14 results about "Growth model" patented technology

The invention relates to the technical field of feed efficiency optimization, in particular to a feed efficiency optimization system based on a growth model, and the system comprises a data collection unit which is used for collecting and associating individual identities, individual weight data, time-phased feed intake data, environment data and environment forecast data; the model calibration unit is used for calibrating individual energy conversion efficiency parameters, generating an environment dynamic correction factor by using the environment data and generating a group classification label based on historical individual performance data; and the decision and execution unit operates a double-cycle cooperation mechanism based on pre-judgment and execution. By dynamically calibrating the individual energy conversion efficiency and combining with the environment forecast, real-time calculation and differentiated feeding regulation and control of the individual energy demand can be realized.

The invention discloses a landscape plant precise maintenance control method based on multi-source perception and a growth model, particularly relates to the technical field of automatic control systems, and is used for solving the problem that a set value in an existing landscape plant maintenance control system is kept constant and cannot adapt to dynamic growth requirements of plants. The optimal environment set value of the current growth stage is calculated by collecting growth environment parameters and plant physiological state parameters, a multivariable time sequence causal network is constructed based on the parameters, the time sequence evolution entropy is calculated, and the change trend of the set value is monitored. Identifying a conflict relationship among different maintenance targets, analyzing a coordination priority through a key causal path to generate a coordinated maintenance strategy, and generating a set value smooth switching track when a time sequence evolution entropy exceeds an adaptive threshold value and a set value change trend is consistent with a phenological period prediction direction; and finally, the controller is adjusted to output an instruction to drive an execution mechanism to carry out maintenance operation according to the updated set value, and optimization of the control system on the whole life cycle of plant growth is achieved.

The invention discloses a poplar growth prediction and region optimization method based on multi-source data fusion, and the method comprises the steps: collecting and preprocessing original multi-source heterogeneous data, generating the multi-source heterogeneous data, carrying out the data fusion processing, and generating a multi-source fusion data set; evaluating the influence weight of each variable in the multi-source fusion data set on the growth index, determining the driving factor type and the interaction of each variable, and constructing a growth model system and a single-variable simplified growth model based on the driving factor sorting and the interaction; generating a dynamic query data table, an economic mature age judgment result, a carbon sink calculation result and a thinning suggestion according to the constructed model; and based on the residual sequence of the long-period prediction model and the time sequence of the climate-driven factor, detecting the period coupling relationship between growth and climate, verifying the lock correlation of the climate-growth system, and determining a climate adaptability operation decision rule based on the period coupling relationship so as to generate a standardized operation scheme. According to the invention, the accuracy of growth prediction and regional optimization can be improved.

The invention relates to a red date yield and quality collaborative prediction method and device fusing a growth model and machine learning and a storage medium. The method comprises the following steps: carrying out causal discovery analysis on historical data related to red date quality, and determining key features influencing the quality and causal weights of the key features; integrating periodic remote sensing observation data into the red date growth mechanism model through a data assimilation technology to obtain an assimilated optimal state variable set; and based on the assimilated optimal state variable set, the real-time environment characteristic data and the causal characteristic adaptive weight, through a trained multi-task deep learning model, carrying out red date yield collaborative prediction processing, and generating a red date yield collaborative prediction result. By adopting the method, pseudo-periodic interference can be effectively inhibited, a physiological mechanism and a data rule are deeply fused, and collaborative prediction of the yield and the quality of the red dates is realized.

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.

Described herein are systems and methods for state change implementation. In some embodiments, an apparatus may obtain system data and classify the system data to descriptors. In some embodiments, an apparatus may determine descriptor ratios as a function of the elements of system data classified to descriptors, weightings associated with the elements of system data, or both. In some embodiments, an apparatus may determine a growth model as a function of a plurality of descriptor ratios.

The invention discloses a sugarcane germination state monitoring system based on image recognition, and relates to the technical field of agricultural intelligent monitoring, the system comprises the following components: an image acquisition module, an image recognition module, a dynamic growth model construction module, a prediction and early warning module and a decision support module; according to the method, a dynamic growth model is constructed based on extracted time sequence characteristic data, the germination progress of sugarcane buds is predicted by using a fusion time sequence analysis and machine learning algorithm, and potential growth problems are warned in time when growth data deviates from a normal curve, and in addition, a decision support module is used for generating a decision suggestion through a data visualization unit and a decision suggestion generation unit. Visual data display and scientific decision basis are provided for farmers, the farmers are helped to deal with abnormal growth in time, a planting management strategy is optimized, and precise agriculture and scientific planting are achieved.

The invention relates to the technical field of poultry breeding weighing, in particular to a breeding decision-oriented poultry weight cleaning and uniformity evaluation method and system. The method comprises the following steps: acquiring poultry weight data in real time through an intelligent weighing sensor array, and performing growth rule constraint screening on the data based on a fluctuation threshold de-weighting strategy, a reference weight proportion range and a day age linear growth model to form a preliminary effective sample set; a density peak region of the weight data is identified through a mean shift clustering algorithm in combination with an adaptive kernel bandwidth, and outliers are automatically eliminated; and constructing a skewness-kurtosis low-dimensional embedding domain to carry out normal distribution statistical discrimination on the high-order moment characteristics of weight distribution, and outputting a distribution result. When the detection data form is in positive skewed distribution, square root transformation and mean correction are performed on the weight data, a high-precision poultry weight statistical index and uniformity evaluation result is generated, a growth trend curve is drawn in real time, and reliable data support can be provided for feeding strategy adjustment, health monitoring and slaughter decision making.

