were detected at time points 83, 131, and 172. By comparing the temporal relationship between the two types of abrupt change points, a perturbation propagation sequence was formed. The perturbation propagation sequence recorded the time difference between stress and density abrupt changes, such as time points 5, 7, and 7, indicating that the density change caused by stress change has a delay of 5 to 7 time points, corresponding to an actual time of 250 to 350 milliseconds.

[0064] When determining the deviation of stress values ​​from the stress reference value at each weaving position based on the disturbance transmission sequence, the difference between the stress value and the stress reference value at each time point is calculated and divided by the stress reference value to obtain the normalized deviation. The deviation calculation formula is: Stress deviation = (Stress value - Stress reference value) / Stress reference value. Similarly, density deviation = (Density value - Density reference value) / Density reference value. Taking a certain weaving position at a certain time as an example, the measured stress value is 16.25 N, the stress reference value is 14.96 N, and the calculated stress deviation is 0.086; the measured density value is 2.85 particles / mm², the density reference value is 3.08 particles / mm², and the calculated density deviation is -0.075. The stress deviations of each weaving position at all time points are arranged in chronological order to form a stress deviation sequence, and the density deviation sequence is obtained similarly.

[0065] When performing wavelet transforms on the stress deviation and density deviation sequences to obtain transform coefficients, the Daubechies wavelet basis DB4 was selected for decomposition, with a decomposition level of 4. A wavelet transform was performed on the original sequence of length 200 to obtain a set of approximate coefficients and a set of detail coefficients. The transform coefficients of the stress deviation and density deviation sequences were combined to construct a joint representation matrix. Principal component analysis was applied to reduce the dimensionality of the joint representation matrix, retaining the principal components with a cumulative contribution rate of 95%, forming a coupling spectrum. For a typical medical elastic bandage weaving process, the coupling spectrum usually contains 3 to 5 main coupling components. The coupling component with the largest contribution rate was extracted from the coupling spectrum; this component typically contains more than 60% of the original information.

[0066] In the process of mapping the coupled components to the reconstruction space to obtain the reconstructed sequence, inverse wavelet transform is employed. First, the extracted coupled components are substituted into their corresponding positions in the original joint representation matrix, with the remaining positions filled with zeros. Then, inverse wavelet transform is performed to obtain the reconstructed stress-density joint sequence. To evaluate the complexity and correlation of the reconstructed sequence, its multi-scale distribution entropy is calculated. The multi-scale distribution entropy calculation uses an embedding dimension of 3, a similarity tolerance of 0.2 standard deviations, and scale factors ranging from 1 to 5. By calculating the average distribution entropy values ​​at five scales, the dynamic correlation index is obtained. For the reconstructed sequence at a certain weaving position, the calculated distribution entropies at the five scales are 0.83, 0.76, 0.71, 0.68, and 0.65, with an average of 0.726, which is used as the dynamic correlation index for that position.

[0067] When performing adaptive classification to obtain classification center values ​​for dynamic correlation indicators, a fuzzy C-means clustering algorithm is used. The initial number of categories is set to 3, the fuzziness parameter to 2, and the termination condition is that the change in the objective function value between adjacent iterations is less than 10^-6 or the number of iterations reaches 100. Clustering of the dynamic correlation indicators for all 225 weaving positions yields three classification center values ​​of 0.72, 0.85, and 0.93. The classification center values ​​are normalized, mapping the minimum value to 0 and the maximum value to 1, resulting in the interval [0, 0.62, 1]. Using 0.62 as the synchronization threshold, the deviation directions of the stress deviation sequence and the density deviation sequence at each weaving position are determined. The criterion for consistent deviation directions is that both sequences are simultaneously positive or simultaneously negative. The number of positions with consistent deviation directions among all 225 weaving positions is counted; for example, if 176 are found, the correlation coefficient is calculated as 176 / 225 = 0.782, indicating a strong positive correlation between stress change and density distribution.

