Intestinal microorganism dynamic detection method and system based on digital twinning

Abstract

The present application relates to the technical field of digital twinning, and particularly provides a kind of based on digital twinning's intestinal microorganism dynamic detection method and system, method contains making different hydrophobicity microorganism in turn separate from magnetic control capture area and collect according to the order of separation, obtain the sequence of microbial components arranged according to the hydrophobicity gradient of microbial cell membrane;After temperature threshold sequencing, the phase transition characteristic vector corresponding to each component is generated, and the liquid crystal phase transition map of microbial community is formed by combining the phase transition characteristic vectors of all components;The liquid crystal phase transition map is input into the intestinal wall multilayer mechanical parameter program reconstructed in advance by ultrasonic elastography, and each microbial component is distributed to the matching intestinal axial region, and the functional zone separation twinning mapping of microbial community along the length direction of intestine is generated from the distribution relationship.The present application realizes the continuous analysis process from the physical and chemical separation of microorganism to the thermodynamic characterization of group, and then to the dynamic matching of intestinal mechanical environment.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and in particular to a method and system for dynamic detection of gut microbiota based on digital twins. Background Technology

[0002] The gut microbiota is closely related to the host's digestive, metabolic, immune, and nervous system functions, and its spatial distribution and functional activities are key to understanding the mechanisms of gut health and disease. Traditionally, research on gut microbiota has relied primarily on fecal sample analysis, intestinal content biopsies combined with high-throughput sequencing or in vitro culture techniques. While fecal analysis is non-invasive, it cannot reflect the differences in in-situ distribution and dynamic functional status of microorganisms in different axial regions of the gut, such as the ileum and colon. Biopsy sampling is invasive and can only provide local, static snapshot information, making it difficult to construct a continuous spatial map of the microbiota along the entire length of the intestine.

[0003] In recent years, the concept of digital twins has emerged in the biomedical field, aiming to construct high-fidelity virtual models of biological systems through multimodal data fusion. However, existing technologies lack a method to dynamically correlate the inherent physicochemical properties of gut microbiota, such as cell membrane hydrophobicity and thermal response behavior, with regional physical environmental parameters of the gut, such as mechanical properties, and to construct a spatial functional mapping of the microbial community. This limits a deeper understanding of microbe-gut interface interactions and the development of precision diagnostic and treatment strategies based on microbial spatial heterogeneity.

[0004] The existing technologies related to the above mainly involve the following aspects: 1. Microbial separation technology based on physical properties: Existing studies have utilized the differences in hydrophobicity of microbial surfaces to separate them through two-phase partitioning, hydrophobic interaction chromatography, or microfluidic technology. For example, some techniques use magnetic beads or chromatographic columns with different hydrophobicities to batch enrich microorganisms under fixed magnetic field or mobile phase conditions; usually, the aim is to roughly divide the microbial community into hydrophobic and hydrophilic subgroups, but it is difficult to achieve continuous and fine separation based on hydrophobic gradients, and the separation process is often disconnected from subsequent functional characterization steps. 2. Biosensing technology based on liquid crystal materials: Due to their high sensitivity to interfacial biomolecular interactions, liquid crystal materials have been used to detect biological entities such as proteins, DNA, and bacteria. Existing technologies typically fix liquid crystal films onto the sensing interface and detect changes in liquid crystal orientation caused by the binding of target substances, such as the transition from an ordered phase to a disordered phase and the corresponding temperature threshold changes. These applications are mostly focused on the qualitative or quantitative analysis of single biological components, and have not yet been used for parallel, high-throughput extraction of thermal response features from multiple components in complex microbial communities, or for constructing community-level phase transition maps. 3. Intestinal biomechanical modeling and digital twins: Based on technologies such as ultrasound elastography and magnetic resonance elastography, the distribution of mechanical parameters of the intestinal wall, such as shear modulus and viscosity coefficient, can be reconstructed, and finite element models of the intestine can be constructed. These models are mainly used to study pathological changes in the intestinal wall, such as fibrosis, tumors, or to simulate stress distribution during intestinal peristalsis. However, existing intestinal biomechanical models have not yet been coupled with the in-situ distribution characteristics of the microbial community, and lack a dynamic matching procedure to map in vitro characterization data of microorganisms back to the specific mechanical microenvironment within the intestine.

[0005] Comprehensive analysis reveals the following main drawbacks of existing technologies: 1. Severe loss of spatial information: Traditional fecal analysis or in vitro culture methods completely destroy the original spatial location information of microorganisms in the intestine; even with biopsy sampling, only scattered data from a limited number of sites can be obtained, making it impossible to reconstruct a continuous, high-resolution spatial distribution map of the microbial community along the entire length of the intestine, from the ileum to the colon; this results in blind spots in the study of microbial-host region-specific interactions. 2. Fragmented separation and characterization processes: Existing physical property-based microbial isolation technologies typically output several broad subgroups, lacking high-resolution continuous component sequences. More importantly, the isolated microorganisms are rarely standardized and subjected to high-throughput characterization techniques that can reflect their collective functional characteristics. No direct correlation bridge is established between the isolated components and the physiological environmental parameters in the intestine, making it difficult to use in vitro isolation results to infer the true functional state and colonization location of microorganisms in vivo. 3. Lack of dynamic, in-situ functional localization capabilities: Existing intestinal digital twin models focus on tissue mechanics itself, without incorporating the biological characteristics of the microbial community as an active variable into the model; currently, it is impossible to dynamically predict or infer the possible functional zoning of microbial samples in the complex and heterogeneous intestinal physical environment by rapidly detecting the physicochemical characteristics of microbial samples in vitro; this limits the development of disease diagnosis, prognosis, and targeted intervention strategies based on the spatial heterogeneity of microorganisms. Summary of the Invention

[0006] To achieve the above objectives, the present invention adopts the following technical solution: One aspect of the present invention provides a method for dynamic detection of gut microbiota based on digital twins, comprising the following steps: Newly collected intestinal contents samples were mixed with magnetic nanoparticles with alkyl chains of different lengths on their surface. Under the action of a gradient alternating magnetic field, based on the compatibility between the hydrophobicity of the microbial cell membrane and the alkyl chains, microorganisms with different hydrophobicities were sequentially released from the magnetic capture area and collected in the order of release, resulting in a sequence of microbial components arranged according to the hydrophobicity gradient of the microbial cell membrane. Each component in the microbial component sequence was introduced into a microcavity coated with liquid crystal films with different phase transition temperatures. The temperature threshold of each liquid crystal film when it transitions from an ordered phase to a disordered phase was recorded by linearly varying the infrared light irradiation of the microcavity. After sorting by temperature threshold, the phase transition feature vectors corresponding to each component were generated. The liquid crystal phase transition map of the microbial community was formed by combining the phase transition feature vectors of all components. The liquid crystal phase transition spectrum is input into a multilayer mechanical parameter program for the intestinal wall, which is pre-reconstructed by ultrasound elastography. Each grid node stores the local shear modulus and viscosity coefficient. The deviation between each phase transition feature vector in the liquid crystal phase transition spectrum and the expected thermodynamic response at the grid node is calculated. Each microbial component is assigned to a matching axial region of the intestine, and the functional partition twin mapping of the microbial community along the length of the intestine is generated from the allocation relationship.

[0007] In one aspect, the present invention provides a dynamic detection system for gut microbiota based on digital twins, comprising: The hydrophobic gradient arrangement module is used to mix newly collected intestinal contents samples with magnetic nanoparticles with alkyl chains of different lengths on their surface. Under the action of a gradient alternating magnetic field, based on the compatibility between the hydrophobicity of the microbial cell membrane and the alkyl chains, microorganisms with different hydrophobicities are sequentially released from the magnetic capture area and collected in the order of release, resulting in a sequence of microbial components arranged according to the hydrophobic gradient of the microbial cell membrane. The phase transition map generation module is used to introduce each component in the microbial component sequence into a microcavity coated with liquid crystal films with different phase transition temperatures. The microcavity is irradiated with infrared light in a linear manner, and the temperature threshold when each liquid crystal film changes from an ordered phase to a disordered phase is recorded. After sorting by temperature threshold, the phase transition feature vectors corresponding to each component are generated. The liquid crystal phase transition map of the microbial community is formed by combining the phase transition feature vectors of all components. The mechanical deviation response module is used to input the liquid crystal phase transition spectrum into a multilayer mechanical parameter program of the intestinal wall reconstructed by ultrasound elastography. Each grid node stores the local shear modulus and viscosity coefficient. By calculating the deviation between each phase transition feature vector in the liquid crystal phase transition spectrum and the expected thermodynamic response at the grid node, the module allocates each microbial component to the matching intestinal axial region and generates a functional partition twin map of the microbial community along the length of the intestine based on the allocation relationship.

