Biological material disinfection method, system and apparatus based on big data analysis

Abstract

The application discloses a biological material disinfection method, system and equipment based on big data analysis, and relates to the technical field of disinfection. The method comprises the following steps: determining a target disinfectant; performing Micro-CT scanning on the biological material to construct a 3D digital model; reading physical attribute information of the target disinfectant and an interaction relationship between the target disinfectant and the biological material; simulating the space-time dynamic distribution of the flow, diffusion and penetration of the target disinfectant in the internal pore channel of the material on the basis of the 3D digital model; optimizing the flow field and concentration field of the internal disinfection blind area through pulse pressure circulation; and generating target disinfection parameters to control the disinfection of the biological material. The technical problems that the adaptability of the disinfectant and the biological material is insufficient in the prior art, resulting in incomplete disinfection and material structure damage are solved, the adaptability between the disinfectant and the biological material is achieved, the internal pore channel of the material is fully disinfected, and the technical effect of effectively reducing the material damage risk is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of disinfection, in particular to a biological material disinfection method, system and equipment based on big data analysis. BACKGROUND

[0002] Biological materials are widely used in medical, food, environmental and other fields, and their use process usually needs to be subjected to strict disinfection treatment to ensure safety and functionality. However, the existing disinfection methods mostly rely on the experience to select disinfectants, lack of adaptability analysis for different material properties, and are easy to cause incomplete disinfection or performance decline or even structure damage of the materials due to excessive disinfectant effect. Especially for biological materials with complex pore channels or porous structure, the traditional method is difficult to realize sufficient penetration and uniform coverage of the internal area, resulting in potential disinfection blind area, thereby reducing the reliability and stability of the overall disinfection. SUMMARY

[0003] The present application provides a biological material disinfection method, system and equipment based on big data analysis, which solves the technical problems of insufficient adaptability of disinfectants to biological materials in the prior art, resulting in incomplete disinfection and material structure damage.

[0004] In a first aspect, the present application provides a biological material disinfection method based on big data analysis, which comprises:

[0005] Collecting material properties of biological materials to be disinfected, combining a disinfectant selection library with a pre-constructed material damage database to perform material-disinfection damage analysis and determine a target disinfectant; performing Micro-CT scanning on the biological materials to construct a 3D digital model; reading physical property information of the target disinfectant and interaction relationship between the target disinfectant and the biological materials, simulating spatiotemporal dynamic distribution of flow, diffusion and penetration of the target disinfectant in the internal pore channels of the materials based on the 3D digital model, optimizing flow field and concentration field of internal disinfection blind area through pulse pressure circulation, and generating target disinfection parameters to control disinfection of the biological materials.

[0006] In a second aspect, the present application provides a biological material disinfection system based on big data analysis, which comprises:

[0007] The analysis module collects material properties of the biological material to be disinfected, performs material-disinfection damage analysis in combination with a disinfectant selection library and a pre-constructed material damage database, and determines a target disinfectant; the model construction module performs Micro-CT scanning on the biological material to construct a 3D digital model; the disinfection control module reads physical property information of the target disinfectant and interaction relationship between the target disinfectant and the biological material, simulates spatiotemporal dynamic distribution of flow, diffusion and penetration of the target disinfectant in internal pores of the material based on the 3D digital model, optimizes flow field and concentration field of internal disinfection blind areas through pulse pressure circulation, and generates target disinfection parameters to perform disinfection control on the biological material.

[0008] In a third aspect, the present application provides an electronic device, comprising: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the biological material disinfection method based on big data analysis provided by the present application.

[0009] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0010] First, the material properties of the biological material to be disinfected are collected, and material-disinfection damage analysis is performed in combination with a disinfectant selection library and a pre-constructed material damage database to determine a target disinfectant. Second, Micro-CT scanning is performed on the biological material to construct a 3D digital model. Finally, the physical property information of the target disinfectant and the interaction relationship between the target disinfectant and the biological material are read, spatiotemporal dynamic distribution of flow, diffusion and penetration of the target disinfectant in internal pores of the material is simulated based on the 3D digital model, flow field and concentration field of internal disinfection blind areas are optimized through pulse pressure circulation, and target disinfection parameters are generated to perform disinfection control on the biological material. The technical problem of insufficient adaptability of disinfectants to biological materials in the prior art, resulting in incomplete disinfection and material structure damage, is solved, the adaptability between disinfectants and biological materials is achieved, sufficient disinfection of internal pores of the material is implemented, and the technical effect of effectively reducing the risk of material damage is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0012] Figure 1 The biological material disinfection method based on big data analysis provided in the embodiments of the present application is shown in the flowchart.

