Big data analysis-based biological material disinfection method, system and equipment

Abstract

The invention 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; the method comprises the following steps: performing Micro-CT scanning on a biological material to construct a 3D digital model; physical attribute information of a target disinfectant and an interaction relation between the target disinfectant and the biological material are read, spatial-temporal dynamic distribution of flowing, diffusion and permeation of the target disinfectant in pore channels in the material is simulated on the basis of the 3D digital model, and flow field and concentration field optimization is carried out on internal disinfection blind areas through pulse type pressure circulation; and generating a target disinfection parameter to perform disinfection control on the biological material. The technical problems that in the prior art, disinfection is not thorough and the material structure is damaged due to insufficient adaptability of a disinfectant and a biological material are solved, and the technical effects that the adaptability between the disinfectant and the biological material is achieved, pore channels in the material are fully disinfected, and meanwhile the damage risk of the material is effectively reduced are achieved.

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 cause 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: 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.

[0005] In a second aspect, the present application provides a biological material disinfection system based on big data analysis, which comprises: 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 control disinfection of the biological material.

[0006] 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.

[0007] The one or more technical solutions provided in the present application have at least the following technical effects or advantages: 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 control disinfection of 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 realized, and the technical effect of effectively reducing the risk of material damage is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. 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 labor.

[0009] Figure 1 A biological material disinfection method flowchart based on big data analysis provided by the embodiments of the present application is shown in the following figure: Figure 2A structure schematic diagram of a biological material disinfection system based on big data analysis is provided in the embodiments of the present application. Figure 3 A structure schematic diagram of an exemplary electronic device is provided in the embodiments of the present application.

[0010] Label explanation: analysis module 11, model construction module 12, disinfection control module 13, processor 21, memory 22, input device 23, output device 24. DETAILED DESCRIPTION

[0011] The present application provides a biological material disinfection method, system and device based on big data analysis, which solves the technical problem of incomplete disinfection and material structure damage caused by insufficient adaptability of disinfectant and biological material in the prior art.

[0012] 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.

[0013] It should be noted that the terms "comprise" and "have" 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 the process, method, product or device.

[0014] Embodiment one, as shown in the present application, a biological material disinfection method based on big data analysis is provided, wherein the method comprises: Figure 1 Collecting material properties of biological material to be disinfected, combining a disinfectant selection library with a pre-constructed material damage database to perform material-disinfection damage analysis, and determining a target disinfectant.

[0015] ​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.

[0016] 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: The material properties are used as retrieval constraints to collect historical disinfection data, analyze the performance change data of the biological material, and establish a material damage database. Based on random forest, the material damage database is used to establish a material-disinfection efficiency-damage comprehensive evaluation model. According to the material-disinfection efficiency-damage comprehensive evaluation model, the disinfection condition that achieves the optimal balance between sterilization efficiency and material safety is screened out, and the target disinfectant is generated.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] The biological material is subjected to Micro-CT scanning to construct a 3D digital model.

[0021] Further, the biological material is subjected to Micro-CT scanning to construct a 3D digital model, including: 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.

[0022] 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 through three-dimensional volume rendering and region segmentation operations, the geometric and pore information of the material is extracted, and finally the 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.

[0023] 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: The same row of pixels in the plurality of 2D projection images is extracted and arranged in a 2D image in order of angle to form 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.

[0024] In the image reconstruction of several 2D projection images, first, the projection data needs to be geometrically arranged, that is, the same row of pixels is extracted from several 2D projection images, and is arranged in order according to the projection angle, so as to construct a 2D image with pixel gray value as an element, which is called a sinogram. Then, distortion correction is performed on the sinogram, wherein the beam hardening correction is performed by calling a predefined linear function to remap the projection value, for compensating for the nonlinear attenuation effect caused by the difference in X-ray spectral components; the ring artifact correction is performed by smoothing the sinogram in a transverse filtering manner, for eliminating the ring stripe artifacts caused by uneven response of the detector pixels or mechanical rotation error. After the above correction is completed, a filtered back projection algorithm is performed on the sinogram, the projection signal is converted into a gray scale distribution in the spatial domain, and continuous 2D gray scale slices are generated layer by layer. Finally, the continuous 2D gray scale slices are stacked in spatial order, so as to obtain volume data capable of representing the three-dimensional internal structure of the material, and further to construct a complete 3D digital model.

[0025] The physical property information of the target disinfectant and the interaction relationship between the target disinfectant and the biological material are read, the space-time dynamic distribution of the flow, diffusion and penetration of the target disinfectant in the internal channel of the material is simulated based on the 3D digital model, the flow field and concentration field of the internal disinfection blind area are optimized through pulse pressure circulation, and the target disinfection parameter is generated to control the disinfection of the biological material.