[0008] This invention realizes a continuous analysis process from microbial physicochemical separation to population thermodynamic characterization, and then to dynamic matching of the intestinal mechanical environment; it can perform in-situ functional localization and dynamic tracking of intestinal microorganisms under non-invasive conditions, providing a high-dimensional spatiotemporal analysis tool for studying microbial-intestinal interface interactions. Attached Figure Description

[0009] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the dynamic detection method for gut microbiota based on digital twins provided in Embodiment 1 of the present invention; Figure 2This is a schematic diagram of the dynamic detection method for gut microbiota based on digital twins provided in Embodiment 1 of the present invention. Figure 3 This is a process diagram of obtaining the microbial component sequences arranged according to the hydrophobicity gradient of the microbial cell membrane, as provided in Embodiment 2 of the present invention; Figure 4 This is a process diagram of the liquid crystal phase transition spectrum of the microbial community formed by combining the phase transition feature vectors of all components, as provided in Embodiment 5 of the present invention. Figure 5 This is a process diagram of distributing each microbial component to a matching intestinal axial region, as provided in Embodiment 8 of the present invention; Figure 6 This is a block diagram of the intestinal microbiome dynamic detection system based on digital twin provided in Embodiment 14 of the present invention; Figure 7 A block diagram of the electronic device provided by the present invention; Figure 8 A block diagram of a computer-readable storage medium provided for this invention.

[0010] Reference numerals: 1. Hydrophobic gradient arrangement module; 2. Phase transition spectrum formation module; 3. Mechanical deviation response module; 4. Central processing unit / microprocessor / main control chip; 5. Storage medium; 6. Data bus; 7. Input / output bus / external bus / device bus; 8. Display; 9. Input / output device; 10. Computer-readable instructions; 11. Non-transitory computer-readable storage medium. Detailed Implementation

[0011] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0012] Hereinafter, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0013] In this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or an integral part; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. Furthermore, unless otherwise explicitly specified and limited, the term "coupling" should be interpreted broadly. For example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components; it can also be understood as an electrical connection between different components in a circuit structure through physical lines capable of transmitting electrical signals, such as copper foil or wires on a printed circuit board (PCB), to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in a non-contact manner, such as an electrical connection between two components using capacitive coupling to transmit electrical signals.

[0014] In this embodiment of the invention, directional terms such as "up," "down," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and can change accordingly depending on the orientation of the components in the accompanying drawings.

[0015] This invention precisely separates microorganisms according to their hydrophobicity gradient, generates a community phase transition map reflecting collective thermodynamic behavior, and calculates and matches the deviation between this map and a multilayer mechanical model of the intestinal wall. Ultimately, it generates a comprehensive detection method for functional partition twinning of the microbial community along the length of the intestine. This represents a leap from community composition analysis to in-situ functional localization, providing a high-dimensional spatiotemporal analytical tool for gut microbiota research.

[0016] Example 1: As Figure 1 As shown, this embodiment of the invention provides a method for dynamic detection of gut microbiota based on digital twins, comprising the following steps: Step S100: Mix the newly collected intestinal contents sample with magnetic nanoparticles with alkyl chains of different lengths on the surface; under the action of a gradient alternating magnetic field, based on the compatibility between the hydrophobicity of the microbial cell membrane and the alkyl chain, microorganisms with different hydrophobicities are sequentially released from the magnetic capture area and collected in the order of release to obtain a sequence of microbial components arranged according to the hydrophobicity gradient of the microbial cell membrane. Step S200: Each component in the microbial component sequence is introduced into a microcavity coated with liquid crystal films with different phase transition temperatures. The microcavity is irradiated with infrared light in a linear manner, and the temperature threshold when each liquid crystal film changes from an ordered phase to a disordered phase is recorded. After sorting by temperature threshold, the phase transition feature vectors corresponding to each component are generated. The liquid crystal phase transition map of the microbial community is formed by combining the phase transition feature vectors of all components. Step S300: Input the liquid crystal phase transition map into the intestinal wall multilayer mechanical parameter program reconstructed by ultrasound elastography. Each grid node stores the local shear modulus and viscosity coefficient. Calculate the deviation between each phase transition feature vector in the liquid crystal phase transition map and the expected thermodynamic response at the grid node. Assign each microbial component to the matching intestinal axial region and generate a functional partition twin map of the microbial community along the length of the intestine based on the assignment relationship.

[0017] The intestinal contents sample refers to unfiltered or uncultured raw intestinal contents containing various microorganisms collected from the intestines of a living organism, used to preserve the natural hydrophobic distribution of microorganisms in the intestines. Magnetic nanoparticles are artificially modified with nanoscale magnetic particles containing alkyl chains of varying lengths. The difference in alkyl chain length allows the particles to exhibit variable adhesion and detachment responses to microbial membranes with different hydrophobicities. A gradient alternating magnetic field environment, with its intensity gradually changing spatially along a time-varying gradient, applies variable magnetic force to the microorganisms adsorbed with the magnetic nanoparticles, causing microorganisms with different hydrophobicities to be released sequentially from the magnetically controlled capture area. The inherent difference in hydrophobicity between the microbial cell membrane and the alkyl chain microbial cell membrane surface, along with the difference in selective binding strength between the alkyl chains of different lengths on the magnetic nanoparticles, determines the detachment order of microorganisms under the gradient alternating magnetic field. The magnetically controlled capture area, a spatial range formed by a fixed magnetic field source, is used to concentrate and retain the microorganisms labeled with the magnetic nanoparticles before the gradient alternating magnetic field is applied, and to release them sequentially according to their hydrophobicity as the alternating magnetic field changes. The microcavities of liquid crystal films with different phase transition temperatures are arrays of multiple microcavities. Each cavity's inner wall is coated with a liquid crystal film of a different phase transition temperature, used to receive separated microbial components and detect their thermal response characteristics. The phase transition feature vector is a numerical sequence composed of multiple liquid crystal films with phase transition temperature thresholds corresponding to the same microbial component, sorted to quantify the collective response characteristics of the microbial component under thermal excitation. In the multi-layer mechanical parameter program of the intestinal wall, grid nodes represent three-dimensional spatial points formed by discretizing the intestinal wall. Each node stores the local shear modulus and viscosity coefficient at that location. Local shear modulus and viscosity coefficient represent the ability of a local region of the intestinal wall to resist shear deformation and energy dissipation characteristics, respectively, obtained through ultrasound elastography reconstruction, and used to establish mechanical environment models of each axial region of the intestinal wall. The axial region of the intestinal wall is divided into continuous segments along the length of the intestinal wall. Each segment has a specific combination of mechanical parameters (shear modulus and viscosity coefficient) to match the natural colonization or distribution locations of different microbial components within the intestine.

[0018] In the above embodiments, the principle is referenced in the appendix. Figure 2This embodiment of the intestinal microbiome dynamic detection method based on digital twins integrates magnetic nanoparticle separation, liquid crystal film phase transition recording, and intestinal mechanical model mapping to achieve high-resolution and dynamic functional localization of the microbial community. Utilizing magnetic nanoparticles with surface-modified alkyl chains of different lengths and a gradient alternating magnetic field, the continuous separation and sequential collection of microorganisms are achieved based on the differences in the hydrophobicity of microbial cell membranes. It can generate ordered microbial component sequences according to the physicochemical properties of microbial surfaces without relying on traditional culture or labeling, providing a structured basis for biological samples. By introducing each microbial component into a microcavity coated with liquid crystal films of different phase transition temperatures and irradiating it with linear infrared light, the temperature threshold for the liquid crystal film to transition from an ordered phase to a disordered phase is recorded, generating phase transition feature vectors corresponding to each component. The combination of these phase transition feature vectors forms a liquid crystal phase transition spectrum of the microbial community, which can reflect the overall response behavior of the microbial community under thermodynamic stimulation, thereby characterizing its metabolic activity or the population characteristics of its membrane composition. The liquid crystal phase transition spectrum is input into a program for reconstructing multilayer mechanical parameters of the intestinal wall based on ultrasound elastography. This program includes a gridded model of local shear modulus and viscosity coefficient. By calculating the deviation between the phase transition eigenvector and the expected thermodynamic response of each grid node, the microbial components are matched to specific regions along the intestinal axis, thereby generating a functional partition twin map of the microbial community along the length of the intestine.