[0068] The correlation coefficient may fluctuate across different batches of medical elastic bandages produced. A correlation coefficient below 0.7 indicates poor synchronization between stress distribution and antibacterial material distribution during weaving, requiring adjustment of weaving parameters. A correlation coefficient above 0.9 indicates high synchronization, with the weaving process at its optimal state. Continuous monitoring of the correlation coefficient's trend allows for timely detection of anomalies during production, preventing batch-to-batch product quality fluctuations. In actual production, adjusting parameters such as weaving tension and feeding speed to maintain the correlation coefficient within the range of 0.75 to 0.85 ensures uniform distribution of antibacterial function and stable elasticity in the medical elastic bandages.

[0069] In this embodiment, a dynamic correlation model between stress distribution and antibacterial functional material distribution was established to monitor the correlation of key quality parameters during the production process. Compared with traditional single-parameter monitoring methods, this correlation evaluation method based on multi-dimensional time-series data analysis can more comprehensively reflect the dynamic characteristics of the weaving process and the intrinsic correlation between parameters. Signal processing techniques combining wavelet transform and entropy analysis accurately capture minute fluctuations and nonlinear characteristics during the weaving process, providing a more precise quality early warning mechanism.

[0070] In one optional implementation, a correlation control strategy is determined based on a correlation coefficient, and the process parameters and antibacterial functional material dosage at the corresponding weaving position are adjusted according to the correlation control strategy to complete the intelligent production of the bandage, including: The correlation coefficients are scaled to obtain the regulation coefficient sequence. The amplitude distribution of the regulation coefficient sequence is calculated, and the amplitude is divided into the main effect coefficient and the secondary effect coefficient. The control feature vector is calculated based on the main effect coefficient and the secondary effect coefficient, and then converted into process control parameters and dosage control parameters. The proportional relationship between the weaving speed adjustment and the weaving tension adjustment is calculated based on the process control parameters, and the proportional relationship between the feeding rate adjustment and the feeding concentration adjustment is calculated based on the feeding control parameters. A process control matrix is ​​constructed based on the proportional relationship between the weaving speed adjustment and the weaving tension adjustment, and an application control matrix is ​​constructed based on the proportional relationship between the application rate adjustment and the application concentration adjustment. The process control matrix and the feed control matrix are combined to generate a linkage control sequence, and the control priority is determined based on the linkage control sequence. Adjust process parameters and the amount of antibacterial functional material added according to the control priority, and calculate the dynamic change range of stress value and distribution density of antibacterial functional material during the control process as the control convergence degree. When the control convergence meets the preset convergence threshold, the adjustment of process parameters and the amount of antibacterial functional material added is stopped, and the intelligent production of bandages is completed.

[0071] In this embodiment, a scaling transformation is first performed on the correlation coefficients to obtain a sequence of control coefficients. The correlation coefficient matrix between antibacterial properties and physical characteristics is then standardized using z-score normalization to ensure that the data distribution has a mean of 0 and a standard deviation of 1, thus obtaining the sequence of control coefficients. For example, the correlation coefficient of 0.82 between antibacterial rate and tensile strength is converted to a control coefficient of 0.67, and the correlation coefficient of 0.56 between antibacterial rate and air permeability is converted to a control coefficient of 0.35.

[0072] Next, the amplitude distribution of the control coefficient sequence is calculated, and the amplitudes are divided into main effect coefficients and secondary effect coefficients by setting thresholds. When the absolute value of the control coefficient is greater than 0.5, it is defined as a main effect coefficient; when it is less than or equal to 0.5, it is defined as a secondary effect coefficient. Taking typical parameters in the bandage weaving process as an example, the control coefficient for weaving speed and antibacterial properties is 0.67, which belongs to the main effect coefficient; the control coefficient for tension and breathability is 0.42, which belongs to the secondary effect coefficient.