[0026] In the embodiment of the application, the physical property information of the target disinfectant is read, including density, viscosity, diffusion coefficient and surface tension and the like, and the interaction relationship between the target disinfectant and the biological material surface is obtained, the interaction relationship includes adsorption rate constant and desorption rate constant, for representing the kinetic characteristics of the adsorption and desorption process of the disinfectant molecules on the material surface. Subsequently, the aforementioned 3D digital model constructed based on the Micro-CT is taken as a calculation domain, a multi-physical field simulation module is called, fluid dynamics and rare substance transfer models are coupled and integrated, the flow, diffusion and penetration process of the target disinfectant in the internal complex channel network of the material is simulated, and the concentration distribution and flow velocity distribution at different space-time points are obtained.

[0027] 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.

[0028] 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: 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 repeated 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.

[0029] 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.

[0030] Further, the parameters of the pulse pressure waveform include peak pressure, valley pressure, pulse period and duty cycle.

[0031] 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 the laminar flow module and the rare substance transfer module, and thus the basic flow and diffusion field distribution of the disinfectant in the pore structure is obtained.

[0032] Based on the preliminary simulation state, an initial pulse pressure waveform (including peak pressure, valley pressure, pulse period and duty cycle) is set, the flow, diffusion and penetration process of the target disinfectant in the material internal pore are simulated by multi-physical field coupling, the disinfectant concentration distribution of each space-time point in the whole calculation domain is output, and the first space-time dynamic distribution map is obtained. Based on the first space-time dynamic distribution map, the area whose concentration is continuously lower than the effective sterilization threshold within the preset time length is identified, defined as the first internal disinfection blind area, and the concentration-time integral result of all grid nodes in the blind area is counted, and the first concentration-time integral value is obtained.

[0033] 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 the 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: .

[0034] Taking the improvement of the first concentration-time integral value as the optimization goal, the 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 the 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, so as to realize complete disinfection.

[0035] In summary, the embodiments of the present application have at least the following technical effects: First, the material properties of the biological material to be disinfected are collected, the material-disinfection damage analysis is performed in combination with a disinfectant selection library and a pre-constructed material damage database, and a target disinfectant is determined. Second, the biological material is subjected to Micro-CT scanning, and a 3D digital model is constructed. Finally, the physical property information of the target disinfectant and the interaction relationship between the target disinfectant and the biological material are read, the spatiotemporal dynamic distribution of the flow, diffusion and penetration of the target disinfectant in the internal pore channel of the material is simulated based on the 3D digital model, the flow field and concentration field of the internal disinfection blind area are optimized through pulse pressure circulation, and the target disinfection parameters are generated to control the disinfection of the biological material. The technical problems of insufficient adaptability of the disinfectant to the biological material 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 risk of material damage is achieved.

[0036] In the second embodiment, based on the same inventive concept as the biological material disinfection method based on big data analysis in the foregoing embodiments, as shown in the following table, the present application provides a biological material disinfection system based on big data analysis, wherein the system comprises: Figure 2 The analysis module 11 collects the 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 12 subjects the biological material to Micro-CT scanning and constructs a 3D digital model. The disinfection control module 13 reads the physical property information of the target disinfectant and the interaction relationship between the target disinfectant and the biological material, simulates the spatiotemporal dynamic distribution of the flow, diffusion and penetration of the target disinfectant in the internal pore channel of the material based on the 3D digital model, optimizes the flow field and concentration field of the internal disinfection blind area through pulse pressure circulation, and generates target disinfection parameters to control the disinfection of the biological material.

[0037] Further, the analysis module 11 is configured to perform the following method: The historical disinfection data is collected with the material properties as a retrieval constraint, the performance change data of the biological material under various disinfection conditions 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, the disinfection conditions that achieve the optimal balance between the sterilization efficiency and the material safety are screened out according to the material-disinfection efficiency-damage comprehensive evaluation model, and the target disinfectant is generated.

[0038] Further, the analysis module 11 is configured to perform the following method: ​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.

[0039] Further, the disinfection control module 13 is configured to perform the following method: 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 disinfectant concentration distribution 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.

[0040] Further, the disinfection control module 13 is configured to perform the following method: The interaction relationship includes an adsorption / desorption rate constant of an adsorption behavior of the target disinfectant on the surface of the biological material.

[0041] Further, the disinfection control module 13 is configured to perform the following method: The parameters of the pulse pressure waveform include a peak pressure, a valley pressure, a pulse period, and a duty cycle.

[0042] Further, the model construction module 12 is configured to perform the following method: The scanned sample of the biological material is fixed and packaged, the sample is continuously 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 body data is output, wherein the three-dimensional body data is stacked by continuous 2D grayscale slices; three-dimensional body rendering and region segmentation are performed based on the three-dimensional body data, and the 3D digital model is generated, and the 3D digital model is identified with porosity, pore size distribution, structure model thickness distribution, tortuosity, and specific surface area.

[0043] Further, the model construction module 12 is configured to perform the following method: A 2D image is extracted from the same row of pixels in the several 2D projection images and arranged in an angle sequence to form a sinogram; the sinogram is subjected to beam hardening correction and annular artifact correction and then filtered back projection to generate continuous 2D gray scale slices, wherein the beam hardening correction is mapping the projection value through a predefined linear function, and the annular artifact correction is transverse filtering of the sinogram; and the continuous 2D gray scale slices are stacked to form the 3D digital model.