[0013] Figure 2 A structure schematic diagram of a biological material disinfection system based on big data analysis is provided for the embodiments of the present application.

[0014] Figure 3 A structure schematic diagram of an exemplary electronic device is provided for the embodiments of the present application.

[0015] Legend: analysis module 11, model construction module 12, disinfection control module 13, processor 21, memory 22, input device 23, output device 24. DETAILED DESCRIPTION

[0016] The present application provides a biological material disinfection method, system and device based on big data analysis, which solves the technical problem of insufficient adaptability of disinfectant to biological material in the prior art, resulting in incomplete disinfection and damage to material structure.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0018] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server comprising a series of steps or units does not have to be limited to those clearly listed steps or units, but can include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] Embodiment one, as shown in the present application, provides a biological material disinfection method based on big data analysis, wherein the method comprises: Figure 1

[0020] Collecting material properties of biological material to be disinfected, combining disinfectant selection library and pre-constructed material damage database for material-disinfection damage analysis to determine target disinfectant.

[0021] ​In the embodiments of the present application, the material properties of the biological material to be disinfected are collected, including but not limited to molecular weight distribution, mechanical strength, surface chemical composition, porosity, specific surface area, and biological activity factor activity, etc. The material properties are used as retrieval constraints to call a pre-established disinfectant selection library and a material damage database constructed by combining historical disinfection data, to comprehensively compare and analyze the sterilization efficiency and material damage degree of different disinfectants acting on the biological material. The material damage database is constructed by collecting a large amount of performance change data of biological materials under different disinfectants, different concentrations, different action times and different processing temperatures, and the performance change data at least includes mechanical property decline amplitude, chemical group degradation degree, cytotoxicity change and structure integrity retention rate. Further, a machine learning method based on random forest is used to train the historical disinfection data to establish a material-disinfection efficiency-damage comprehensive evaluation model, which can output the sterilization efficiency and damage risk score of different disinfectant and parameter combinations under the given material property constraints. Finally, according to the calculation results of the comprehensive evaluation model, the disinfectant that achieves the optimal balance between sterilization efficiency and material safety is selected as the target disinfectant.

[0022] Further, the material properties of the biological material to be disinfected are collected, and the material-disinfection damage analysis is performed in combination with the disinfectant selection library and the pre-constructed material damage database to determine the target disinfectant, including:

[0023] The historical disinfection data is collected with the material properties as retrieval constraints, the performance change data of the biological material is analyzed, the material damage database is established, the material-disinfection efficiency-damage comprehensive evaluation model is established based on random forest and using the material damage database, and the disinfection condition that achieves the optimal balance between sterilization efficiency and material safety is screened out according to the material-disinfection efficiency-damage comprehensive evaluation model to generate the target disinfectant.

[0024] Firstly, historical disinfection data related to the biological material is collected from existing experimental records and literature data, with the material properties as the retrieval constraint, wherein the historical disinfection data covers experimental results under different disinfectant types, different action concentrations, different processing times and processing temperatures; on this basis, performance change data of the biological material under the above various disinfection conditions is analyzed, the performance change data including but not limited to molecular weight distribution change, mechanical strength decline ratio, surface chemical group degradation rate, in vitro cytotoxicity increase amplitude and biological activity factor activity retention rate, and a material damage database is established accordingly. Subsequently, the material damage database is trained and modeled based on a random forest algorithm, to obtain a material-disinfection efficiency-damage comprehensive evaluation model, which can output corresponding sterilization efficiency score and damage risk score after inputting specific material properties and disinfection condition parameters. Finally, the material-disinfection efficiency-damage comprehensive evaluation model is called to perform batch prediction and screening on candidate disinfectants and their parameter combinations, and the optimal disinfection condition is determined according to the weighted balance result between sterilization efficiency and material safety, so as to generate the target disinfectant as the input of subsequent disinfection control.

[0025] Further, the performance change data includes the change characteristics of key performance indicators under various disinfection conditions, wherein the key performance indicators at least include molecular weight distribution, mechanical strength, surface chemistry, in vitro cytotoxicity and biological activity factor activity.