[0019] In summary, this embodiment realizes a continuous analysis process from microbial physicochemical separation to population thermodynamic characterization, and then to dynamic matching of the intestinal mechanical environment; it enables in-situ functional localization and dynamic tracking of intestinal microorganisms under non-invasive conditions, providing a high-dimensional spatiotemporal analysis tool for studying microbe-intestinal interface interactions.

[0020] Example 2: As Figure 3 As shown, based on Example 1, in step S100 provided in this embodiment of the invention... Step S101: The newly collected intestinal contents sample is mixed with magnetic nanoparticles with alkyl chains of different lengths on the surface in an ultrasonic standing wave field, so that the alkyl chains can reversibly bind to the hydrophobic region of the microbial cell membrane to obtain a labeled microbial suspension. Step S102: The labeled microbial suspension is injected into a microcapillary array whose inner wall is immobilized with short-chain nucleic acid probes complementary to the ends of alkyl chains. An alternating magnetic field with a gradient increasing along the length of the microcapillary array is applied to the outside of the microcapillary array. At the same time, a linearly increasing temperature gradient is established from one end of the microcapillary array to the other end, so that microorganisms with different hydrophobicities are separated along the length of the tube under the superposition of thermophoretic migration and magnetophoretic shift, resulting in microbial zones arranged along the capillary axis. Step S103: After turning off the gradient alternating magnetic field and removing the temperature gradient, use laser-induced fluorescence imaging to read the axial position of each microbial zone in the microcapillary array; draw each zone into an independent collection container in order from the low temperature end to the high temperature end, and obtain the microbial component sequence arranged according to the hydrophobicity gradient of the microbial cell membrane from the order of aspiration.

[0021] Among them, the short-chain nucleic acid probes with complementary alkyl chain ends refer to artificially synthesized short-chain nucleic acid molecules that are anticomplementary to the nucleotide sequences at the ends of the alkyl chains on the surface of magnetic nanoparticles. These probes are covalently immobilized on the inner wall of the microcapillary array. In the presence of a temperature gradient, microorganisms with different hydrophobicities, carrying alkyl chains of varying lengths, undergo instantaneous and reversible hybridization with the probes on the inner wall. The hybridization intensity is regulated by local temperature, thereby assisting in the differentiated retention of microorganisms along the length of the capillary, achieving separation. The microcapillary array is a microfluidic structure composed of multiple parallel capillaries with the same inner diameter. The inner wall of each capillary is pre-immobilized with the aforementioned short-chain nucleic acid probes. This array is used to contain the labeled microbial suspension and, under the combined action of an external gradient alternating magnetic field and a temperature gradient, provides a physical channel for the migration and separation of microorganisms along the length of the capillary. The gradient alternating magnetic field has a linearly increasing field strength and periodically changing direction along the length of the microcapillary array. This magnetic field exerts a magnetic force on microorganisms adsorbed with magnetic nanoparticles, the magnitude of which varies with position. This causes the magnetophoretic migration velocity of the microorganisms within the array to differ along the length of the array. The magnetophoretic migration direction is perpendicular to the array axis and affects the net migration rate of the microorganisms along the array axis. The temperature gradient is a linearly increasing temperature distribution from one end of the microcapillary array to the other, with the lower temperature end closer to the initial injection end and the higher temperature end at the end of the array. This temperature gradient regulates the hybridization and dissociation rate between the alkyl chain ends and the nucleic acid probes on the inner wall, and induces thermophoretic migration in the liquid within the array, i.e., the movement of microorganisms in a specific direction due to local temperature differences. Thermophoretic migration and magnetophoretic migration: Thermophoretic migration refers to the phenomenon of microorganisms moving from a high-temperature region to a low-temperature region or in the opposite direction under the drive of a temperature gradient, the specific direction of which is determined by the microbial membrane composition and the solution environment; magnetophoretic migration refers to the movement of microorganisms away from the center of the array axis under the influence of a gradient alternating magnetic field and the magnetic force. The two are superimposed within the microcapillary array, allowing microorganisms with different hydrophobicities to obtain different net displacements as they migrate along the length of the tube, ultimately forming axially arranged microbial zones, thereby achieving continuous separation according to the hydrophobicity gradient.

[0022] In the above embodiments, this embodiment achieves high-resolution, non-destructive separation and sequencing of complex microbial communities. Magnetic nanoparticle labeling, through the reversible binding of alkyl chains to the hydrophobic regions of the cell membrane, can specifically identify the hydrophobic properties of microbial surfaces; an ultrasonic standing wave field promotes binding efficiency and maintains microbial activity. Complementary nucleic acid probes immobilized on the inner wall of the microcapillary array provide directional anchoring points, enhancing separation specificity; the synergistic effect of a gradient alternating magnetic field and a linear temperature gradient causes differentiated migration of microorganisms with different hydrophobicities in the thermophoretic and magnetophoretic coupled fields, achieving continuous spatial separation along the length of the capillary. Laser-induced fluorescence imaging enables precise identification of zonal positions, and sequential collection of zonal samples directly outputs the microbial component sequences arranged according to the hydrophobicity gradient. This avoids the impact of traditional separation methods on microbial activity while preserving the original functional characteristics of the microorganisms, providing structurally complete community subgroups for subsequent microbial functional studies or targeted culture.

[0023] Example 3: Based on Example 2, the process of using laser-induced fluorescence imaging to read the axial position of each microbial zone within the microcapillary array in step S103 of this embodiment of the invention specifically includes the following steps: Step S1031: After turning off the gradient alternating magnetic field and removing the temperature gradient, a fluorescent quenching probe solution complementary to the alkyl chain ends is simultaneously injected into each capillary in the microcapillary array. It does not emit light in the free state, but when it hybridizes with the alkyl chain ends on the surface of the magnetic nanoparticles in the microbial zone, it resumes fluorescence emission, thus obtaining the in-situ activated fluorescent labeling band in each microbial zone. Step S1032: Place the entire microcapillary array in a confocal Raman scattering illumination path and sequentially excite each fluorescent labeling band with multiple discrete wavelengths matching the alkyl chain length; at the same time, record the excitation wavelength and capillary number corresponding to the fluorescence intensity peak collected by the fiber bundle fixed at the outlet end of each capillary, and obtain a pairing list of spatial coordinates and wavelength codes for each microbial zone. Step S1033: Based on the mapping relationship between the capillary number in the pairing list and the pre-calibrated pixel position along the tube length direction, convert the tube length position corresponding to each fluorescence intensity peak into an axial distance value, and generate the position sequence of each microbial zone along the capillary axis by sorting the distance values ​​from smallest to largest.

[0024] The fluorescent quenching probe solution is a homogeneous liquid reagent composed of a fluorescent quenching probe and a buffer solution. The nucleotide sequence of the probe is inversely complementary to the nucleotide sequence at the end of the alkyl chain on the surface of the magnetic nanoparticles. The probe molecule is simultaneously linked to both a fluorescent group and a quenching group. When the probe is free in the solution, the two groups quench each other due to their spatial proximity, and no fluorescence is emitted. When the probe hybridizes with the end of the alkyl chain on the surface of the magnetic nanoparticles in the microbial zone within the microcapillary array, the fluorescent group and the quenching group separate, and fluorescence emission is restored. This solution is used to perform in-situ, background-free fluorescent labeling of microbial zones that have been arranged along the tube length after the magnetic field is turned off and the temperature gradient is removed, so that each microbial zone obtains an excitable fluorescent signal corresponding to its alkyl chain length. Confocal Raman scattering illumination optical path: This refers to an optical system consisting of a multi-wavelength laser source, a confocal pinhole, a beam splitter, a scanning galvanometer, and an objective lens. The illumination laser beam is focused by the objective lens onto a specific depth plane of a capillary within the microcapillary array. The confocal pinhole blocks scattered light from outside the focal plane. The Raman scattering signal is collected by the same objective lens and then enters the detector through the beam splitter. In this optical path, multiple discrete wavelength lasers, numerically matched to the alkyl chain length, are sequentially switched as excitation sources. Each wavelength can only effectively excite the fluorescent label band hybridized at the end of the corresponding alkyl chain. This optical path is used to scan the axial position of each capillary point by point within the microcapillary array, while simultaneously distinguishing the fluorescence intensity peaks generated at different excitation wavelengths. This allows each fluorescence signal to be associated with a specific capillary number and excitation wavelength to obtain the spatial coordinates and wavelength encoding information of the microbial zone.

