16 results about "Growth curve" patented technology

This invention relates to the field of mechanical property monitoring technology for mine backfill bodies, specifically to a system and method for acquiring and analyzing tensile strength data of mine backfill bodies. The system comprises a data acquisition unit, a physical information coupling unit, a tensile strength inversion unit, and a spatiotemporal evolution mapping unit. This invention acquires the waveguide flight time and energy attenuation coefficient of the backfill body using an embedded guided wave sensor array. It uses the monotonically increasing law of hydration degree as a physical constraint to train the physical information coupling network, outputting a compliant hydration degree time series. Based on a hydration degree-tensile strength mapping relationship library, it inverts and fits to generate a strength growth curve, which is then loaded into a four-dimensional digital twin model to invert the internal tensile strength gradient field. It automatically identifies local abnormal regions where the growth rate is below the safety threshold, improving the physical rationality and accuracy of the inversion results, and realizing visualization of the spatiotemporal evolution of backfill body strength and early safety warning.

This invention relates to the field of ecological environment management technology, specifically disclosing a refined method and system for visualizing regional vegetation carbon sequestration. The method includes: extracting land use types of a target area based on remote sensing data of land use types and biomass; determining forest areas based on land use types and establishing growth curves for the forest areas based on preset fitting curves; calculating the carbon sequestration amount of the forest areas based on the growth curves and determining the carbon sequestration ratio of different forest areas; allocating the total carbon sequestration amount to different sub-regions of the target area based on the carbon sequestration ratios, and visually converting the allocated target areas. This invention first selects a target area, calculates the carbon sequestration amount based on the forest areas within the target area, then redistributes the carbon sequestration to different areas according to land use types, and visualizes the redistribution results, facilitating the management of the area.

This invention provides a method, apparatus, equipment, and medium for constructing an oil and gas field production growth curve model, belonging to the field of oil and gas field production evolution technology. The method collects multiple sets of geological reserves, first and second rate coefficients, and cumulative production data for oil and gas fields. Then, it applies the oil and gas field growth differential equation and obtains a growth model for cumulative production through mathematical solution. After the model is converted to a first-order linear form, iterative calculations are used to find at least two sets of data to ensure a linear relationship between the independent and dependent variables. These data are used to determine the slope and intercept of the linear equation, thereby estimating the final recoverable reserves and model constants of the oil and gas field. Finally, combining the rate coefficients and estimated parameters, a growth curve model for predicting the cumulative production of the oil and gas field is constructed. This invention improves the accuracy of oil production prediction by constructing an accurate oil and gas field production growth curve model.

This invention belongs to the field of granulation, specifically disclosing an automated water suspension granulation control method based on online particle size monitoring, comprising: Step 1, during the granulation process, using an online particle size analyzer to monitor the particle size in real time and plotting a real-time particle size growth curve over time; Step 2, based on the morphological characteristics such as the slope change points of the real-time particle size growth curve, automatically dividing the granulation process into three characteristic stages; Step 3, for each stage divided in Step 2, establishing a quantitative model between particle size growth and key process parameters; Step 4, based on the model's predictive and feedback control, executing closed-loop control in a new granulation batch. This invention also discloses an automated water suspension granulation control system. This method and system, through real-time feedback and dynamic adjustment, ensure that the granulation process of each batch follows the optimal growth trajectory, significantly reducing product quality differences between batches.

This application discloses a method for association analysis based on high-throughput dynamic phenotypes and genetic effects, relating to the field of agricultural bioinformatics. By fitting growth curves to high-throughput dynamic phenotype data from multiple time points, the dynamic changes in the growth cycle of a biological population are obtained. A joint likelihood function algorithm is then constructed to associate this dynamic vector with genetic effects, revealing genetic markers related to growth and development trends and processes. Compared to existing GWAS methods, which can only analyze static, single-time-point phenotype data and cannot uncover genetic loci regulating biological growth and development, this application, by performing association analysis on data collected from multiple consecutive time points, can more comprehensively reveal the evolutionary patterns of phenotypes over time, thereby achieving accurate analysis of genetic markers.