[0073] The regulatory feature vector is calculated based on the main effect coefficient and the secondary effect coefficient. The main effect coefficient is multiplied by the weighting coefficient by 1.5, and the secondary effect coefficient is multiplied by the weighting coefficient by 0.8. Then, the dimensions are combined to form the regulatory feature vector. For example, for weaving position A, the regulatory feature vector is [1.01, 0.34, 0.76, 0.28], corresponding to the four dimensions of speed, tension, dispensing rate, and dispensing concentration, respectively.

[0074] The control feature vector is converted into process control parameters and dosing control parameters. The process control parameters are taken from the first two dimensions of the control feature vector, i.e., [1.01, 0.34], representing the control parameters of speed and tension; the dosing control parameters are taken from the last two dimensions, i.e., [0.76, 0.28], representing the control parameters of dosing rate and dosing concentration.

[0075] The ratio of knitting speed adjustment to knitting tension adjustment is calculated based on the process control parameters. The ratio K1 is equal to the speed control parameter divided by the tension control parameter, i.e., K1 = 1.01 / 0.34 = 2.97, indicating that the effect of speed adjustment is 2.97 times that of tension adjustment.

[0076] The ratio of the dosage rate adjustment to the dosage concentration adjustment is calculated based on the dosage control parameters. The ratio K2 is equal to the dosage rate control parameter divided by the dosage concentration control parameter, i.e., K2 = 0.76 / 0.28 = 2.71, indicating that the effect of dosage rate adjustment is 2.71 times that of dosage concentration adjustment.

[0077] A process control matrix is ​​constructed based on the proportional relationship between the knitting speed adjustment and the knitting tension adjustment. Let the knitting reference speed be V0, the reference tension be T0, and the adjustment coefficient be α. Then the control matrix is ​​[[V0(1+2.97α), T0(1+α)], [V0(1-2.97α), T0(1-α)]], representing four control combinations of speed and tension.

[0078] A dosing control matrix is ​​constructed based on the proportional relationship between the dosing rate adjustment and the dosing concentration adjustment. Let the baseline dosing rate of the antibacterial material be R0, the baseline concentration be C0, and the adjustment coefficient be β. Then the dosing control matrix is ​​[[R0(1+2.71β), C0(1+β)], [R0(1-2.71β), C0(1-β)]], representing four control schemes for dosing rate and concentration.

[0079] The process control matrix and the application control matrix are combined to generate a linkage control sequence. 4×4=16 control combinations are calculated using matrix multiplication, and these combinations are ranked according to a comprehensive score of expected antibacterial effect and physical performance to generate the linkage control sequence. For example, the first combination of the linkage control sequence is [V0(1+2.97α), T0(1+α), R0(1+2.71β), C0(1+β)], representing a control scheme that increases weaving speed, tension, application rate, and concentration.

[0080] The regulatory priority is determined based on the linkage regulation sequence. The linkage regulation sequence is sorted from high to low according to the comprehensive score to determine the regulatory priority. The score calculation considers two factors: improvement in antibacterial performance and balance of physical performance, with weights of 0.6 and 0.4, respectively. For example, for bandages used on sensitive wound sites, the linkage regulation priority is a combination of increased speed, increased dosage, increased concentration, and reduced tension.

[0081] The process parameters and the amount of antibacterial functional material added were adjusted according to the control priority. Starting with the highest priority control scheme, parameters such as weaving speed, tension, application rate, and concentration were gradually adjusted. After each adjustment, the changes in stress and the distribution density of the antibacterial functional material were calculated as indicators of control convergence. For example, when the weaving speed was initially increased from 2.5 m / min to 3.0 m / min and the tension from 180 N to 195 N, the stress change rate was monitored to be 8.7%, and the material distribution density change rate was 9.2%.

[0082] Adjustment stops when the convergence rate meets the preset convergence threshold. The convergence thresholds for both the stress change rate and the material density change rate are set at 3%. When the change rates are all less than this threshold after three consecutive adjustments, the control process is considered converged, and intelligent production of the bandage is complete. In actual production, typically 4-6 iterations are needed to achieve the optimal balance in the performance of the antibacterial bandage, ensuring both antibacterial efficacy and compliance with medical requirements for physical properties.