[0025] In the above embodiments, this embodiment achieves high-precision, multi-channel parallel optical detection and coding localization of the axial position of microbial zones. The in-situ hybridization activation mechanism of the fluorescence quenching probe ensures that the fluorescence signal is strictly limited to the target microbial zone, effectively reducing background interference. The confocal Raman illumination optical path combined with discrete wavelength excitation enables microbial zones labeled with magnetic nanoparticles of different alkyl chain lengths to produce wavelength-specific fluorescence responses, realizing optical coding identification based on hydrophobic properties. The fiber bundle synchronously collects the exit signals of each capillary, allowing the parallel acquisition of fluorescence intensity peak data of all zones within the capillary. By pairing the excitation wavelength with the capillary number, the optical signal is associated with spatial coordinates to form a traceable coding list. Based on the pre-calibrated pixel-distance mapping relationship, the fluorescence peak position is converted into a precise axial distance value, and finally, a sequence of microbial zone positions arranged in spatial order is output. This is completed under non-contact conditions, avoiding physical disturbance to the microbial zones. At the same time, wavelength coding enables simultaneous detection of multiple parameters, providing a reliable spatial positioning basis for sequential collection.

[0026] Example 4: Based on Example 3, the process of converting the tube length position corresponding to each fluorescence intensity peak into an axial distance value in step S1033 of this embodiment of the invention specifically includes the following steps: Step S10331: Multiply the imaging pixel row number corresponding to each fluorescence intensity peak in the pairing list with the pre-stored linear transformation coefficient from pixel row number to physical distance to obtain the original distance measurement value of the fluorescence intensity peak along the capillary axis. Step S10332: Based on the capillary number corresponding to the peak value in step S10331, retrieve the cubic polynomial coefficients that match the capillary number from the pre-recorded bending correction coefficient array; perform term-by-term polynomial evaluation on the original distance measurement value to obtain the distance measurement value after tube distortion correction. Step S10333: Arrange all corrected distance measurements in ascending order of excitation wavelength in the pairing list; and subtract the fixed offset corresponding to the same capillary inlet end in turn to output the distance value sequence of each microbial zone along the capillary axis.

[0027] In the above embodiments, this embodiment achieves high-precision and repeatable spatial calibration conversion from imaging pixel coordinates to actual physical distances. A linear transformation coefficient is used to quickly convert pixel row numbers into preliminary axial distances, establishing a direct correspondence between optical signals and spatial positions. Based on capillary numbers, pre-stored cubic polynomial coefficients are retrieved for bending correction, effectively compensating for systematic errors in axial distance measurements caused by tubular distortion, bending, or non-uniform deformation of the microcapillary array due to fabrication or thermodynamic effects, thus improving the geometric consistency of cross-capillary measurements. Distance values ​​are arranged in order of excitation wavelength to ensure a strict correspondence between the output sequence and the hydrophobicity gradient. Subtracting the fixed offset at the capillary inlet eliminates reference plane errors introduced by installation deviations of the sample inlet or optical detection window, ultimately obtaining a standardized axial distance sequence with the capillary inlet as the unified origin. Through a multi-level correction mechanism, optical imaging information is transformed into spatial coordinate data with clear physical meaning, providing a metrological basis for the precise location and sequential collection of microbial zones, while ensuring the comparability and repeatability of cross-batch experimental data.

[0028] Example 5: Figure 4 As shown, based on Example 1, the process of forming a liquid crystal phase transition spectrum of a microbial community by combining the phase transition feature vectors of all components in step S200 of this embodiment of the invention specifically includes the following steps: Step S201: Divide the temperature thresholds in the phase transition feature vector corresponding to each component in the microbial component sequence into two subsets with odd numbers and even numbers according to their numerical values. Calculate the modulus ratio of the discrete Fourier transform coefficients of the thresholds in the two subsets respectively to obtain the frequency domain asymmetry factor corresponding to each component. Arrange the frequency domain asymmetry factors of all components in the component separation order to form a one-dimensional frequency domain modulation sequence. Step S202: Perform bilinear interpolation on each frequency domain asymmetry factor in the one-dimensional frequency domain modulation sequence and the physical position number of the corresponding component in the micro-chamber array to generate a two-dimensional interpolation surface; perform point-by-point convolution operation on the two-dimensional interpolation with the pre-stored intestinal axial stress distribution template to obtain a two-dimensional liquid crystal phase transition convolution field. Step S203: Rearrange the values ​​of each grid point in the two-dimensional liquid crystal phase transition convolution field according to the physiological direction from the colon to the ileum, and use the values ​​as pixel gray levels to output a two-dimensional grayscale image. The two-dimensional grayscale image is a liquid crystal phase transition spectrum of the microbial community.

[0029] Bilinear interpolation refers to a method of calculating the value at any position on a two-dimensional grid by performing two linear interpolations based on the known values ​​of four adjacent grid points. It is used to map each frequency domain asymmetry factor in a one-dimensional frequency domain modulation sequence to its physical position number, row and column coordinates in a microchamber array, generating a continuous two-dimensional surface. The two-dimensional interpolation surface refers to the continuous two-dimensional numerical surface obtained after bilinear interpolation; each point on the surface corresponds to an estimated value of the frequency domain asymmetry factor at any position in the microchamber array; the surface is used for convolution with the intestinal axial stress distribution template. Discrete Fourier transform coefficients refer to the set of complex coefficients obtained by performing a Fourier transform on a discrete sequence, containing both real and imaginary parts; for each subset, the discrete Fourier transform of the odd- or even-numbered temperature threshold sequences is calculated to obtain a series of coefficients. The modulus ratio refers to the ratio of the absolute values ​​of the modulus extracted from the discrete Fourier transform coefficients, specifically the ratio of the Fourier transform modulus of the odd-numbered subset and the even-numbered subset corresponding to the same component. This ratio quantifies the difference in energy distribution between two sets of temperature thresholds in the frequency domain. The frequency domain asymmetry factor is a single value calculated from the modulus ratio, representing the degree of asymmetry in the frequency domain of the temperature threshold sequence within the same microbial component. This factor reflects the periodic modulation characteristics of the component in the thermal response of the liquid crystal film and is related to the composition of the microbial film. The temperature threshold refers to the critical temperature value at which the liquid crystal film transitions from an ordered phase to a disordered phase, recorded by irradiating the microcavity with linearly varying infrared light. Each microbial component corresponds to multiple temperature thresholds for liquid crystal films with different phase transition temperatures, and these thresholds constitute the phase transition feature vector of that component. Pixel grayscale refers to the brightness value of each pixel in a two-dimensional grayscale image, typically ranging from 0 to 255. The value of each grid point in the two-dimensional liquid crystal phase transition convolution field is linearly mapped to this range as the grayscale value of the corresponding pixel. The physiological direction from the colon to the ileum refers to the order along the length of the intestine, from the colon near the anus towards the ileum and then towards the stomach. Under natural conditions, gut microbiota exhibit a gradient distribution along this direction. The values ​​of the convolutional field grid points are rearranged according to this physiological direction to match the actual anatomical orientation of the intestine. A two-dimensional grayscale image refers to a two-dimensional array image composed of pixel grayscale values, where the grayscale value of each pixel represents the corresponding value of the liquid crystal phase transition convolutional field. This is essentially a liquid crystal phase transition map of the microbial community, used for matching with multi-layer mechanical parameters of the intestinal wall.

[0030] In the above embodiments, the process of constructing a liquid crystal phase transition map of a microbial community involves dividing the temperature thresholds of the phase transition feature vectors of each component into odd and even values ​​and calculating the modulus ratio of the discrete Fourier transform coefficients to generate a frequency domain asymmetry factor sequence, thereby reflecting the frequency domain distribution characteristics of the component phase transition behavior. Subsequently, bilinear interpolation is used to correlate the frequency domain modulation sequence with the spatial location of the components, forming a two-dimensional interpolation surface. This surface is then convolved with an intestinal axial stress distribution template to obtain a two-dimensional liquid crystal phase transition convolution field. Finally, the grid point values ​​are rearranged according to the physiological direction from the colon to the ileum and mapped to pixel grayscale, outputting a two-dimensional grayscale image as the liquid crystal phase transition map. This embodiment extracts the symmetry differences in the phase transition characteristics of microbial components through frequency domain analysis, enhances the spatial resolution of the map by combining spatial location interpolation, and uses intestinal stress template convolution to achieve the coupling mapping between the biophysical environment and phase transition behavior. The resulting grayscale image can intuitively characterize the spatial distribution pattern of liquid crystal phase transitions in the intestinal axial direction of the microbial community, providing a visual data foundation for quantitative analysis of the spatial correlation between the structural phase transitions of the microbial community and the intestinal environment.