[0044] Embodiment three, Figure 3 The structural schematic diagram of an 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 of the processor 21, the processor 21, the memory 22, the input device 23 and the output device 24 in the electronic device can be connected through a bus or other means, Figure 3 In the example of connection through a bus.

[0045] The memory 22 is a kind of computer readable storage medium, which 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 executes the software programs, instructions and modules stored in the memory 22, thereby performing various functional applications and data processing of the electronic device, i.e. implementing the above-mentioned biological material disinfection method based on big data analysis.

[0046] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, not representing the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application have been described. 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.

[0047] The above-mentioned 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.

[0048] 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 sterilization of biological material based on big data analysis, characterized in that, The method comprises: 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 determining 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 internal pores of the materials based on the 3D digital model, optimizing flow field and concentration field of internal disinfection blind areas through pulsed pressure circulation, and generating target disinfection parameters to control disinfection of the biological materials.

2. The big data analytics based biological material disinfection method as claimed in claim 1, wherein, 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 determining a target disinfectant, comprising: Collecting historical disinfection data with the material properties as retrieval constraints, analyzing performance change data of the biological materials under various disinfection conditions, and establishing a material damage database; Based on random forest, a material-disinfection efficiency-damage comprehensive evaluation model is established using the material damage database; According to the material-disinfection efficiency-damage comprehensive evaluation model, disinfection conditions that achieve an optimal balance between bactericidal efficiency and material safety are screened out, and the target disinfectant is generated.

3. The big data analytics based biological material disinfection method as claimed in claim 2, wherein, The performance change data includes variation 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.

4. The big data analytics based biological material disinfecting method as claimed in claim 1, wherein, Simulating spatiotemporal dynamic distribution of flow, diffusion and penetration of the target disinfectant in internal pores of the materials based on the 3D digital model, and optimizing flow field and concentration field of internal disinfection blind areas through pulsed pressure circulation, comprising: Taking the 3D digital model as a calculation domain, taking the physical property information and the interaction relationship as input parameters, establishing a preliminary simulation state by coupling laminar flow and rare substance transfer physical field interface; Step A: based on the preliminary simulation state, simulating flow, diffusion and penetration process of the target disinfectant in internal pores of the materials under an applied initial pulsed pressure waveform, outputting disinfectant concentration distribution at different spatiotemporal points in the entire calculation domain, and obtaining a first spatiotemporal dynamic distribution map; Step B: based on the first spatiotemporal dynamic distribution map, identifying a first internal blind area where disinfectant concentration is continuously lower than an effective sterilization threshold for a preset time length, and calculating a first concentration-time integral value of disinfectant in the first internal blind area; Taking improving the first concentration-time integral value as an optimization objective, adjusting parameters of the pulsed pressure waveform, and repeatedly executing steps A to B until the concentration-time integral value in the first internal blind area reaches a preset threshold, and determining a combination of pulse pressure parameters at this time as the target disinfection parameters.

5. The big data analytics based biological material disinfection method as claimed in claim 4, wherein, The interaction relationship includes adsorption / desorption rate constants of adsorption behavior of the target disinfectant on the surface of the biological materials.

6. The big data analytics based biological material disinfecting method as claimed in claim 5, wherein, The parameters of the pulsed pressure waveform include peak pressure, valley pressure, pulse period and duty cycle.

7. The big data analytics based biological material disinfecting method as claimed in claim 1, wherein, The biological material is subjected to Micro-CT scanning to construct a 3D digital model, including: The scanned sample of the biological material is fixed and packaged, and the sample is subjected to continuous Micro-CT scanning in a 360° range according to preset scanning parameters to obtain a plurality of 2D projection images; Based on the plurality of 2D projection images, image reconstruction is performed, 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; Based on the three-dimensional volume data, three-dimensional volume rendering and region segmentation are performed 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.

8. The big data analytics based biological material disinfecting method as claimed in claim 7, wherein, Based on the plurality of 2D projection images, image reconstruction is performed, including beam hardening correction and ring artifact correction, and three-dimensional volume data is output, including: The same row of pixels in the plurality of 2D projection images is extracted and arranged in order according to the angle to form a 2D image, forming a sinogram; After the sinogram is subjected to beam hardening correction and ring artifact correction, it is subjected to filter back projection 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.

9. A biological material disinfection system based on big data analysis, characterized by, A system for implementing the biological material disinfection method based on big data analysis according to any one of claims 1-8, the system comprising: An analysis module: 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; A model construction module: performing Micro-CT scanning on the biological material to construct a 3D digital model; A disinfection control module: reading physical property information of the target disinfectant and interaction relationship between the target disinfectant and the biological material, simulating the spatiotemporal dynamic distribution of the target disinfectant in the internal channels of the material based on 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.

10. An electronic device, comprising: The electronic device comprises: a memory for storing executable instructions; a processor for executing the executable instructions stored in the memory to implement the biological material disinfection method based on big data analysis according to any one of claims 1-8. The electronic device comprises: a memory for storing executable instructions; a processor for executing the executable instructions stored in the memory to implement the biological material disinfection method based on big data analysis according to any one of claims 1-8.

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