[0083] A second aspect of the present invention provides an intelligent production system for medical elastic bandages with antibacterial function, the system comprising: The first unit is used to determine the tensile resilience of elastic fiber raw materials and the surface active site density of antibacterial functional materials. The second unit is used to calculate the change in pore volume of elastic fibers based on tensile resilience, calculate the space occupied by antibacterial functional materials based on surface active site density, and determine the mass ratio and dispersion particle size range of antibacterial functional materials based on the geometric relationship between the change in pore volume and the space occupied, thereby obtaining the formulation parameters. The third unit is used to mix and weave antibacterial functional materials with elastic fiber raw materials according to the proportioning parameters. The fourth unit is used to collect stress values ​​and distribution density of antibacterial functional materials at each weaving position during the weaving process, calculate the average stress value as the stress reference value, and calculate the average distribution density as the density reference value. The fifth unit is used to extract the deviation of the stress value from the stress reference value at each weaving position to form a stress deviation sequence, extract the deviation of the distribution density from the density reference value at each weaving position to form a density deviation sequence, and calculate the correlation coefficient between the stress deviation sequence and the density deviation sequence. The sixth unit is used to determine the correlation control strategy based on the correlation coefficient, and adjust the process parameters and antibacterial functional material dosage at the corresponding weaving position according to the correlation control strategy to complete the intelligent production of bandages.

[0084] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0085] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0086] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent production method for medical elastic bandages with antibacterial function, characterized in that, include: The tensile resilience of elastic fiber raw materials and the surface active site density of antibacterial functional materials were determined. The change in pore volume of elastic fibers is calculated based on tensile resilience, and the space occupied by antibacterial functional materials is calculated based on surface active site density. The mass ratio and dispersion particle size range of antibacterial functional materials are determined based on the geometric relationship between the change in pore volume and the space occupied, thus obtaining the formulation parameters. The antibacterial functional material is mixed with elastic fiber raw material according to the proportion parameters and then woven. During the weaving process, stress values ​​and distribution density of antibacterial functional materials at each weaving position are collected. The average stress value is calculated as the stress reference value, and the average distribution density is calculated as the density reference value. The deviation of the stress values ​​at each weaving position from the stress reference value is extracted to form a stress deviation sequence, and the deviation of the distribution density at each weaving position from the density reference value is extracted to form a density deviation sequence. The correlation coefficient between the stress deviation sequence and the density deviation sequence is calculated. Based on the correlation coefficient, a correlation control strategy is determined. The process parameters and the amount of antibacterial functional material added at the corresponding weaving position are adjusted according to the correlation control strategy to complete the intelligent production of bandages.

2. The method according to claim 1, characterized in that, The determination of tensile resilience of elastic fiber raw materials and surface active site density of antibacterial functional materials includes: Cyclic tensile loads were applied to the elastic fiber material, and strain-time and stress-time curves of the elastic fiber material were collected. Identify the strain recovery start point and strain recovery end point of the strain-time curve, and calculate the time interval between the strain recovery start point and strain recovery end point as the strain recovery time; Identify the stress decay start point of the stress-time curve, extract the stress peak value corresponding to the stress decay start point and the residual stress value corresponding to the strain recovery termination point, and calculate the difference between the stress peak value and the residual stress value as the stress decay amplitude. The tensile resilience of elastic fiber raw materials is calculated based on strain recovery time and stress attenuation amplitude. Electron energy spectrum scanning is performed on the surface of antibacterial functional materials to obtain the distribution of elemental energy spectrum signal intensity, identify antibacterial characteristic elements, extract the spatial location coordinates of the energy spectrum signal intensity of antibacterial characteristic elements that exceed the preset intensity threshold, and mark the spatial location coordinates on the surface of antibacterial functional materials to form an elemental distribution map. The density of surface active sites of antibacterial functional materials is obtained by counting the total number of spatial coordinates in the distribution map of statistical elements, measuring the total area of ​​the surface of antibacterial functional materials, and calculating the ratio of the total number to the total area.