[0031] Example 6: Based on Example 5, the process of performing point-by-point convolution operation between the two-dimensional interpolation and the pre-stored intestinal axial stress distribution template in step S202 of this embodiment of the invention specifically includes the following steps: Step S2021: After aligning the two-dimensional interpolation surface with the pre-stored intestinal axial stress distribution template, multiply each stress value in the intestinal axial stress distribution template by the square root of the frequency domain asymmetry factor corresponding to the position to obtain a weighted stress template; perform Hadamard product between the weighted stress template and the two-dimensional interpolation surface within a window of the same size to obtain the intermediate surface after point-by-point modulation. Step S2022: Perform symbol-aware accumulation and operation on the intermediate surface along two orthogonal directions respectively; wherein the accumulation direction forms a 45-degree angle with the physiological direction from colon to ileum. When the sign changes during the accumulation process, the accumulation value is reset to obtain the symbol accumulation field in the two directions; then take the geometric mean of the corresponding positions of the symbol accumulation fields in the two directions to generate a two-dimensional nonlinear convolution kernel. Step S2023: The two-dimensional nonlinear convolution kernel and the original two-dimensional interpolation surface are superimposed with median weighting within a sliding window. The weight of the convolution kernel in each window is determined by the product of the frequency domain asymmetry factor of the center point of the sliding window and the local variation coefficient of the stress template in the window. After superposition, the convolution result of the center point of each window is output. All center point results are arranged according to the original grid to form a two-dimensional liquid crystal phase transition convolution field.

[0032] In the above embodiments, this embodiment aligns the two-dimensional interpolation surface with the intestinal axial stress distribution template, then weights the stress values ​​at each point of the template using the square root of the frequency domain asymmetry factor, and then performs a Hadamard product with the interpolation surface to achieve local stress modulation at each point of the surface, enhancing the correlation between stress distribution and frequency domain features. The modulated intermediate surface is then accumulated and summed using sign perception along two orthogonal directions, with the accumulation direction at a 45-degree angle to the physiological direction from the colon to the ileum. The accumulated value is reset when the sign changes, thereby capturing the stress accumulation and interruption patterns along the physiological direction. The geometric average of the accumulated fields in the two directions generates a two-dimensional nonlinear convolution kernel that reflects the directional transmission and interruption characteristics of stress. The convolution kernel and the original two-dimensional interpolation surface are superimposed with median weighting within a sliding window. The weight of the convolution kernel within the window is dynamically determined by the product of the frequency domain asymmetry factor at the center point of the window and the local stress variation coefficient, so that the convolution process can adapt to the frequency domain characteristics and stress fluctuation degree of different regions. The results of the center points of each window after superposition constitute a two-dimensional liquid crystal phase transition convolution field. Overall, it realizes multi-scale, direction-sensitive and nonlinear convolution fusion of intestinal stress distribution, which enhances the ability to characterize complex physiological stress patterns and maintain spatial continuity.

[0033] Example 7: Based on Example 6, the process of performing median-weighted superposition of the two-dimensional nonlinear convolution kernel and the original two-dimensional interpolation surface within a sliding window in step S2023 of this embodiment of the invention specifically includes the following steps: Step S20231: Using each grid point in the original two-dimensional interpolation surface as the center point of the sliding window, extract all grid points within a predetermined radius adjacent to the center point to form a window region; multiply the value of each grid point in the window region with the kernel value at the corresponding position in the two-dimensional nonlinear convolution kernel to obtain the weighted value at each position in the window, and the weighted value set of the sliding window is formed by all the weighted values. Step S20232: Sort the weighted numerical set according to the numerical size, and take the value in the middle position after sorting as the median candidate value of the sliding window; at the same time, calculate the product of the local variation coefficient of the stress template in the sliding window and the frequency domain asymmetry factor of the window center point, and use the product as the confidence weight of the median candidate value. The weighted median set is composed of the confidence weights of all windows and the median candidate values. Step S20233: Multiply the candidate median value in the weighted median set of each window by its corresponding confidence weight, and then divide by the sum of all confidence weights in the window to obtain the normalized weighted median of the window center point; arrange the normalized weighted median of all center points according to the grid position of the original two-dimensional interpolation surface to output the two-dimensional liquid crystal phase transition convolution field.

[0034] The local coefficient of variation (LCV) is the ratio of the standard deviation to the mean of all stress values ​​in the intestinal axial stress distribution template within the sliding window region. This ratio quantifies the relative dispersion of stress distribution within the window. A larger LCV indicates more drastic stress changes within the window, while a smaller LCV indicates a more uniform stress distribution. The coefficient is derived from a pre-stored intestinal axial stress distribution template and is used to adjust the confidence weight of the window center point. The frequency domain asymmetry factor is the calculated frequency domain asymmetry factor corresponding to each microbial component, representing the degree of asymmetry in the frequency domain of the temperature threshold sequence within that component. This factor is assigned to the window center point as the frequency domain characteristic value of the microbial component containing the center point, and is multiplied by the LCV to jointly determine the confidence weight of the median candidate value. The confidence weight is a numerical coefficient calculated by multiplying the local coefficient of variation of the stress template within the sliding window by the frequency domain asymmetry factor of the window's center point. This coefficient measures the reliability of the median candidate value within the sliding window; a higher confidence weight indicates greater importance of the median candidate value in the subsequent normalized weighted median calculation. The confidence weight is derived from the product of the local coefficient of variation and the frequency domain asymmetry factor, coupling the mechanical distribution characteristics with the frequency domain characteristics of the microbial components. The median candidate value is the value at the middle of the weighted numerical set of the sliding window, sorted by value. The weighted numerical set is obtained by multiplying the value of each grid point within the window region by the corresponding kernel value in the two-dimensional nonlinear convolution kernel. The median candidate value represents the central tendency of the window when extreme values ​​are ignored and is used in conjunction with the confidence weight to calculate the normalized weighted median of the window's center point.

[0035] In the above embodiments, this embodiment achieves the following technical effects by superimposing a two-dimensional nonlinear convolution kernel and the original two-dimensional interpolation surface using median weighting within a sliding window: First, the local weighting calculation based on the sliding window can effectively suppress high-frequency noise and outliers in the surface while preserving edge and texture details; by multiplying the values ​​of each grid point within the window with the corresponding kernel value, the nonlinear modulation effect of the convolution kernel in the local region is strengthened, making the weighted numerical set more consistent with the actual physical distribution characteristics. Second, the calculation mechanism of median candidate values ​​combined with confidence weights enhances the algorithm's adaptability to local stress variations and frequency domain asymmetry features; by dynamically adjusting the reliability of the median values ​​in each window through the product of the local variation coefficient and the frequency domain asymmetry factor, the output values ​​have higher robustness and representativeness in regions with stress concentration or significant frequency domain features. Finally, through point-by-point calculation and global reconstruction of the normalized weighted median, the generated two-dimensional liquid crystal phase transition convolution field significantly improves the physical consistency of the local region while maintaining overall smoothness; it can more accurately reflect the spatial non-uniformity of the phase transition process and provide a discretized numerical distribution with clear physical meaning for analysis.

[0036] Example 8: As Figure 5 As shown, based on Example 1, the process of allocating each microbial component to a matching intestinal axial region in step S300 of this embodiment of the invention specifically includes the following steps: Step S301: Use the gray value of each pixel in the liquid crystal phase transition spectrum as the weighting coefficient of the phase transition feature vector of the microbial component at the location, multiply it by the product of the local shear modulus and viscosity coefficient of the corresponding grid node to obtain the weighted thermodynamic response value at each grid node; arrange the weighted thermodynamic response values ​​of all grid nodes in the spatial order of the network nodes to form a one-dimensional response curve. Step S302: Divide the one-dimensional response curve into several continuous segments according to the preset boundary of the intestinal axial region, accumulate and sum the weighted thermodynamic response values ​​of all nodes in each segment to obtain the total response value of each segment; then calculate the ratio of the total response values ​​of two adjacent segments to obtain the response gradient sequence. Step S303: The phase transition feature vectors corresponding to each component in the microbial component sequence are sequentially matched with the response gradient sequence in the order of separation. The matching method is to calculate the absolute value of the difference between the magnitude of each phase transition feature vector and each gradient value in the response gradient sequence, and take the intestinal axial region corresponding to the gradient value with the smallest absolute value of the difference as the component allocation region. The functional partition twin mapping of the microbial community along the length of the intestine is generated from all allocation results.

