379 results about "Metabolic activity" patented technology

The invention relates to the technical field of the Internet of Things, in particular to a rice wine fermentation monitoring system based on the Internet of Things, in the rice wine fermentation monitoring system, a model construction module fuses a material heat conduction and gas diffusion equation to form a physical field calculation substrate, and a metabolic environment dynamic coupling function is embedded to generate a joint calculation model; the inversion analysis module takes temperature and humidity data of the sensor as boundary constraint, microbial metabolic rate parameters are iteratively corrected through reverse parameter optimization, a global metabolic activity spatial distribution field is generated, and the limitation of traditional point-like monitoring on spatial heterogeneity perception is broken through; the deduction and prejudgment module initializes a model through the active field, outputs a double-change curve of the temperature field and the active gradient, captures a critical mutation point, and then generates a targeted control signal based on space coordinates and a prejudgment time window, so that advanced regulation and control of the key turning point are realized, and the fermentation stability and the flavor quality are maintained.

The invention discloses a composite denitrification accelerant and a preparation method thereof, and belongs to the technical field of sewage treatment. The carbon source composite material is prepared from the following raw materials in parts by weight: 30-50% of a carbon source, 2-5% of vitamins, 2-5% of amino acids, 1-3% of a biological stimulant, 1-2% of signal molecules, 1-5% of metal elements, 3-5% of a pH regulator and 25-60% of water. Through metabolites such as vitamins, amino acids and biological stimulants, growth and reproduction of denitrifying bacteria are promoted, and functions of related enzymes in the denitrification process are improved. The signal molecule optimizes the metabolic activity of bacteria by regulating and controlling the expression of genes, related to the denitrification process, of the denitrifying bacteria. The metal ions can enhance the activity of the denitrifying enzyme, so that the efficiency of denitrification treatment is improved. According to the denitrification accelerant, the denitrification process is promoted through the synergistic effect of three aspects of growth and reproduction of denitrifying bacteria, expression of denitrification genes and improvement of activity of denitrification-related enzymes.

The invention relates to the technical field of material testing, and particularly discloses an intelligent quality detection method and system for a micro-mineral bio-organic fertilizer, local thermal excitation is applied to a fertilizer sample through a micro-area thermal pulse excitation device, a gas release kinetic curve and a spectrum change track are synchronously collected, and a gas spectrum coupling data cube is constructed; performing differential transformation and modal decomposition on the time sequence feature set, extracting transient response feature vectors and generating an activity response distribution diagram; establishing component-activity correlation analysis, and decoupling biological metabolic activity and matrix background interference through feature separation and a comparative learning strategy to obtain a dynamic metabolic fingerprint; constructing a multi-dimensional feature space by using the dynamic metabolic fingerprints and the apparent characteristic parameters, and performing information aggregation and feature reconstruction by using a graph convolutional network to generate a comprehensive quality index; and finally, realizing quality grade judgment based on a quality characteristic pyramid structure, and establishing a dynamic early warning mechanism by analyzing time sequence evolution characteristics of the activity response distribution diagram.

The invention relates to the technical field of component monitoring, and discloses a compost fermentation process material component intelligent monitoring method comprising the following steps: collecting compost process environment data and compost process material component data in a compost process, and carrying out thermophilic bacteria metabolic activity on-line prediction to obtain a flora metabolic activity index; receiving the composting process environment data, the composting process material component data and the flora metabolism activity index by using the lightweight material component inversion model, and generating an optimal material component ratio; and comprehensively adjusting the material components in the composting fermentation process by using the optimal material component ratio. According to the method, environmental data and material component data in the composting process are collected in real time, the metabolic activity index of thermophilic bacteria is predicted on line, the optimal material component proportion is generated on the basis of a lightweight material component inversion model in combination with the environmental data, the material component data and the metabolic activity index, and the material components in the composting fermentation process are comprehensively adjusted, so that the composting fermentation efficiency is improved. Therefore, the composting effect is optimized.