3. The method according to claim 1, characterized in that, The change in pore volume of the elastic fiber is calculated based on the tensile resilience. The space occupied by the antibacterial functional material is calculated based on the surface active site density. The mass ratio and dispersion particle size range of the antibacterial functional material are determined based on the geometric relationship between the change in pore volume and the space occupied volume, resulting in the following proportioning parameters: The fiber spacing expansion is determined based on the tensile resilience. The initial pore volume and target pore volume before and after tensile deformation are determined based on the fiber spacing expansion. The volume difference between the target pore volume and the initial pore volume is taken as the pore volume change of the elastic fiber. The size of a single particle of the antibacterial functional material is measured. The active coverage of a single particle is determined based on the density of surface active sites. The effective radius of action of a single particle is determined by combining the size of the single particle with the active coverage. The effective volume occupied by a single particle is determined based on the effective radius of action. The space occupied by the antibacterial functional material is determined based on the effective volume of action and the total number of particles to be added. By comparing the change in pore volume with the volume occupied in space, when the volume occupied in space is less than the change in pore volume, the effective radius of action is extracted as the dispersion particle size range of the antibacterial functional material. The mass ratio of the antibacterial functional material is determined based on the volume relationship between the change in pore volume and the volume occupied in space. The mass ratio and the dispersion particle size range are combined to form the proportioning parameters.

4. The method according to claim 1, characterized in that, According to the mixing ratio parameters, the antibacterial functional material is mixed with elastic fiber raw material and then woven, including: The antibacterial functional material particles are screened according to the dispersion particle size range in the proportioning parameters. The mass of the antibacterial functional material particles and the elastic fiber raw material are weighed according to the mass ratio in the proportioning parameters. The antibacterial functional material particles and the elastic fiber raw material are put into the mixing device for mixing to obtain the mixture. A tensile load is applied to the mixture to cause the elastic fiber raw material to undergo tensile deformation. The fiber spacing expansion under tensile deformation is measured. When the fiber spacing expansion reaches a preset expansion threshold, the antibacterial functional material particles are driven into the expanded pores of the elastic fiber raw material to form an embedded distribution state. The tensile load is released to restore the elastic fiber raw material to its initial state. The amount of fiber spacing shrinkage during the recovery process is monitored. When the amount of fiber spacing shrinkage reaches the preset shrinkage threshold, a locking force is applied to the antibacterial functional material particles through pore shrinkage to form a stable bond. The mixture that has formed a stable bond is then transported to the weaving device for weaving, thus completing the weaving of the antibacterial functional material and the elastic fiber raw material.

5. The method according to claim 1, characterized in that, During the weaving process, stress values ​​and the distribution density of the antibacterial functional material at each weaving location are collected. The average stress value is calculated as the stress reference value, and the average distribution density is calculated as the density reference value, including: Multiple monitoring points are set along the weaving direction as weaving positions during the weaving process; Stress sensors and density detectors are arranged at each weaving position. The stress sensors collect the stress values ​​of each weaving position under the action of weaving tension, and the density detectors scan the number of particles per unit area of ​​the antibacterial functional material at each weaving position. The number of particles per unit area is taken as the distribution density of the antibacterial functional material at each weaving position. Stress datasets were constructed by extracting stress values ​​from all weaving locations, and density datasets were constructed by extracting the distribution density of antibacterial functional materials from all weaving locations. The average stress value is obtained by statistically calculating the stress values ​​in the stress dataset and used as the stress benchmark value. Similarly, the average distribution density of the antibacterial functional material in the density dataset is obtained by statistically calculating the distribution density and used as the density benchmark value.