[0037] In the above embodiments, this embodiment calculates the weighted thermodynamic response value based on the product of the grayscale weight of the liquid crystal phase transition spectrum and the local mechanical parameters, coupling the phase transition characteristics of microbial components with the local physical state of the intestine. By forming a one-dimensional response curve, the complex spatially distributed phase transition information is transformed into a response signal that changes continuously along the intestinal axis, providing a physically meaningful quantitative basis for region division. Secondly, by segmenting the one-dimensional response curve and calculating the ratio of the total response values ​​of adjacent segments, the generated response gradient sequence can characterize the degree of difference in thermodynamic behavior between different axial regions of the intestine. The gradient expression method highlights the transition characteristics of the physical state of the intestine along the length direction, providing a dynamically changing reference scale for the spatial matching of microbial components. Ultimately, by using a deviation matching mechanism between the phase transition eigenvector magnitude and the response gradient value, a precise correspondence between microbial components and the axial region of the gut was achieved. The matching process is based on the principle of minimizing physical response differences, allowing different microbial components to be allocated to the gut segment most suited to the physical environment according to their phase transition characteristics, thereby forming a functional partition twin mapping of the microbial community along the length of the gut. The mapping relationship reflects the intrinsic correlation between microbial distribution and the local physical state of the gut, providing a structured model for understanding the spatial heterogeneity of the gut microenvironment.

[0038] Example 9: Based on Example 8, the process of taking the intestinal axial region corresponding to the gradient value with the smallest absolute difference as the component distribution region in step S303 of this embodiment of the invention specifically includes the following steps: Step S3031: Record the start and end positions of the corresponding intestinal axial region in the allocation results of each microbial component as the left and right boundary values ​​of the component, respectively; at the same time, take the difference between the maximum and minimum temperature threshold values ​​in the phase transition feature vector of the component as the feature span of the component; arrange the left boundary values, right boundary values ​​and feature spans of all components in the order of component separation to form an initial partitioning parameter table; Step S3032: For two adjacent components in the initial segmentation parameter table, calculate the difference between the right boundary value of the previous component and the left boundary value of the subsequent component as the boundary gap; then calculate the reciprocal of the product of the characteristic span of the previous component and the characteristic span of the subsequent component as the fusion factor; multiply the boundary gap by the fusion factor to obtain the boundary adjustment amount, and use the boundary adjustment amount to correct the right boundary value of the previous component and the left boundary value of the subsequent component respectively to obtain the adjusted segmentation boundary set; Step S3033: Using the length of the intestine as the horizontal axis and each boundary position in the adjusted partition boundary set as the segmentation point, divide the entire length of the intestine into continuous segments. Fill each segment with the geometric mean of all temperature thresholds in the phase transition feature vector of its corresponding component, and output a numerical curve that varies along the length of the intestine. The numerical curve is a twin mapping of the functional partitions of the microbial community along the length of the intestine.

[0039] In the above embodiments, this embodiment forms an initial partitioning parameter table by recording the left and right boundary values ​​and characteristic spans of the components, and structurally associates the spatial location information of microbial components with the temperature range of their phase transition characteristics; this provides a basic data framework for boundary optimization, ensuring that the allocation region of each component has a clear geometric location and characteristic quantitative description. Secondly, based on the calculation of boundary gaps and fusion factors, the boundaries of adjacent components are dynamically adjusted; the boundary gap reflects the continuity of spatial distribution, while the fusion factor characterizes the coupling strength of the characteristic spans of adjacent components; by multiplying the two to obtain the boundary adjustment amount, the boundary position is corrected, making the regional separation between components more consistent with the actual physical transition characteristics, avoiding spatial discontinuities or overlaps caused by initial matching. Finally, by using the adjusted boundary set as segmentation points, the intestinal length was divided into continuous segments, and the geometric mean of the corresponding component temperature threshold was filled into each segment. The resulting numerical curve fully expressed the functional compartmentalization distribution of the microbial community along the intestinal axis. The curve not only preserved the spatial separation characteristics between components, but also smoothed the fluctuations of intra-regional characteristics by introducing the geometric mean, forming a continuous mapping that reflects the gradient change of microbial functional characteristics along the intestinal length, providing a quantitative basis for analyzing the spatial functional heterogeneity of the intestinal microenvironment.

[0040] Example 10: Based on Example 9, the process of filling in the geometric mean of all temperature thresholds in the phase transition characteristic vector of the corresponding component in each segment in step S3033 of this embodiment of the invention specifically includes the following steps: Step S30331: Based on the intestinal length coordinate values ​​corresponding to each boundary position in the adjusted segment boundary set, determine the start index and end index of each segment in the storage array of the output numerical curve to obtain a segment index table containing the start index and end index; Step S30332: For each segment in the segment index table, extract the continuous memory block occupied by all temperature thresholds in the phase transition feature vector of the corresponding component of the segment, and perform a bitwise XOR operation on each threshold in the continuous memory block with the obtained frequency domain asymmetry factor of the component to obtain a set of XOR results; then take the bitwise AND operation on the XOR results to obtain a single integer, and use the integer as the representative value of the segment. Step S30333: Copy the representative value of each segment to each element position from the start index to the end index of the segment in the output numerical curve storage array; after all segments are filled, the output array serves as a numerical curve that changes along the length of the intestine.

[0041] In the above embodiments, this embodiment determines the index range of segments in the storage array based on the intestinal length coordinates, establishing a precise correspondence between the intestinal physical space and the data storage space. Through the generation of the segment index table, it ensures that the intestinal segment corresponding to each microbial component has a clear and continuous data position in the numerical curve, providing a structured access path for numerical filling. Secondly, by performing bitwise XOR and bitwise AND operations on the temperature threshold and frequency domain asymmetry factor in the phase transition feature vector, the multidimensional temperature features are compressed into a single integer representative value. This not only achieves data dimensionality reduction but also integrates frequency domain asymmetry features through bitwise operations, making the representative value simultaneously contain information on temperature threshold distribution and frequency domain characteristics, enhancing the physical representation capability of the numerical value. Finally, by filling the array elements of the corresponding index range with the representative value of each segment, the generated numerical curve maintains the continuity of the intestinal axis while completely preserving the fusion characteristics of each microbial component. The curve, as a functional partition twin mapping of the microbial community along the intestinal length direction, provides a one-dimensional numerical representation with clear physical meaning and computational efficiency, facilitating spatial feature analysis and functional modeling of the intestinal microenvironment.

[0042] Example 11: Based on Example 10, the process of performing a bitwise XOR operation between each threshold in the continuous memory block and the obtained frequency domain asymmetry factor of the component in step S30332 of this embodiment of the invention specifically includes the following steps: Step S303321: Take each temperature threshold and the frequency domain asymmetry factor of the component in the continuous memory block as the x and y coordinates of the rectangular coordinates, respectively, construct complex points on the complex plane, and extract the principal argument value of the point; then multiply the principal argument value by the integer part of the characteristic span of the component and round it to obtain the angle code integer corresponding to each threshold. Step S303322: Input each angle-coded integer sequentially into a feedback shift register with the difference between the left and right boundary values ​​of the component as the modulus. The feedback method of the feedback shift register is to divide the product of the current input value and the previous output value by the modulus and take the remainder. The initial value of the feedback shift register is set to the first three digits of the fractional part of the frequency domain asymmetry factor of the component. Output a set of coded sequences with the same length as the input. Step S303323: Cyclicly permutate the decimal part of the reciprocal of the frequency domain asymmetry factor of each encoded value in the encoded sequence with the digit sequence after the decimal point; the permutation rule is to take an equal length segment from the digit sequence with each encoded value as the starting position, add the extracted digit segment to the original encoded value bit by bit and take the units digit to obtain the nonlinear mixing value corresponding to each threshold, and form a set of mixing results in order of all the mixing values.

[0043] In the above embodiments, this embodiment maps the temperature threshold and the frequency domain asymmetry factor to arguments on the complex plane and encodes them as integers, converting the threshold information into angular features to enhance the data's discriminability in the phase dimension. A feedback shift register with the component boundary difference as the modulus is used to process the angularly encoded integers, generating an encoded sequence with pseudo-random characteristics, improving the sequence's unpredictability and nonlinear complexity. By performing cyclic permutation and bitwise addition operations on the encoded sequence and the digital sequence of the reciprocal of the frequency domain asymmetry factor, a deep nonlinear mixture of threshold data and component features is achieved, enhancing the data obfuscation level and resistance to analysis. This realizes multi-level transformation and obfuscation of temperature threshold data, strengthening the hidden correlations and structural complexity between data, and providing a mixed result with high entropy characteristics for subsequent processing.