The invention discloses a multi-modal image auxiliary diagnosis system based on endometriosis and a medium. The system comprises a determination module, a diagnosis module and a diagnosis module, wherein the determination module is used for determining focus position information, a metabolic activity analysis result and hemodynamic characteristic data according to a near infrared spectrum image, a magnetic resonance image and an ultrasonic image of a human body; the extraction module is used for extracting lesion feature information from different modal images; the optimization and reconstruction module generates a focus multi-modal image according to the focus feature information, processes the focus multi-modal image to obtain a focus three-dimensional model, and obtains a focus morphological feature quantized value based on the focus three-dimensional model; and the diagnosis module inputs the medical record related information, the focus morphological characteristic quantized value, the metabolic activity analysis result and the hemodynamic characteristic data into a preset focus comprehensive diagnosis model, and outputs a diagnosis result of endometriosis. According to the method, the multidimensional information is analyzed through the preset focus comprehensive diagnosis model, and accurate diagnosis of endometriosis is realized.

The invention provides a monascus drying degree real-time control method which comprises the following steps: monitoring the surface moisture content, internal metabolic activity index and pigment content change of mycelium in a monascus drying process, and generating a mycelium state distribution diagram; extracting moisture gradient characteristics and metabolic activity parameters from the mycelium state distribution diagram, analyzing a relationship between a moisture evaporation rate and mycelium activity, and determining a dynamic trend of moisture gradient and activity change; analyzing the dynamic trend of the moisture gradient and the activity change, determining the relationship between the moisture gradient and the activity change through a fitting curve, and predicting the change trend of the moisture and the activity of the mycelium in different drying stages; aiming at a moisture distribution non-uniformity index, the uniform distribution of heat in the red yeast rice is improved by optimizing an air supply mode, so that the moisture evaporation rate deviation is reduced, and a temperature and humidity control sequence is obtained.

The invention discloses a mutton quality index intelligent hyperspectral image detection method, and belongs to the technical field of hyperspectral image detection.The mutton quality index intelligent hyperspectral image detection method comprises the steps that the surface of a mutton sample is scanned by rotating a polarized light source, and a muscle fiber four-direction light field matrix is generated. A three-dimensional muscle fiber topological graph is constructed, Gaussian curved surface fitting and iterative optimization of an energy function are combined, segmentation precision and robustness of geometric modeling are remarkably improved, reliability and multi-scale adaptability of muscle fiber spatial distribution analysis are improved, and by fusing the sarcomedo contraction state, intermuscular fat marks and metabolic activity, the segmentation accuracy is improved. And a tenderness-freshness comprehensive decision is generated through dynamic weight distribution and a nonlinear scoring formula, so that multi-dimensional data complementation is realized, and the comprehensiveness and automatic decision-making capability of quality grading are improved.

The invention discloses an application of a compound in preparation of a product for enhancing the metabolic activity of hematopoietic stem cells and treating metachromatic white matter dystrophy. The compound comprises Azoramide. Azoramide is adopted to treat transfected hematopoietic stem cells, and cell apoptosis, cell necrosis and mitochondrial respiration conditions of the hematopoietic stem cells are obviously improved, so that the hematopoietic stem cells are expected to be applied to enhancement of transfection efficiency and metabolic activity of the hematopoietic stem cells transfected by the ARSA gene and reduction of ROS generation, and the effectiveness of the hematopoietic stem cells in treatment of MLD diseases is enhanced.

The invention belongs to the technical field of soil heavy metal pollution remediation, and discloses a heavy metal pollution soil bioremediation method and system based on soil metagenome sequencing, and the method comprises the following steps: carrying out gene sequencing on experimental microorganism class groups in heavy metal pollution soil to determine functional microorganisms containing heavy metal resistance genes; heavy metal resistant microorganism species in the soil can be accurately identified by judging whether functional genes are contained or not, and ecological unbalance possibly caused by blind introduction of alien species is avoided; a remediation strategy taking a microbial resistance mechanism as a core can effectively reduce the bioavailability of heavy metals in the soil, and also can improve the physicochemical properties of the soil through metabolic activity to form a sustainable remediation effect; the method has the advantages of being environmentally friendly, controllable in cost, lasting in effect and the like, and an innovative path is provided for soil ecological function restoration.