[0026] In the process of establishing the material damage database, the performance change data not only records the overall state of the biological material before and after disinfection, but also specifically covers the change characteristics of key performance indicators under different disinfection conditions. Specifically: molecular weight distribution, which represents the degradation of polymer chain segments under the action of disinfection through gel permeation chromatography and other means; mechanical strength, including tensile strength, compressive strength and elongation at break, which is used to reflect the change of material bearing capacity and toughness before and after disinfection; surface chemistry, which detects the change of surface functional groups through infrared spectroscopy, X-ray photoelectron spectroscopy and other methods to reflect the degree of bond rupture or recombination; in vitro cytotoxicity, which evaluates the influence of the material on cell survival rate and proliferation capacity after disinfection treatment by using standard cell viability determination method; biological activity factor activity, which is used to reflect the activity level of biological functional molecules (such as proteins, polypeptides or polysaccharides, etc.) retained by the material after disinfection process. Through systematic recording and quantitative analysis of the above key performance indicators, the influence of disinfection conditions on material performance can be fully characterized, providing reliable input features for the material-disinfection efficiency-damage comprehensive evaluation model.

[0027] The biological material is subjected to Micro-CT scanning to construct a 3D digital model.

[0028] Further, the biological material is subjected to Micro-CT scanning to construct a 3D digital model, including:

[0029] The scanning sample of the biological material is fixed and packaged, and the sample is continuously scanned in a 360° range according to preset scanning parameters to obtain a plurality of 2D projection images; image reconstruction is performed based on the plurality of 2D projection images, including beam hardening correction and ring artifact correction, and three-dimensional volume data is output, wherein the three-dimensional volume data is stacked by continuous 2D grayscale slices; three-dimensional volume rendering and region segmentation are performed based on the three-dimensional volume data to generate the 3D digital model, and the 3D digital model is identified with porosity, pore size distribution, structure model thickness distribution, tortuosity and specific surface area.

[0030] Specifically, first, the scanning sample of the biological material is fixed and packaged to avoid displacement or deformation during scanning, thereby ensuring the stability and accuracy of imaging; then, the sample is continuously scanned in a 360° range according to preset scanning voltage, current, resolution and rotation step parameters, and a plurality of 2D projection images covering the entire sample structure are gradually obtained; based on the 2D projection images, image reconstruction processing is performed using a computer tomographic reconstruction algorithm, which includes beam hardening correction and ring artifact correction of the projection data to eliminate distortion caused by uneven X-ray energy distribution or detector defects. The three-dimensional volume data output after the above processing is stacked by continuous 2D grayscale slices, which can accurately reflect the internal structure characteristics of the sample at the micron scale. After obtaining the three-dimensional volume data, further three-dimensional volume rendering and region segmentation operations are performed to extract the geometric and pore information of the material, and finally a 3D digital model is generated, which can intuitively represent the key parameters of the biological material such as porosity, pore size distribution, structure model thickness distribution, tortuosity and specific surface area, providing accurate structural basis for subsequent multi-physical field simulation of disinfectant flow, diffusion and penetration in digital space.

[0031] Further, the image reconstruction based on the plurality of 2D projection images includes beam hardening correction and ring artifact correction, and outputs three-dimensional volume data, including:

[0032] The same row of pixels in the plurality of 2D projection images is extracted and arranged in angle order to form a 2D image, forming a sinogram; after beam hardening correction and ring artifact correction of the sinogram, filter back projection is performed to generate continuous 2D grayscale slices, wherein the beam hardening correction maps the projection value through a predefined linear function, and the ring artifact correction performs transverse filtering on the sinogram; the continuous 2D grayscale slices are stacked to form the 3D digital model.

[0033] When reconstructing an image from several 2D projected images, the projection data first needs to be geometrically processed. This involves extracting pixels from the same row from several 2D projected images and arranging them sequentially according to the projection angle, thus constructing a 2D image with pixel grayscale values ​​as elements, called a sine curve. Next, distortion correction is performed on the sine curve. Beam hardening correction remaps the projection values ​​using a predefined linear function to compensate for the nonlinear attenuation effect caused by differences in X-ray energy spectrum composition. Ring artifact correction uses lateral filtering to smooth the sine curve, eliminating ring-shaped stripe artifacts caused by uneven detector pixel response or mechanical rotation errors. After these corrections, a filtered back-projection algorithm is applied to the sine curve to convert the projection signal into a spatial grayscale distribution, generating continuous 2D grayscale slices layer by layer. Finally, the continuous 2D grayscale slices are stacked in spatial order to obtain volume data that characterizes the three-dimensional internal structure of the material, and a complete 3D digital model is further constructed.