[0044] Example 12: Based on Example 11, the process of extracting the principal argument values ​​of complex points after constructing complex points on the complex plane in step S303321 of this embodiment of the invention specifically includes the following steps: Step S3033211: Take the temperature threshold as the radial length on the complex plane and the frequency domain asymmetry factor as the rotation angle on the complex plane; take the cosine of the radial length multiplied by the rotation angle as the real part value and the sine of the radial length multiplied by the rotation angle as the imaginary part value, and form an initial complex point by the real part value and the imaginary part value. Step S3033212: Multiply the real part and imaginary part of the initial complex point by the reciprocal of the sum of the left and right boundary values ​​of the group, respectively, to obtain the scaled real part and scaled imaginary part; a scaled complex point is formed by the scaled real part and scaled imaginary part. Step S3033213: Calculate the ratio of the imaginary part to the real part of the scaled complex point, then take the arctangent of the ratio, and use the result as the principal argument value of the complex point.

[0045] In the above embodiments, the temperature threshold and frequency domain asymmetry factor are mapped to radial length and rotation angle on the complex plane, respectively, and initial complex points are constructed to achieve a linear transformation from the original data to the complex domain, preserving the basic correspondence between the threshold magnitude and the factor direction. The real and imaginary parts of the complex points are simultaneously scaled using the reciprocal of the sum of component boundary values, ensuring that the distribution range of the complex points is constrained by the component boundary characteristics, thus enhancing the correlation between the data and the component structure. The principal argument value is obtained by calculating the arctangent of the ratio of the imaginary to the real part of the scaled complex points, transforming the geometric information of the complex points into angular features, eliminating amplitude influence, and highlighting phase information. Mapping the combination of threshold and factor to a principal argument value with clear geometric meaning provides a normalized phase data foundation related to the component boundaries for angle encoding.

[0046] Example 13: Based on Example 12, the process of constructing an initial complex point from the real and imaginary part values ​​in step S3033211 of this embodiment of the invention specifically includes the following steps: Step S30332111: Multiply the calculated real part value and imaginary part value by the reciprocal of the characteristic span of the component, respectively, to obtain the adjusted real part value and the adjusted imaginary part value; Step S30332112: Write the adjusted real part value to the first storage cell of a contiguous storage area, and write the adjusted imaginary part value to the second storage cell of the same contiguous storage area, so that the two storage cells are adjacent in physical address. Step S30332113: Use the starting address of the contiguous storage area as the identifier of the complex point. The contents of the two adjacent storage units pointed to by the starting address together constitute an initial complex point.

[0047] In the above embodiments, this embodiment normalizes the complex components by multiplying the real and imaginary values ​​by the reciprocal of the component feature span, thus associating the distribution of complex points with the component feature scale and enhancing the structural adaptability of the data. The adjusted real and imaginary values ​​are written into contiguous storage units with adjacent physical addresses, ensuring the locality and access efficiency of complex point data in memory and supporting efficient continuous data access. Using the starting address of the contiguous storage area as the complex point identifier, the real and imaginary data are associated through address references, simplifying the data structure representation of complex points, reducing storage overhead, and improving the addressing performance of subsequent processing. This achieves normalized storage and compact representation of complex point data, providing a data foundation with memory continuity and structural consistency for complex number operations.

[0048] Example 14: As Figure 6 As shown, based on Examples 1-13, the intestinal microbiome dynamic detection system based on digital twin provided in this embodiment of the invention includes: The hydrophobic gradient arrangement module 1 is used to mix newly collected intestinal contents samples with magnetic nanoparticles with alkyl chains of different lengths on their surface. Under the action of a gradient alternating magnetic field, based on the compatibility between the hydrophobicity of the microbial cell membrane and the alkyl chains, microorganisms with different hydrophobicities are sequentially released from the magnetic capture area and collected in the order of release, resulting in a sequence of microbial components arranged according to the hydrophobic gradient of the microbial cell membrane. Phase transition map generation module 2 is used to introduce each component in the microbial component sequence into a micro-cavity coated with liquid crystal films with different phase transition temperatures, and to record the temperature threshold when each liquid crystal film changes from an ordered phase to a disordered phase by linearly varying the infrared light irradiation of the micro-cavity. After sorting by temperature threshold, phase transition feature vectors corresponding to each component are generated, and the liquid crystal phase transition map of the microbial community is formed by combining the phase transition feature vectors of all components. The mechanical deviation response module 3 is used to input the liquid crystal phase transition spectrum into the intestinal wall multilayer mechanical parameter program reconstructed by ultrasound elastography. Each grid node stores the local shear modulus and viscosity coefficient. By calculating the deviation between each phase transition feature vector in the liquid crystal phase transition spectrum and the expected thermodynamic response at the grid node, the module allocates each microbial component to the matching intestinal axial region and generates a functional partition twin mapping of the microbial community along the length of the intestine based on the allocation relationship.

[0049] In the above embodiments, the hydrophobic gradient alignment module of this embodiment combines magnetic nanoparticles with alkyl chains of different lengths modified on their surfaces with a gradient alternating magnetic field to achieve physical separation and sequential collection based on the hydrophobicity of microbial cell membranes. This yields a sequence of microbial components arranged according to a hydrophobic gradient, providing a sample basis with ordered structure and clearly distinguishable physical properties. The phase transition map formation module utilizes microcavities coated with liquid crystal films of different phase transition temperatures and linearly varying infrared light irradiation to convert the temperature response of each component into a phase transition temperature threshold and generate a phase transition feature vector. This constructs a liquid crystal phase transition map of the microbial community, achieving a quantitative mapping from microbial components to thermodynamic response characteristics, forming a digitally characterizable description of the community's functional state. The mechanical deviation response module combines liquid crystal phase transition maps with a program for reconstructing multilayer mechanical parameters of the intestinal wall based on ultrasound elastography. It calculates the deviation between the phase transition feature vector and the expected thermodynamic response of local mechanical nodes. Based on the deviation matching results, it allocates microbial components to the corresponding axial regions of the intestine, generating a functional partition twin mapping of the microbial community along the length of the intestine. This realizes the dynamic spatial association between microbial functional characteristics and the local mechanical environment of the intestine, providing a structured digital twin model for microbial-host interactions.

[0050] Figure 7 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown.

[0051] The electronic device may include a central processing unit / microprocessor / main control chip 4; and a storage medium 5 coupled to the central processing unit / microprocessor / main control chip 4 and storing computer-executable instructions therein for performing the steps of various methods of embodiments of the present invention when executed by the processor.

[0052] The central processing unit / microprocessor / main control chip 4 may include, but is not limited to, one or more processors or microprocessors.

[0053] Storage medium 5 may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0054] In addition, the electronic device may include (but is not limited to) a data bus 6, an input / output bus / external bus / device bus 7, a display 8, and input / output devices 9 (e.g., keyboard, mouse, speaker, etc.).

[0055] The central processing unit / microprocessor / main control chip 4 can communicate with external devices (8, 9, etc.) via wired or wireless networks (not shown) through the input / output bus / external bus / device bus 7.

[0056] The storage medium 5 may also store at least one computer-executable instruction for performing the steps of various functions and / or methods in the embodiments described herein when the central processing unit / microprocessor / main control chip 4 is running.

[0057] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0058] Figure 8 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0059] like Figure 8 As shown, the non-transitory computer-readable storage medium 11 stores instructions, such as computer-readable instructions 10. When the computer-readable instructions 10 are executed by a processor, the various methods described above can be performed. The non-transitory computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium 11 can be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions 10 stored on the non-transitory computer-readable storage medium 11, the various methods described above can be performed.

[0060] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0061] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0062] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0063] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of the present invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0064] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic detection of gut microbiota based on digital twins, characterized in that, Includes the following steps: The phase transition feature vectors of each component in the microbial component sequence are combined to form a liquid crystal phase transition map of the microbial community. The liquid crystal phase transition map is input into a multilayer mechanical parameter program of the intestinal wall reconstructed by ultrasound elastography. Each grid node stores the local shear modulus and viscosity coefficient. The deviation between each phase transition feature vector in the liquid crystal phase transition map and the expected thermodynamic response at the grid node is calculated. Each microbial component is assigned to a matching intestinal axial region, and the functional partition twin mapping of the microbial community along the length of the intestine is generated from the allocation relationship.

2. The method for dynamic detection of gut microbiota based on digital twins as described in claim 1, characterized in that, The process of allocating each microbial component to a matching axial region of the gut includes the following steps: The gray value of each pixel in the liquid crystal phase transition spectrum is used as the weighting coefficient of the phase transition feature vector of the microbial component at the location. This weighting coefficient is multiplied by the product of the local shear modulus and viscosity coefficient of the corresponding grid node to obtain the weighted thermodynamic response value at each grid node. The weighted thermodynamic response values ​​of all grid nodes are arranged in the spatial order of the network nodes to form a one-dimensional response curve. The one-dimensional response curve is divided into several continuous segments according to the preset boundary of the intestinal axial region. The weighted thermodynamic response values ​​of all nodes in each segment are accumulated and summed to obtain the total response value of each segment. Then, the ratio of the total response values ​​of two adjacent segments is calculated to obtain the response gradient sequence. The phase transition feature vectors corresponding to each component in the microbial component sequence are sequentially matched with the response gradient sequence in the order of separation. The matching method is to calculate the absolute value of the difference between the magnitude of each phase transition feature vector and each gradient value in the response gradient sequence, and take the intestinal axial region corresponding to the gradient value with the smallest absolute value of the difference as the component allocation region. The functional partition twin mapping of the microbial community along the length of the intestine is generated from all allocation results.