The invention discloses a self-repairing synchronous grouting material and a preparation method thereof, and belongs to the technical field of grouting materials. The self-repairing synchronous grouting material comprises a cement base material, a microbial capsule and chitosan calcium salt, the concrete comprises the following raw materials in parts by mass: 7-12 parts of Portland cement, 20-28 parts of fly ash, 40-55 parts of quartz sand, 20-30 parts of water, 2-7 parts of bentonite, 1-5 parts of microbial capsules and 1-5 parts of chitosan calcium salt. The self-repairing synchronous grouting material disclosed by the invention is combined with a microorganism self-repairing technology, and mineral substances such as calcium carbonate and the like can be generated through metabolic activity of microorganisms when the material has cracks, so that the cracks are automatically filled, and expansion of the cracks on the surface of a concrete material can be effectively prevented; the grouting material can continuously repair microcracks caused by environmental changes or construction stress in the long-term use process, and the integrity and stability of the structure are kept.

The invention discloses a ground trough type planting and cultivation management system applied to a greenhouse, and belongs to the technical field of crop planting, and the system specifically comprises a parameter collection module which is used for monitoring the fertilizer absorption behavior of a root system in a planting trough in real time, the planting trough is laid with a composite film, and the metabolic activity parameters of the root system are obtained through a non-invasive sensing technology; the demand division module is used for dividing a seedling stage, a vegetative growth stage and a reproductive growth stage according to crop growth stages, and establishing a differentiated demand model of water and fertilizer absorption of root systems in each stage; the instruction generation module is used for constructing a drainage frequency self-adaptive adjustment strategy, performing multi-dimensional correlation analysis on the root metabolism activity parameters, the matrix osmotic pressure and the environment evaporation rate, and generating a drainage time sequence control instruction matched with the growth stage; the drainage control module is used for placing a perforated drainage blind pipe at the bottom of a planting groove according to the drainage time sequence control instruction and controlling the cooperative action of a drainage valve group through regional gradient; scientific drainage management is achieved.

The invention discloses a complex medical image intelligent recognition system and method based on deep learning, and relates to the technical field of medical image.The system comprises an anatomical image data acquisition module, an anatomical image data analysis module, a functional image analysis module and a medical image evaluation module. By combining the anatomical image, collecting the anatomical information, evaluating the image evaluation index of each anatomical tissue of the medical image and matching each risk tissue, subjective deviation of manual measurement is avoided, the risk tissue detection efficiency and positioning precision are improved, and the processing efficiency is improved. According to the method, the cell metabolic activity of each risk tissue is captured, the functional influence assessment index of each risk tissue of the medical image is assessed, the medical image recognition coincidence value is assessed and optimized, the limitation that only the morphological structure is observed in the traditional anatomical image is broken through, the diagnosis accuracy and reliability are improved, and the medical image diagnosis development is promoted.

The invention discloses a preservation and refrigeration method of pears, which comprises the following steps: (a) pretreatment after picking, (b) stepped precooling, (c) dynamic controlled atmosphere refrigeration, and (d) regular monitoring of gas components. CaCl2 and tea polyphenol composite treatment is adopted to enhance the cold resistance and disease resistance of fruits, and the metabolic activity is gradually reduced in combination with a three-stage stepped precooling process, so that the preservation and refrigeration effects of the pears are improved; and the respiratory metabolism is precisely regulated and controlled by matching with staged dynamic air-conditioned storage. The method breaks through the bottleneck of the traditional refrigeration technology, prolongs the storage period of the pears to 6-8 months, reduces the rotting rate, is suitable for large-scale commercial preservation, and has remarkable economic value and technical advantages.