[0034] The physical properties of the target disinfectant and its interaction with the biological material are read. Based on the 3D digital model, the spatiotemporal dynamic distribution of the target disinfectant's flow, diffusion, and penetration in the material's internal pores is simulated. The flow field and concentration field of the internal disinfection blind zone are optimized through pulsed pressure cycling, and target disinfection parameters are generated to control the disinfection of the biological material.

[0035] In this embodiment, the physical properties of the target disinfectant, including parameters such as density, viscosity, diffusion coefficient, and surface tension, are read. Simultaneously, the interaction between the target disinfectant and the surface of the biomaterial is obtained. This interaction includes adsorption rate constants and desorption rate constants, used to characterize the kinetics of the adsorption and desorption processes of disinfectant molecules on the material surface. Subsequently, using the aforementioned 3D digital model based on Micro-CT as the computational domain, a multiphysics simulation module is invoked to comprehensively couple fluid dynamics and rare-mass transport models to simulate the flow, diffusion, and penetration processes of the target disinfectant within the complex porous network of the material, obtaining the concentration and velocity distributions at different spatiotemporal points.

[0036] In the simulation process, a pulsed pressure cycle control strategy is introduced to change the local flow field structure and mass transfer path by periodically applying peak pressure and valley pressure, thereby promoting the convection and diffusion of the disinfectant in the blind area. Specifically, in each pulse cycle, the pressure waveform parameters are dynamically adjusted according to the simulation results, including peak pressure, valley pressure, pulse period and duty cycle, to improve the concentration-time integral value of the disinfectant in the disinfection blind area. Through multiple rounds of iterative optimization, the effective concentration distribution in the blind area reaches the preset sterilization threshold. Finally, the combination of pulse pressure waveform parameters corresponding to the optimization convergence is determined as the target disinfection parameter, and the target disinfection parameter is applied to the actual disinfection control process to achieve comprehensive disinfection of the biological material.

[0037] Further, based on the 3D digital model, the spatiotemporal dynamic distribution of the flow, diffusion and penetration of the target disinfectant in the internal channels of the material is simulated, and the internal disinfection blind area is optimized in terms of flow field and concentration field through pulsed pressure cycle, including:

[0038] Taking the 3D digital model as the calculation domain, the physical property information and the interaction relationship are taken as input parameters, a preliminary simulation state is established by coupling the laminar flow and the rare substance transfer physical field interface; step A: based on the preliminary simulation state, the flow, diffusion and penetration process of the target disinfectant in the internal channels of the material under the applied initial pulse pressure waveform is simulated, the disinfectant concentration distribution at different spatiotemporal points in the entire calculation domain is output, and a first spatiotemporal dynamic distribution map is obtained; step B: based on the first spatiotemporal dynamic distribution map, a first internal blind area where the disinfectant concentration is continuously below the effective sterilization threshold for a preset time is identified, and a first concentration-time integral value of the disinfectant in the first internal blind area is calculated; the parameters of the pulse pressure waveform are adjusted to improve the first concentration-time integral value as the optimization target, and steps A to B are repeatedly executed until the concentration-time integral value in the first internal blind area reaches the preset threshold, and the pulse pressure parameter combination corresponding to this time is determined as the target disinfection parameter.

[0039] Further, the interaction relationship includes the adsorption / desorption rate constant of the adsorption behavior of the target disinfectant on the surface of the biological material.

[0040] Further, the parameters of the pulse pressure waveform include peak pressure, valley pressure, pulse period and duty cycle.

[0041] In the simulation based on the 3D digital model, the 3D digital model can be taken as a calculation domain, and the physical property information (including density, viscosity, diffusion coefficient, etc.) of the target disinfectant and the interaction relationship with the biological material surface (including adsorption rate constant and desorption rate constant) are taken as input parameters, a preliminary simulation state is established by coupling a laminar flow module and a rare substance transfer module, and thus a basic flow and diffusion field distribution of the disinfectant in the pore structure is obtained.