The present invention discloses a method for monitoring the phenology of winter rapeseed based on an improved shape model, which includes: collecting the observation data of winter rapeseed and establishing a dynamic growth curve of winter rapeseed; selecting vegetation indices and establishing a shape model; using the shape model for phenology detection to obtain the phenological dynamics of winter rapeseed. The method of the present invention greatly saves the input of human and material costs, and further promotes the development of agricultural research and the transformation of achievements. Obtaining the crop phenological dynamics in a timely manner can provide a decision-making basis for the formulation of irrigation and fertilization systems, and while increasing crop yields, it can also provide important parameter support for the simulation of farmland ecological systems, which is of great significance in the context of the continuously increasing global food demand.

This application relates to a method, apparatus, equipment, and medium for identifying and removing abnormally small fruits in orchards. The method includes: constructing a basic growth model of the target fruit to obtain a basic growth curve showing the theoretical diameter of the target fruit changing over time; calculating environmental correction parameters for the target fruit based on the actual effective accumulated temperature, standard accumulated temperature, and the weighting coefficient of accumulated temperature influence during the fruit's growth period; calculating an abnormally small fruit judgment threshold based on the basic growth curve, environmental correction parameters, and by introducing an allowable lower limit fluctuation ratio; marking target fruits with a true diameter smaller than the abnormally small fruit judgment threshold as abnormally small fruits; planning a work path based on the global three-dimensional coordinates of the abnormally small fruits; and controlling the actuator of the harvesting robot to remove the abnormally small fruits. This application can accurately distinguish between normally small fruits and abnormally small fruits, avoiding misjudgments and omissions, and significantly improving the accuracy of abnormally small fruit identification.

The application discloses a genetic element for accelerating morphological differentiation of actinomycetes, and belongs to the fields of synthetic biology and molecular biology. eshA Gene overexpression vector pIB139-PermE-his- eshA -his is transformed into target actinomycetes streptomyces coelicolor, and an engineering strain is obtained through screening, and through plate phenotype observation and growth curve determination, the significant effect of gene overexpression is verified. eshA The overexpression strain O eshA S. coelicolor WT appears aerial hyphae and mature spores earlier. The application provides a simple and specific technical scheme for controlling the speed of morphological differentiation of actinomycetes, provides a new genetic element for accelerating morphological differentiation of actinomycetes, and has important application value.​

This invention discloses a high-throughput analysis method and system for seedling phenotypic analysis based on time-series growth images, specifically relating to the field of seedling cultivation and phenotypic analysis technology. It addresses the problems of distortion in growth curves and inaccurate evaluation and screening caused by transient disturbance noise introduced by moving imaging operations in existing high-throughput analysis methods. The method involves acquiring time-series images and extracting phenotypic parameters during the seedling growth cycle, constructing population and individual time series separately, performing time-frequency transformation to separate high-frequency disturbance components and generate energy proportion time series, identifying synchronous peak fluctuations in the population and individual time series to determine the timing of operational disturbances, and interpolating and correcting the contaminated phenotypic parameter values ​​at that moment. This invention effectively identifies and removes operational disturbance noise, improving the quality of seedling phenotypic data and the reliability of subsequent analysis.

This invention discloses a dynamic optimization system for precise feeding amount in livestock farming based on individual growth curves, relating to the fields of intelligent livestock farming and precision feeding technology. The feeding amount determination module determines the target feeding amount based on the individual records, real-time weight, historical feed intake data, and individual growth curves of the target livestock. A multi-source data acquisition module collects data on changes in trough weight, mouth-nose contact status, head movement status, trough vibration status, and feed spillage status outside the trough during batch feeding, generating feeding evidence features and disturbance evidence features. A weight loss source determination module identifies the source of weight loss. A true feed intake correction module eliminates non-feeding weight loss. The dynamic feeding amount optimization module updates the individual growth curve based on the corrected true feed intake and adjusts the target feeding amount for the next feeding cycle. This invention improves the accuracy of feed intake recording and the reliability of feeding optimization.

The application discloses a pig growth curve prediction method based on multi-source data, an electronic device, a storage medium and a program product, relates to the field of intelligent breeding and agricultural information technology, and is used for solving the problem of extensive stress large-feeding decision collection in contact type in pig growth prediction technology. By fusing an RGB image and depth point cloud data, an improved HRNet network introducing trunk rigid constraint and four-limb elastic constraint loss is used to extract pig key points and calculate body size parameters; environment data is fused to calculate an environment stress index to correct time sequence characteristics; a growth inflection point is determined based on multi-head self-attention weight and growth rate; a Chrono-Growth Transformer model is used to segment and fit a growth curve with the inflection point as a boundary; and growth deviation is calculated to realize abnormal early warning and individualized feeding. The application combines deep learning and multi-source data, and the average accuracy of weight estimation can reach more than 95% and the breeding cost is reduced.