The invention relates to the field of image processing, in particular to an adrenal metastatic tumor image feature visualization method and system. The method comprises the following steps: collecting an adrenal metastatic tumor PET-CT image, carrying out spatial deviation elimination and image fusion optimization, and constructing a cross-modal standard fusion image; performing adrenal gland network segmentation and three-dimensional voxel reconstruction on the cross-modal standard fusion image, and constructing a three-dimensional adrenal gland voxel rendering model; extracting a metastatic tumor area sub-model based on the three-dimensional adrenal voxel rendering model, performing multi-time-point metabolic information change calculation, and constructing a metastatic tumor dynamic metabolic change curve; and performing internal metabolic activity distribution characteristic and heterogeneity color mapping according to the metastatic tumor dynamic metabolic change curve, and constructing an internal metabolic activity distribution diagram. According to the invention, accurate image presentation is provided, so that the morphology, metabolism and growth conditions of metastatic tumors are more intuitive and clearer.

The invention provides a livestock oxidative stress reaction prediction method and system, and the method comprises the steps: obtaining a real-time physiological monitoring data set with a timestamp mark of a livestock group, carrying out the feature extraction of the real-time physiological monitoring data set, and obtaining a target physiological index feature reflecting an oxidative stress state, the target physiological indexes comprise metabolic activity characteristics, oxidation product accumulation characteristics and anti-oxidation response characteristics. Calling a pre-training model to carry out time sequence correlation analysis on the target physiological index features, and generating an oxidative stress state prediction sequence; and determining a target individual with oxidative stress reaction and reaction development stage information according to the prediction sequence, and finally generating an oxidative stress reaction prediction result for the target individual. Therefore, the oxidative stress reaction of the livestock and poultry can be accurately predicted in real time.

The invention relates to the field of probiotic fermentation, and discloses a probiotic fermented beverage prepared from sea buckthorn fruit peptide and morinda citrifolia fruit peptide and a preparation method thereof, and the probiotic fermented beverage is prepared from the following components by weight: 10-15 parts of a sea buckthorn fruit peptide extract, 8-12 parts of a morinda citrifolia fruit peptide extract, 5-8 parts of a composite probiotic starter, 3-5 parts of fructo-oligosaccharide, and 1-2 parts of a natural sweetener. According to the present invention, the sea buckthorn fruit peptide and the morinda citrifolia fruit peptide are scientifically compounded, such that the synergistic interaction of the two plant active components is achieved, the double-fruit peptide matrix provides the differentiated nutrition supply for the composite probiotics, and the fermentation efficiency is improved through the nutrition complementation; according to the present invention, with the fermentation medium, the growth requirements of different strains are met, the proliferation efficiency and the metabolic activity of the probiotics are significantly improved, the industry problems of poor strain adaptability and low viable count in the traditional single-substrate fermentation are solved through the innovative compatibility, and the fermentation process is stable and efficient.

The invention discloses camel milk protein-polysaccharide composite nanoparticles and a preparation method thereof, and belongs to the technical field of preparation of natural fermentation composite particles. The preparation method comprises the following steps: (1) inoculating phytobacterium plantarum into camel milk for fermentation, centrifuging, and collecting supernatant; and (2) carrying out ultrasonic treatment on the supernatant under an ice-water bath condition, adding absolute ethyl alcohol to precipitate after the ultrasonic treatment is finished, standing, centrifuging, and dialyzing to remove impurities, so as to finally obtain the camel milk protein-polysaccharide composite nanoparticles. According to the preparation method, camel milk is taken as a fermentation substrate, fermentation is carried out by utilizing phytobacterium plantarum LR-1, and the natural polysaccharide-protein nanoparticles (PPNPs) are successfully prepared in combination with ultrasonic treatment. In the fermentation process, the metabolic activity of lactic acid bacteria promotes the combination of protein and polysaccharide, and the particle size, the stability and the oxidation resistance are improved; the ultrasonic treatment further optimizes the particle size and structure, and enhances the oil-water interface adsorption capacity.