[0042] Based on the preliminary simulation state, an initial pulse pressure waveform (including peak pressure, valley pressure, pulse period and duty cycle) is set, a multi-physical field coupling simulation is performed on the flow, diffusion and penetration process of the target disinfectant in the material internal pore, a disinfectant concentration distribution of each space-time point in the entire calculation domain is output, and a first space-time dynamic distribution map is obtained. Based on the first space-time dynamic distribution map, an area with a concentration continuously lower than an effective sterilization threshold within a preset time length is identified, defined as a first internal disinfection blind area, and an integral result of the concentration with respect to time of all grid nodes in the blind area is counted, and a first concentration-time integral value is obtained.

[0043] In step B, the concentration-time integral value is introduced as an evaluation index of disinfection effectiveness when the first internal disinfection blind area is quantitatively analyzed. Specifically, the concentration-time integral value of any grid node i in the blind area can be expressed as: , wherein, represents the disinfectant concentration of node i at time t, and T is a preset evaluation time length. The integral values of all n nodes in the blind area are averaged to obtain the first concentration-time integral value of the blind area: .

[0044] Taking the improvement of the first concentration-time integral value as an optimization objective, an iterative optimization method is used to dynamically adjust the parameter combination of the pulse pressure waveform, and steps A to B are re-executed until the calculation result shows that the concentration-time integral value in the blind area reaches or exceeds a preset threshold, indicating that the local area has met the effective disinfection condition. At this time, the corresponding pulse pressure parameter combination is determined as the target disinfection parameter. The target disinfection parameter can be directly used in the actual disinfection control process to ensure that the disinfectant is uniformly distributed and effectively covers the blind area in the material pore, thereby realizing complete disinfection.

[0045] In summary, the embodiments of the present application have at least the following technical effects:

[0046] First, the material properties of the biological material to be disinfected are collected. Material-disinfection damage analysis is then performed using a disinfectant selection library and a pre-constructed material damage database to determine the target disinfectant. Second, Micro-CT scans are performed on the biological material to construct a 3D digital model. Finally, the physical properties of the target disinfectant and its interaction with the biological material are read. Based on the 3D digital model, the spatiotemporal dynamic distribution of the target disinfectant's flow, diffusion, and penetration within the material's internal pores is simulated. Pulsed pressure cycling is used to optimize the flow field and concentration field in the internal disinfection blind zone, generating target disinfection parameters for disinfection control of the biological material. This method solves the technical problem of insufficient compatibility between disinfectants and biological materials in existing technologies, leading to incomplete disinfection and material structural damage. It achieves compatibility between disinfectants and biological materials, ensuring thorough disinfection of the material's internal pores while effectively reducing the risk of material damage.

[0047] Example 2, based on the same inventive concept as the biological material disinfection method based on big data analysis in the previous examples, such as... Figure 2 As shown, this application provides a biomaterial disinfection system based on big data analysis, wherein the system includes:

[0048] Analysis Module 11: Collects the material properties of the biological material to be disinfected, and performs material-disinfection damage analysis by combining the disinfectant selection library and the pre-constructed material damage database to determine the target disinfectant; Model Building Module 12: Performs Micro-CT scanning on the biological material to construct a 3D digital model; Disinfection Control Module 13: Reads the physical property information of the target disinfectant and the interaction between the target disinfectant and the biological material, and simulates the spatiotemporal dynamic distribution of the flow, diffusion, and penetration of the target disinfectant in the internal pores of the material based on the 3D digital model. It optimizes the flow field and concentration field of the internal disinfection blind zone through pulsed pressure cycling, and generates target disinfection parameters to control the disinfection of the biological material.

[0049] Furthermore, the analysis module 11 is used to perform the following methods:

[0050] Using the material properties as search constraints, historical disinfection data is collected, and the performance change data of the biomaterials under various disinfection conditions are analyzed to establish a material damage database. Based on random forest, the material damage database is used to establish a comprehensive evaluation model of material-disinfection efficiency-damage. According to the comprehensive evaluation model of material-disinfection efficiency-damage, the disinfection conditions that achieve the optimal balance between sterilization efficiency and material safety are screened to generate the target disinfectant.

[0051] Furthermore, the analysis module 11 is used to perform the following methods:

[0052] The performance change data includes the change characteristics of key performance indicators under various disinfection conditions, wherein the key performance indicators at least include molecular weight distribution, mechanical strength, surface chemistry, in vitro cytotoxicity, and bioactive factor activity.