The application provides an engineering machinery field-oriented industry chain supply chain resilience index calculation method, and belongs to the technical field of supply chain resilience index evaluation, and comprises the following steps: based on historical demand and supply data, a continuous demand increase fluctuation period is identified through threshold analysis; for each fluctuation period, quantitative evaluation is carried out from two dimensions of delivery guarantee and capacity elasticity; on one hand, a delivery index is calculated by combining a total amount delivery ratio and a time coincidence degree to measure an instant response capability; on the other hand, a production increase resistance index is calculated by fitting an actual production growth curve and comparing it with a target growth rate based on total production to evaluate production capacity climbing potential. Finally, the supply chain resilience index is calculated by comprehensively considering the two indexes of all fluctuation periods, weighted and adjusted for stability. The index can effectively quantify the comprehensive adjustment and adaptation capability of the supply chain when facing demand shocks.

This invention provides the application of norzelamin in the preparation of NDM-1 enzyme inhibitors. Through cefotaxime hydrolysis assays, checkerboard minimum inhibitory concentration (MIC) tests, growth curves, bactericidal curves, hemolysis tests, and *Gnaphalium affine* infection experiments, it was demonstrated that norzelamin can inhibit NDM-1 enzyme activity and exhibits good synergistic antibacterial activity with the carbapenem antibiotic meropenem, enhancing the protective effect of meropenem against *Gnaphalium affine* larval infection models. Therefore, norzelamin, as a potential adjuvant for β-lactam antibiotics, can enhance the antibacterial effect of β-lactam antibiotics by inhibiting NDM-1 enzyme activity, which is of great significance for the clinical development of safe and effective β-lactam antibiotic adjuvants.

The present application belongs to the technical field of forest carbon sink measurement and monitoring, and specifically discloses a forestry carbon sink dynamic baseline investigation method based on historical sample plots. The method disclosed by the present application comprises: collecting historical sample plot data at different periods and integrating the data into a data platform for storage and management; arranging project sample plots in a carbon sink project area and investigating the project sample plots to obtain project sample plot data; using a geographic information system to perform spatial analysis and selecting historical sample plots with a relative error of not more than 15% in tree species composition and stand density from the data platform as control sample plots; based on the historical data of the control sample plots, using a statistical model to fit a growth curve of carbon storage with stand age or time as a dynamic baseline; calculating the difference between the project carbon storage and the dynamic baseline carbon storage as the carbon sink amount. The present application can make full use of historical sample plot data, reduce costs, and improve the accuracy, flexibility and adaptability of carbon sink amount calculation. The present application is suitable for forest carbon sink measurement and carbon sink project monitoring.

This application discloses a method and system for yield prediction based on a crop growth model. The method includes: obtaining the full growth curve of the target crop using remote sensing data of the target crop's production area from the planting time to the current time using a pre-trained vegetation index growth prediction model; generating heterogeneous development curves and heterogeneous guidance curves of the target crop using a preset spatial heterogeneity analysis mechanism, performing differential analysis with preset dual-benchmark curves, and extracting the target deviation interval; extracting the NDVI multivariate feature vector of the target crop's full growth curve based on the target deviation interval, inputting it into the pre-trained crop yield prediction model, and outputting the predicted yield value of the target crop. This comprehensively improves the accuracy and relevance of crop yield prediction.

This application relates to a method, system, device, and medium for optimizing the nutritional balance of children's diets. The method includes: acquiring children's growth status data and comparing the data with standard growth curve data to obtain standard growth indicators; inputting the standard growth indicators into a classification diagnostic machine learning model to generate children's health profile data; calculating daily nutritional target ranges based on the children's health profile data and growth status data; matching ingredients that meet the daily nutritional target ranges in a food database to generate a draft diet plan; identifying combinations of ingredients with antagonistic effects from the draft diet plan, generating nutritional conflict result information, and optimizing the draft diet plan based on the nutritional conflict result information to obtain a nutritionally balanced optimized children's diet. This method can avoid antagonistic effects in diets that lead to poor absorption and improve the reliability of the diet plan.