The invention relates to a method and a system for constructing a traditional Chinese medicine lung cancer prevention and treatment system based on multi-source data fusion, belongs to the technical field of big data, integrates multi-source information such as global evidence-based literature, real world clinical data and expert decision support, constructs an AI-driven three-layer cognitive model, develops a clinical transformation platform, and provides a traditional Chinese medicine lung cancer prevention and treatment system. The method comprises the following steps: screening dominant treatment stages through an intelligent algorithm, optimizing a diagnosis and treatment scheme, establishing a quantitative correlation model of pathogenesis key elements and tumor metabolic activities, further constructing a syndrome-metabolism knowledge graph, forming a syndrome differentiation validity verification mechanism and a prescription linkage database, and finally designing a result transformation engine. New drug research and development and nosocomial preparation conversion are driven through an evidence chain, and evaluation standards are formulated in cooperation with traditional Chinese medicine syndrome improvement and western medicine solid tumor curative effect indexes. According to the method, the bottleneck problems of data splitting, dialectical staticizing and conversion in traditional research are solved, and full-chain innovation from cognitive modeling to clinical landing is achieved.

The invention discloses a bone defect repair material suitability prediction method based on deep learning, and the method comprises the steps: carrying out the overlapping and uniform partitioning of a bone defect region and a host bone region in three-dimensional CT image data, carrying out the position coding and feature extraction of a plurality of sub-blocks through a three-dimensional visual Transform model of multi-scale position coding, and obtaining the anatomical and functional features; calculating a bone metabolism activity score and a systemic inflammation score based on blood detection data, and then constructing pathophysiological features; performing multi-round interaction on the anatomical and functional features, the pathophysiological features, the parameter data and the multidirectional multi-head cross attention layer, in each round, selecting one modal feature as a query vector, and selecting any modal feature in the rest modal features as a key vector and a value vector; and fusing the interaction features of each round, and determining the suitability score and the risk level of the bone repair material by combining the attention weight and the bone metabolism activity score in the multidirectional multi-head cross attention layer.

The near-infrared wearable subcutaneous fat metabolism monitoring device comprises a flexible substrate, a multi-wavelength NIR light source array, a photoelectric detector, a motion artifact suppression module, a wireless transmission unit, a fixing band and a photonic crystal enhanced detector, and the flexible substrate comprises a biocompatible layer, an optical window array and a pressure sensing layer. The wavelength of the multi-wavelength NIR light source array is 760nm / 850nm / 930nm / 980nm, the photoelectric detector is high-sensitivity InGaAs, the motion artifact suppression module is provided with an embedded signal processing module, the embedded signal processing module contains a motion artifact elimination algorithm, the wireless transmission unit is BLE5.0, the fixing band is connected with the flexible substrate, and the flexible substrate is connected with the fixed band. A first subcutaneous fat oxidation characteristic database based on time-frequency analysis is established by installing a fat oxidation dynamic fingerprint spectrum technology, FODS = summa 850980d [mu] a (lambda, t) dt.e-j2pif0tdt (f0 = 0.05 Hz) FODS = summa 850980dtd [mu] a (lambda, t). E-j2pif0tdt (f0 = 0.05 Hz), metabolic oscillation characteristics are extracted through wavelet-Hilbert transform, and a metabolic activity quantitative evaluation core algorithm WO2023 / 123456 is realized.

The invention discloses a medical image recognition method and system based on artificial intelligence, and the method comprises the steps: obtaining CT image data, pathological section data and PET metabolic activity data, introducing a GNN graph neural network to model the semantic association between multi-modal features, and outputting a fusion feature vector; inputting the fusion feature vector into a DSRN double-flow space-time recursive network, extracting 3D lesion morphological features of a single image by using a spatial flow, analyzing lesion growth kinetic parameters of a historical image sequence through a time flow, and generating a lesion malignancy probability index based on a gating fusion unit; based on the focus malignancy probability index, Monte Carlo Dropout sampling is utilized to generate a confidence interval, a clinically interpretable credibility score is output, and when the confidence is smaller than a threshold value, a low-confidence area is displayed for a doctor to check. The deep fusion of multi-source information is realized, the one-sidedness of a single mode is avoided, and the recognition accuracy and efficiency are improved.