[0053] Further, the disinfection control module 13 is configured to perform the following method:

[0054] Taking the 3D digital model as a calculation domain, taking the physical property information and the interaction relationship as input parameters, and through a coupling of a laminar flow and a rare substance transfer physical field interface, a preliminary simulation state is established; step A: based on the preliminary simulation state, a flow, diffusion, and penetration process of the target disinfectant in the internal pore of the material under an applied initial pulse pressure waveform is simulated, a concentration distribution of the disinfectant at different space-time points in the entire calculation domain is output, and a first space-time dynamic distribution map is obtained; step B: based on the first space-time dynamic distribution map, a first internal blind area in which the disinfectant concentration is continuously lower than an effective sterilization threshold for a preset time length is identified, and a first concentration-time integral value of the disinfectant in the first internal blind area is calculated; taking an increase of the first concentration-time integral value as an optimization target, parameters of the pulse pressure waveform are adjusted, and steps A to B are repeatedly executed until the concentration-time integral value in the first internal blind area reaches a preset threshold, and a corresponding pulse pressure parameter combination at this time is determined as the target disinfection parameter.

[0055] Further, the disinfection control module 13 is configured to perform the following method:

[0056] The interaction relationship includes an adsorption / desorption rate constant of an adsorption behavior of the target disinfectant on the surface of the biological material.

[0057] Further, the disinfection control module 13 is configured to perform the following method:

[0058] The parameters of the pulse pressure waveform include a peak pressure, a valley pressure, a pulse period, and a duty cycle.

[0059] Further, the model construction module 12 is configured to perform the following method:

[0060] The scanned sample of the biological material is fixed and packaged, the sample is continuously Micro-CT scanned in a 360° range according to preset scanning parameters, and a plurality of 2D projection images are obtained; image reconstruction is performed based on the plurality of 2D projection images, including beam hardening correction and ring artifact correction, and three-dimensional volume data is output, wherein the three-dimensional volume data is stacked by continuous 2D grayscale slices; three-dimensional volume rendering and region segmentation are performed based on the three-dimensional volume data, and the 3D digital model is generated, and the 3D digital model is marked with porosity, pore size distribution, structure model thickness distribution, tortuosity, and specific surface area.

[0061] Further, the model construction module 12 is configured to perform the following method:

[0062] A 2D image is formed by extracting the same row of pixels in the several 2D projection images and arranging them in order of angle. After beam hardening correction and annular artifact correction, the 2D image is filtered back projection to generate continuous 2D grayscale slices, wherein the beam hardening correction is performed by mapping the projection value with a predefined linear function, and the annular artifact correction is performed by transverse filtering of the 2D image. The 3D digital model is formed by stacking the continuous 2D grayscale slices.

[0063] Embodiment three, Figure 3 The structural schematic diagram of the electronic device provided for the third embodiment of the present application shows a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present application. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functions and use range of the embodiments of the present application. As Figure 3 As shown, the electronic device includes a processor 21, a memory 22, an input device 23, and an output device 24; the number of processors 21 in the electronic device can be one or more, Figure 3 In the example, the processor 21 in the electronic device, the memory 22, the input device 23, and the output device 24 can be connected through a bus or other means, Figure 3 In the example, the connection is through a bus.

[0064] The memory 22, as a computer readable storage medium, can be used to store software programs, computer executable programs, and modules, such as program instructions / modules corresponding to the biological material disinfection method based on big data analysis in the embodiments of the present application. The processor 21 performs various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 22, i.e., implements the above-mentioned biological material disinfection method based on big data analysis.

[0065] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above has described a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0066] The above is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0067] The specification and drawings are, of course, to be regarded in an illustrative rather than a restrictive sense. It is to be understood that any such modifications, variations, combinations or equivalents that fall within the scope of the application are intended to be embraced herein.