A method of modeling sustainability strategies for an aviation industry is presented. The method comprises receiving, at one or more processors, flight traffic information for a plurality of flights over a select time period. A traffic growth model is applied to the flight traffic information to generate projected flight traffic information over a future time period. Baseline calculations are performed on the projected flight information to generate forecasted aviation emissions information over the future time period. User input is received at one or more processors indicating one or more parameters for each of one or more of a plurality of sustainability strategies. The one or more of the plurality of sustainability strategies are applied to the forecasted aviation emissions information to generate adjusted forecasted aviation emissions information. An emissions reduction potential is indicated over the future time period based on the adjusted forecasted aviation emissions information.

A method (900) of modeling sustainability strategies (116) for the aviation industry is presented. The method (900) includes receiving (910), at one or more processors (1010), flight traffic information (102) for a plurality of flights for a selected time period. A traffic growth model (110) is applied (930) to the flight traffic information (102) to generate projected flight traffic information for a future time period. Baseline calculations (112) are performed (940) on the projected flight information to generate predicted aviation emissions information for the future time period. User input (104) is received (950), at the one or more processors (1010), the user input (104) indicating one or more parameters for each of one or more of a plurality of sustainability strategies (116). The one or more of the plurality of sustainability strategies (116) are applied (960) to the predicted aviation emissions information to generate adjusted predicted aviation emissions information. Based on the adjusted predicted aviation emissions information, an emissions reduction potential is indicated (970) for the future time period.

An apparatus and method for state change implementation. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive system data including a plurality of elements from a system data source, determine one or more elements of the plurality of elements using a machine learning model, determine one or more quantitative values as a function of elements of the system data categorized to one or more descriptors, determine a descriptor ratio as a function of the one or more quantitative values, determine a growth model as a function of the descriptor ratio, wherein the growth model comprises a model of a relationship between descriptor ratio and a metric, generate a growth model modification as a function of the growth model, and configure a remote device to display the growth model modification.

The invention belongs to the field of intelligent recommendation, and particularly relates to an AI intelligent recommendation engine construction method and system, and the method comprises the steps: carrying out the uplink compliance verification of user resume text information through a block chain and a preset compliance verification library, and generating an information verification matching chain; when a target recommendation demand is responded, compliance verification and text analysis are firstly performed on the demand, and the contribution degree of a field to a service is evaluated to obtain an effective matching field; on the basis of the effective fields, forward matching is carried out in combination with a user hierarchical portrait map and a skill growth model in the matching chain, and a matched user pool and accuracy are obtained; meanwhile, the user task completion degree is reversely evaluated through analogue simulation and a task-skill dependency graph, the matching strategy is dynamically adjusted in combination with time causal analysis till the recommendation requirement is met, bidirectional precise matching is achieved through the block chain in combination with the layered portrait and the skill growth model, and the recommendation efficiency is improved through reverse simulation and dynamic adjustment. And the man-post matching precision, efficiency and suitability are greatly improved.

This application provides a dynamic calibration method and system for a cumulative hair growth time series model, relating to the field of hair surgery technology. It addresses the technical problem of insufficient objectivity, quantification, personalization, and dynamic correction capabilities in the prediction and tracking of postoperative hair growth effects in related techniques. The method includes: acquiring pre-operative hair growth data; acquiring post-operative hair growth data; determining multiple cumulative growth data points based on the pre-operative hair growth data and post-operative hair growth data acquired at multiple different time points; applying a growth model to each of multiple predetermined regions; determining the parameters of the growth model for each of the multiple predetermined regions based on the multiple cumulative growth data points and the growth model; and generating a predicted cumulative growth value at a predetermined future time point for each of the multiple predetermined regions based on the determined parameters and the growth model.

This invention relates to the field of yak farming and discloses a yak self-growth simulation system based on 3D twins. The system includes: a perception and data acquisition layer for automatically collecting yak-related data and environmental data via IoT devices, integrating multi-dimensional data, and creating a model library to provide data support and a model foundation; a digital twin engine, serving as the core of the system, enabling the binding and mapping of physical yaks to virtual models, growth simulation, and analytical decision-making; and a 3D twin scene and interaction platform for constructing a virtual pasture and providing visual interactive functions, ultimately achieving accurate mapping, growth prediction, decision support, and visual interaction for yak growth. Compared with existing technologies, this invention has the advantage of deeply integrating real yak data with a three-dimensional growth model to achieve dynamic simulation, prediction, and interactive analysis, thus solving problems such as opaque data, experience-dependent decision-making, and the lack of visibility into the growth process in traditional farming.