The invention relates to the technical field of compound microbial additives, and particularly discloses a compound microbial additive suitable for alpine regions and a preparation method, the additive comprises microbial thalli, a protective agent and a carrier; the microorganisms are low-temperature-resistant nitrogen-fixing bacteria, cold-resistant phosphate-solubilizing bacteria, cold-resistant potassium-solubilizing bacteria, low-temperature-resistant plant growth-promoting rhizobacteria and cold-resistant streptomyces; the selected microbial strains are all derived from extreme environments of the alpine regions, are naturally selected and artificially domesticated for a long time, and have extremely strong adaptability to severe conditions such as low temperature and oxygen deficit of the alpine regions. Compared with the existing microbial additive in the market, the growth rate and metabolic activity of the microbial strain in the additive are kept at a relatively high level in a low-temperature environment of 0-15 DEG C.

The invention relates to the technical field of cell analysis, and discloses a cell analysis method, a cell analysis device and a storage medium, and the method comprises the following steps: S1, multi-modal labeling: synchronously loading cell surface protein, a metabolism probe and a spatial positioning marker; s2, collaborative acquisition: obtaining space-time multi-dimensional data through flow type, mass spectrum and two-photon combination; s3, tensor modeling: constructing four-dimensional data and correcting spatial drift; s4, metabolic constraint decomposition: executing non-negative tensor decomposition to obtain a factor matrix; s5, dynamically adjusting parameters: optimizing excitation and sampling frequencies in real time according to factor changes; and S6, feature analysis: calculating a metabolism-phenotype coupling index. Through a multi-modal synchronous marking and four-dimensional dynamic tensor modeling method, cross-dimensional association of physical characteristics, molecular expression and metabolic activity is broken through, lossless fusion of single-cell multi-dimensional data is realized, and the spatial resolution is greatly improved compared with the prior art.

The invention belongs to the technical field of microbial fermentation, and particularly relates to a fermentation method for increasing the content of pyocyanin in pseudomonas aeruginosa fermentation liquor. The invention provides a novel pseudomonas aeruginosa fermentation method. The multiplication capacity and metabolic activity of thalli are regulated and enhanced through a microgravity environment. According to the method, single colonies of pseudomonas aeruginosa are cultured through streak isolation, activated and then inoculated to a fermentation medium, fermentation is performed under the microgravity condition of 0.001 g, the microgravity environment fermentation remarkably optimizes thallus growth, the pyocyanin yield is increased, and the biofilm forming ability is enhanced.

The invention discloses a preparation method and application of athlete-derived bifidobacterium probiotics for intestinal barrier repair, and relates to the technical field of biochemistry and microorganisms, and the preparation method comprises the steps of special bacteria source sampling, directional separation and purification, molecular biological identification, antibiotic sensitivity evaluation and injury repair verification. According to the preparation method and application of the sportsman-derived bifidobacterium probiotics for repairing the intestinal barrier, the sportsman intestinal flora is used as an exclusive bacterial source, and the unique metabolic elite attribute and stress adaptability of the sportsman intestinal flora are utilized to directionally screen and obtain a bifidobacterium strain adaptive to an extreme physiological stress environment; the bottleneck that conventional probiotics are only suitable for a common physiological environment and the survival rate and activity in a stress state are greatly reduced is broken through, high survival rate and metabolic activity can be kept in extreme environments such as exercise heat stress and the like, and accurate repair of intestinal barriers is realized through multiple mechanisms; meanwhile, an exclusive screening, identification and safety evaluation system is established, so that the safety and functional specificity of the strain are ensured.