Claims

1. A method for disinfecting biological materials based on big data analysis, characterized in that, The method includes: Material properties of the biological materials to be disinfected are collected, and material-disinfection damage analysis is performed by combining a disinfectant selection library with a pre-constructed material damage database to determine the target disinfectant, specifically including: Using the material properties as search constraints, historical disinfection data is collected, and the performance change data of the biomaterial under various disinfection conditions is analyzed to establish a material damage database. The performance change data includes the change characteristics of key performance indicators under various disinfection conditions. The key performance indicators include at least molecular weight distribution, mechanical strength, surface chemistry, in vitro cytotoxicity, and bioactive factor activity. Based on random forest and using the aforementioned material damage database, a comprehensive evaluation model of material-disinfection efficiency-damage is established. Based on the material-disinfection efficiency-damage comprehensive evaluation model, the disinfection conditions that achieve the optimal balance between sterilization efficiency and material safety are screened out, and the target disinfectant is generated. Micro-CT scans were performed on biological materials to construct 3D digital models; The process involves reading the physical properties of the target disinfectant and its interaction with the biomaterial. Based on the 3D digital model, the spatiotemporal dynamic distribution of the target disinfectant's flow, diffusion, and penetration within the material's internal pores is simulated. Flow and concentration fields in the internal disinfection blind zone are optimized using pulsed pressure cycling to generate target disinfection parameters for disinfection control of the biomaterial. Specifically, this includes: Using the 3D digital model as the computational domain, the physical property information and the interaction relationship are used as input parameters. A preliminary simulation state is established by coupling the physical field interface between laminar flow and rare matter. Step A: Based on the preliminary simulation state, simulate the flow, diffusion, and penetration process of the target disinfectant in the internal pores of the material under the applied initial pulse pressure waveform, and output the disinfectant concentration distribution at different spatiotemporal points in the entire computational domain to obtain the first spatiotemporal dynamic distribution map; Step B: Based on the first spatiotemporal dynamic distribution map, identify the first internal blind zone where the disinfectant concentration is continuously lower than the effective sterilization threshold for a preset duration, and calculate the first concentration-time integral value of the disinfectant in the first internal blind zone; With the goal of increasing the first concentration-time integral value, the parameters of the pulse pressure waveform are adjusted, and steps A to B are repeated until the concentration-time integral value in the first internal blind zone reaches a preset threshold. The pulse pressure parameter combination corresponding to this point is then determined as the target disinfection parameter.

2. The method for disinfecting biological materials based on big data analysis as described in claim 1, characterized in that, The interaction relationship includes the adsorption / desorption rate constant of the target disinfectant on the surface of the biomaterial.

3. The method for disinfecting biological materials based on big data analysis as described in claim 2, characterized in that, The parameters of the pulse pressure waveform include peak pressure, valley pressure, pulse period, and duty cycle.

4. The method for disinfecting biological materials based on big data analysis as described in claim 1, characterized in that, Micro-CT scans of biological materials were performed to construct 3D digital models, including: The biomaterial scanning sample was fixed and encapsulated, and the sample was continuously scanned in a 360° range according to preset scanning parameters to obtain several 2D projection images. Image reconstruction is performed based on the aforementioned 2D projection images, including beam hardening correction and ring artifact correction, and three-dimensional volume data is output, wherein the three-dimensional volume data is composed of continuous 2D grayscale slices stacked together. Based on the three-dimensional volume data, three-dimensional volume rendering and region segmentation are performed to generate the 3D digital model. The 3D digital model is identified by porosity, pore size distribution, structural model thickness distribution, tortuosity, and specific surface area.

5. The method for disinfecting biological materials based on big data analysis as described in claim 4, characterized in that, Image reconstruction is performed based on the aforementioned 2D projection images, including beam hardening correction and ring artifact correction, and three-dimensional volume data is output, including: The pixels in the same row of the several 2D projection images are extracted and arranged in angular order to form a 2D image, thus forming a sine graph; After performing beam hardening correction and ring artifact correction on the sine graph, a filtered back projection is performed to generate a continuous 2D grayscale slice. The beam hardening correction maps the projection values ​​through a predefined linear function, and the ring artifact correction performs lateral filtering on the sine graph. The 3D digital model is formed by stacking the continuous 2D grayscale slices.

6. A biomaterial disinfection system based on big data analysis, characterized in that, The system is used to implement the biomaterial disinfection method based on big data analysis as described in any one of claims 1-5, the system comprising: Analysis module: Collects the material properties of the biological materials to be disinfected, combines the disinfectant selection library with the pre-constructed material damage database to perform material-disinfection damage analysis, and determines the target disinfectant; Model building module: Performs Micro-CT scans on biological materials to construct 3D digital models; Disinfection control module: Reads the physical properties of the target disinfectant and the interaction between the target disinfectant and the biological material. Based on the 3D digital model, it simulates the spatiotemporal dynamic distribution of the target disinfectant's flow, diffusion, and penetration in the material's internal pores. It optimizes the flow field and concentration field of the internal disinfection blind zone through pulsed pressure cycling and generates target disinfection parameters to control the disinfection of the biological material.

7. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the biological material disinfection method based on big data analysis as described in any one of claims 1-5.

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