6. The method according to claim 1, characterized in that, The deviations of stress values ​​at each weaving location from the stress reference value are extracted to form a stress deviation sequence, and the deviations of distribution density at each weaving location from the density reference value are extracted to form a density deviation sequence. The correlation coefficients between the stress deviation sequence and the density deviation sequence are calculated, including: Collect stress values ​​and distribution density time series data at each weaving location, identify stress abrupt change points and density abrupt change points in the distribution density time series data, and generate a disturbance transmission sequence; The deviation between the stress value at each weaving position and the stress reference value is determined based on the disturbance transmission sequence to form a stress deviation sequence, and the deviation between the distribution density at each weaving position and the density reference value is determined to form a density deviation sequence. Wavelet transform is performed on the stress deviation sequence and density deviation sequence to obtain transform coefficients. The transform coefficients are reconstructed into a coupling spectrum, and the coupling component with the maximum information content is extracted from the coupling spectrum. The coupled components are mapped to the reconstruction space to obtain the reconstruction sequence, and the distribution entropy value of the reconstruction sequence is calculated as a dynamic correlation index. Adaptive classification is performed on the dynamic correlation index to obtain the classification center value. The classification center value is normalized to obtain the interval value. The interval value is used as the synchronization threshold to determine the deviation direction of the stress deviation sequence and the density deviation sequence at each weaving position. The ratio of the number of positions with the same deviation direction to the total number of positions is used as the correlation coefficient.

7. The method according to claim 1, characterized in that, Based on the correlation coefficient, a correlation control strategy is determined. Following this strategy, the process parameters and the amount of antibacterial functional material applied at the corresponding weaving positions are adjusted to achieve intelligent production of the bandages. The correlation coefficients are scaled to obtain the regulation coefficient sequence. The amplitude distribution of the regulation coefficient sequence is calculated, and the amplitude is divided into the main effect coefficient and the secondary effect coefficient. The control feature vector is calculated based on the main effect coefficient and the secondary effect coefficient, and then converted into process control parameters and dosage control parameters. The proportional relationship between the weaving speed adjustment and the weaving tension adjustment is calculated based on the process control parameters, and the proportional relationship between the feeding rate adjustment and the feeding concentration adjustment is calculated based on the feeding control parameters. A process control matrix is ​​constructed based on the proportional relationship between the weaving speed adjustment and the weaving tension adjustment, and an application control matrix is ​​constructed based on the proportional relationship between the application rate adjustment and the application concentration adjustment. The process control matrix and the feed control matrix are combined to generate a linkage control sequence, and the control priority is determined based on the linkage control sequence. Adjust process parameters and the amount of antibacterial functional material added according to the control priority, and calculate the dynamic change range of stress value and distribution density of antibacterial functional material during the control process as the control convergence degree. When the control convergence meets the preset convergence threshold, the adjustment of process parameters and the amount of antibacterial functional material added is stopped, and the intelligent production of bandages is completed.

8. An intelligent production system for medical elastic bandages with antibacterial function, used to implement the method of any one of claims 1-7, characterized in that, include: The first unit is used to determine the tensile resilience of elastic fiber raw materials and the surface active site density of antibacterial functional materials. The second unit is used to calculate the change in pore volume of elastic fibers based on tensile resilience, calculate the space occupied by antibacterial functional materials based on surface active site density, and determine the mass ratio and dispersion particle size range of antibacterial functional materials based on the geometric relationship between the change in pore volume and the space occupied, thereby obtaining the formulation parameters. The third unit is used to mix and weave antibacterial functional materials with elastic fiber raw materials according to the proportioning parameters. The fourth unit is used to collect stress values ​​and distribution density of antibacterial functional materials at each weaving position during the weaving process, calculate the average stress value as the stress reference value, and calculate the average distribution density as the density reference value. The fifth unit is used to extract the deviation of the stress value from the stress reference value at each weaving position to form a stress deviation sequence, extract the deviation of the distribution density from the density reference value at each weaving position to form a density deviation sequence, and calculate the correlation coefficient between the stress deviation sequence and the density deviation sequence. The sixth unit is used to determine the correlation control strategy based on the correlation coefficient, and adjust the process parameters and antibacterial functional material dosage at the corresponding weaving position according to the correlation control strategy to complete the intelligent production of bandages.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

Images