The present invention discloses a measurement and judgment method for orthopedic diagnosis, which belongs to the technical field of orthopedic diagnosis and treatment, comprising: collecting magnetic field signals of bone metabolism through a magnetic field sensor array, collecting electrical impedance spectroscopy data through a four-electrode method to form an original data set; filtering and denoising the magnetic field signals and correcting motion artifacts, and then fitting the electrical impedance spectroscopy data with a Cole-Cole model to extract bone density, porosity, and moisture content; calculating bone metabolic current density through a magnetic field-current inversion algorithm, combining electrical impedance characteristics and current characteristics, and generating a bone health index through a dynamic weight distribution mechanism; by combining the magnetic field-current inversion algorithm with Cole-Cole model analysis, multi-dimensional information of bone metabolic activity and bone tissue structure can be simultaneously obtained, thereby effectively overcoming the problem of insufficient information from a single physical field and significantly improving the accuracy of diagnosis.

The invention provides a trapped person position state analysis method and system, and relates to the field of data processing. According to the method, building structure distribution, conductive fluid flow channels and metal component distribution in a disaster target area are constructed to form a background entropy flow ground state, and noise intensity probability distribution, a channel joint fluctuation relation and a time stability index are counted to generate a background entropy flow field. In the search and rescue stage, a background entropy flow field is converted into a controlled mode through active disturbance, multi-physical channel noise responses before and after disturbance are collected through a multi-field passive sensor network, and a multi-dimensional local entropy response vector is constructed. And combining the consistency, directivity and duration of the multi-noise channel to identify an initial suspected region and extract an entropy anomaly core region, finally inferring metabolic activity and posture limitation conditions according to a local entropy response cooperative relationship in different disturbance modes, and outputting a position state result of the trapped person. By implementing the technical scheme provided by the invention, the position state of the trapped person can be accurately identified.

The invention concerns the specific combination of non-digestible saccharides and lactic acid producing bacteria that was found to be especially suitable for young children, facilitating the most favorable development of the microbiota from an infant-like to an adult-like type taking into account the composition as well as the metabolic activity, and on top of that, advantageously enabling a high level of intestinal butyrate formation.

The present application relates to a kind of regret-alpha ratio-based PET / CT kinetics parameter estimation bayesian optimization method.The present application utilizes reversible double-input three-chamber model to model the metabolic activity of liver tissue, and determines optimization target in combination with real TAC data.By introducing regret-alpha ratio and net inflow rate as weight, high and low weight of initial sampling point is distinguished, and different optimization strategies are used respectively, these strategies are integrated into bayesian optimization framework, and the best parameters are modeled and predicted by gaussian process to target function.After preliminary optimization is completed, whether overfitting exists is judged in combination with target function value to parameter estimation result that does not satisfy physiological reasonableness, and multi-objective optimization is carried out to overfitting result.The present application combines mathematical optimization model with physiological parameters, aims at accurately estimating kinetic parameters, more effectively distinguishes hepatocellular carcinoma and background liver tissue, and provides reliable basis for the analysis of hepatocellular carcinoma.

This application discloses a spatial segmentation radiotherapy guidance method and system based on target immune region activation, relating to the field of digital electrophysiological data processing. The method achieves three-dimensional visualization of metabolic activity and immune response through multimodal fusion of spectral CT / functional magnetic resonance imaging and ResNet radiomics feature extraction, which significantly improves the recognition accuracy compared to anatomical target volume. Furthermore, the immune thermogram generated by the XGBoost model and SAMed-2 segmentation markers can accurately locate highly immune-responsive voxels, and the Monte Carlo simulation gradient dose distribution ensures that the dose intensity in the immune-activated region is several times that of the conventional region while protecting the inactive region. The XGBoost model training of this application can dynamically adjust the dose hotspot according to the patient's specific immune characteristics, so that the radiation can accurately and sufficiently reach the region in the tumor area that is most capable of activating the immune system.