3. The method for dynamic detection of gut microbiota based on digital twins as described in claim 2, characterized in that, The process of selecting the intestinal axial region corresponding to the gradient value with the smallest absolute difference as the component allocation region includes the following steps: The starting and ending positions of the corresponding intestinal axial region in the allocation results of each microbial component are recorded as the left and right boundary values ​​of the component, respectively; at the same time, the difference between the maximum and minimum temperature threshold values ​​in the phase transition feature vector of the component is used as the feature span of the component; the left and right boundary values ​​and feature spans of all components are arranged in the order of component separation to form an initial partitioning parameter table; For two adjacent components in the initial segmentation parameter table, the difference between the right boundary value of the preceding component and the left boundary value of the following component is calculated as the boundary gap; then the reciprocal of the product of the characteristic span of the preceding component and the characteristic span of the following component is calculated as the fusion factor; the boundary gap is multiplied by the fusion factor to obtain the boundary adjustment amount, and the right boundary value of the preceding component and the left boundary value of the following component are corrected by the boundary adjustment amount to obtain the adjusted segmentation boundary set; Using the length of the intestine as the horizontal axis and each boundary position in the adjusted partition boundary set as the segmentation point, the entire length of the intestine is divided into continuous segments. The geometric mean of all temperature thresholds in the phase transition feature vector of its corresponding component is filled into each segment, and a numerical curve that varies along the length of the intestine is output. The numerical curve is a twin mapping of the functional partitions of the microbial community along the length of the intestine.

4. The method for dynamic detection of gut microbiota based on digital twins as described in claim 3, characterized in that, The process of filling in the geometric mean of all temperature thresholds in the phase transition eigenvector of the corresponding component within each segment includes the following steps: Based on the intestinal length coordinates corresponding to each boundary position in the adjusted segment boundary set, the start and end indices of each segment are determined in the storage array of the output numerical curves, resulting in a segment index table containing the start and end indices. For each segment in the segment index table, take out the contiguous memory block occupied by all temperature thresholds in the phase transition feature vector of the corresponding component of the segment, and perform a bitwise XOR operation on each threshold in the contiguous memory block with the obtained frequency domain asymmetry factor of the component to obtain a set of XOR results; then take the bitwise AND operation on the XOR results to obtain a single integer, and use the integer as the representative value of the segment. The representative value of each segment is copied to each element position in the output numerical curve storage array from the segment start index to the end index; after all segments are filled, the output array serves as a numerical curve that varies along the length of the intestine.

5. The method for dynamic detection of gut microbiota based on digital twins as described in claim 4, characterized in that, The process of performing a bitwise XOR operation between each threshold in a contiguous memory block and the frequency domain asymmetry factor of the obtained component includes the following steps: Each temperature threshold and the frequency domain asymmetry factor of the component in the continuous memory block are used as the x and y coordinates of the rectangular coordinate system, respectively. Complex points are constructed on the complex plane, and the principal argument value of the complex points is extracted. Then, the principal argument value is multiplied by the integer part of the characteristic span of the component and rounded to obtain the angle code integer corresponding to each threshold. Each angle-coded integer is sequentially input into a feedback shift register with the difference between the left and right boundary values ​​of the component as the modulus. The feedback method of the feedback shift register is to divide the product of the current input value and the previous output value by the modulus and take the remainder. The initial value of the feedback shift register is set to the first three digits of the fractional part of the frequency domain asymmetry factor of the component, and a set of coded sequences with the same length as the input is output. The sequence of digits is cyclically permuted with the decimal part of the reciprocal of the frequency domain asymmetry factor of the component for each coded value. The permutation rule is to take an equal-length segment from the digit sequence starting from each coded value, add the extracted digit segment to the original coded value bit by bit, and take the units digit to obtain the nonlinear mixing value corresponding to each threshold. All the mixing values ​​are arranged in order to form a set of mixing results.

6. The method for dynamic detection of gut microbiota based on digital twins as described in claim 5, characterized in that, The process of extracting the principal argument values ​​of complex points after constructing them on the complex plane includes the following steps: The temperature threshold is taken as the radial length on the complex plane, and the frequency domain asymmetry factor is taken as the rotation angle on the complex plane; the cosine of the radial length multiplied by the rotation angle is taken as the real part value, and the sine of the radial length multiplied by the rotation angle is taken as the imaginary part value. An initial complex point is formed by the real part value and the imaginary part value. Multiply the real and imaginary values ​​of the initial complex point by the reciprocal of the sum of the left and right boundary values ​​of that group, respectively, to obtain the scaled real and imaginary values; a scaled complex point is formed by the scaled real and imaginary values. Calculate the ratio of the imaginary part to the real part of the scaled complex point, then take the arctangent of the ratio, and use the result as the principal argument value of the complex point.

7. The method for dynamic detection of gut microbiota based on digital twins as described in claim 6, characterized in that, The process of constructing an initial complex point from the real and imaginary parts includes the following steps: Multiply the calculated real and imaginary part values ​​by the reciprocal of the characteristic span of the component to obtain the adjusted real and imaginary part values. Write the adjusted real part value to the first memory cell of a contiguous memory area, and write the adjusted imaginary part value to the second memory cell of the same contiguous memory area, so that the two memory cells are adjacent in physical address. The starting address of a contiguous storage area is used as the identifier of a complex point. The contents of two adjacent storage units pointed to by the starting address together constitute an initial complex point.

8. The method for dynamic detection of gut microbiota based on digital twins as described in claim 1, characterized in that, Newly collected intestinal contents samples were mixed with magnetic nanoparticles with alkyl chains of different lengths on their surface. Under the action of a gradient alternating magnetic field, based on the compatibility between the hydrophobicity of the microbial cell membrane and the alkyl chain, microorganisms with different hydrophobicities are sequentially released from the magnetic capture area and collected in the order of release, resulting in a sequence of microbial components arranged according to the hydrophobicity gradient of the microbial cell membrane.

9. The method for dynamic detection of gut microbiota based on digital twins as described in claim 8, characterized in that, Each component in the microbial component sequence was introduced into a microcavity coated with liquid crystal films with different phase transition temperatures. The temperature threshold at which each liquid crystal film transitioned from an ordered phase to a disordered phase was recorded by linearly varying the infrared light irradiation of the microcavity. After sorting by temperature threshold, phase transition feature vectors corresponding to each component were generated. The liquid crystal phase transition map of the microbial community was formed by combining the phase transition feature vectors of all components.

10. A digital twin-based system for dynamic detection of gut microbiota, used to implement the digital twin-based method for dynamic detection of gut microbiota as described in any one of claims 1 to 9, characterized in that, include: A hydrophobic gradient arrangement module is used to mix newly collected intestinal contents samples with magnetic nanoparticles with alkyl chains of different lengths on their surface. Under the action of a gradient alternating magnetic field, based on the compatibility between the hydrophobicity of the microbial cell membrane and the alkyl chain, microorganisms with different hydrophobicities are sequentially released from the magnetic capture area and collected in the order of release, resulting in a sequence of microbial components arranged according to the hydrophobicity gradient of the microbial cell membrane. The phase transition map generation module is used to introduce each component in the microbial component sequence into a microcavity coated with liquid crystal films with different phase transition temperatures. The microcavity is irradiated with infrared light in a linear manner, and the temperature threshold when each liquid crystal film changes from an ordered phase to a disordered phase is recorded. After sorting by temperature threshold, the phase transition feature vectors corresponding to each component are generated. The liquid crystal phase transition map of the microbial community is formed by combining the phase transition feature vectors of all components. The mechanical deviation response module is used to input the liquid crystal phase transition spectrum into a multilayer mechanical parameter program of the intestinal wall reconstructed by ultrasound elastography. Each grid node stores the local shear modulus and viscosity coefficient. By calculating the deviation between each phase transition feature vector in the liquid crystal phase transition spectrum and the expected thermodynamic response at the grid node, the module allocates each microbial component to the matching intestinal axial region and generates a functional partition twin map of the microbial community along the length of the intestine based on the allocation relationship.

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