25 results about "Growth data" patented technology

The application discloses an online semantic momentum stabilization system and method using a Welford algorithm and an optimistic concurrent control. In view of the semantic centroid calculation deviation and timing conflict problems caused by data noise, distribution drift and distributed out-of-order writing in the process of continuous incremental input of a large-scale vector database, the application combines a Welford online iterative algorithm to realize high-precision incremental updating of the semantic centroid mean and variance under constant space complexity. Meanwhile, a distributed optimistic concurrent control mechanism is used to implement version revision number checking on semantic segments and block obsolete data coverage caused by cross-node out-of-order arrival. The application guarantees the numerical stability and timing consistency of semantic feature expression in a dynamic growing data environment.

The application relates to the technical field of medical systems, and particularly discloses a new-born baby comprehensive development monitoring and intervention guidance system which comprises a data acquisition module, a development stage identification module, a multi-dimensional development evaluation module, a deviation calculation module, an intervention scheme generation module and an intervention execution and feedback module; the data acquisition module is used for acquiring multi-dimensional development data of a new-born baby, wherein the multi-dimensional development data at least comprises physiological parameters, neurobehavioral data, physical growth data, feeding data and sleep data; and the development stage identification module is used for determining a current development stage according to a corrected age of the new-born baby. Through multi-dimensional data acquisition and a whole-process standardized preprocessing mechanism, the new-born baby physiological, neurobehavioral, physical growth, feeding and sleep multi-dimensional development data are integrated, the systematic errors and external interference of multi-source heterogeneous data are eliminated through a standardized preprocessing process, and the stability, reliability and effectiveness of the development evaluation data are ensured.

This invention relates to the field of food security monitoring technology, specifically a method for monitoring and warning of food security yield based on big data analysis. The method includes: collecting multi-source monitoring data such as remote sensing vegetation index time-series data, meteorological observations, soil moisture, crop growth surveys, and historical yield statistics. An improved time-series decomposition algorithm combining crop phenological patterns and interannual cycle characteristics is employed to decompose multiple components of the remote sensing time-series data. Spatiotemporal fusion and feature derivation are performed on farmland environment and growth data to construct a multi-dimensional feature vector set. A yield prediction integration model is used to calculate the predicted grain yield and confidence interval, and combined with yield security thresholds, graded risk warning information is output. Multiple data types are integrated to generate a structured warning report, which is then pushed to a decision support platform, achieving accurate grain yield calculation and standardized risk warning.

The application provides a farm load prediction method, device and equipment based on multi-source data, and relates to the field of power load prediction.The method comprises the following steps: obtaining historical load data, breeding animal growth data, environmental data and equipment data of a farm; decomposing the historical load data according to a variational mode decomposition algorithm to obtain a plurality of load components; wherein the variational mode decomposition algorithm optimizes parameters through a sparrow search algorithm, and the sparrow search algorithm establishes a fitness function about the load components according to the breeding animal growth data; after the plurality of load components, the breeding animal growth data, the environmental data and the equipment data are spliced, the spliced data is input into a preset Transformer model to obtain a power load prediction curve of the farm.The application can improve the accuracy and availability of the power load prediction result of the farm.

The present application relates to the technical field of tree species optimization screening, and discloses a tree species multi-element mixed carbon sequestration optimization screening method based on artificial intelligence, which comprises the following steps: constructing the single growth characteristics and mixed growth characteristics of tree species according to the pretreated multi-source growth data; predicting the carbon sequestration amount of different tree species combinations in a given growth environment by using a carbon sequestration amount prediction model; calculating the joint growth adaptability and functional complementarity of different tree species combinations, comprehensively scoring the tree species combinations, screening a candidate tree species combination set based on the comprehensive score, constructing a multi-objective optimization function of the candidate tree species combinations in the candidate tree species combination set and solving the function to screen the candidate tree species combination with the highest carbon sequestration efficiency. Through multi-source growth data preprocessing and feature construction, tree species combination carbon sequestration amount prediction, joint scoring and screening and multi-objective optimization, the present application realizes the quantitative evaluation of the carbon sequestration potential of different tree species combinations and the selection of the optimal tree species combination configuration.

The application relates to the field of garden management and ecological environment monitoring, and discloses a garden vegetation parameter dynamic monitoring system and method, which comprises a data acquisition module, a data fusion module, a three-dimensional modeling module, a point cloud recovery module, a parameter extraction module and a decision output module. The method comprises the following steps: constructing a three-dimensional garden model by fusing and integrating multi-source point clouds and historical growth data, identifying occlusions and reconstructing structures, extracting vegetation parameters, predicting growth trends, and generating visual maintenance suggestions. The application effectively improves the point cloud coverage integrity under the complex spatial structure of the garden, has high fusion registration accuracy, continuous model structure, strong occlusion area completion capability, can accurately extract structural parameters such as the height, crown width and volume of vegetation, realizes trend prediction in combination with historical records, improves the scientific nature, foresight and automation level of maintenance management, and significantly enhances the intelligent capability of garden ecological monitoring.

The application provides a method for obtaining the culture performance of target microorganisms in a microdroplet system, which comprises the following steps: activating and diluting target microorganisms to obtain target bacterial liquid; culturing the target bacterial liquid by using a microdroplet culture system; and obtaining the culture performance of the target microorganisms in the microdroplet system according to the culture performance relationship of the target microorganisms in the microdroplet system and a traditional culture system. According to the growth data of the microorganisms in the existing traditional culture system, the accuracy of the optical method (OD measurement) commonly used in the microdroplet culture system can be calibrated and verified, and the culture condition of the microorganisms in the microdroplet system can be more accurately predicted.

This invention relates to the field of kiwifruit germplasm screening technology and discloses a kiwifruit waterlogging resistance germplasm screening system, including a data acquisition module and an intelligent screening module. The system acquires simulated field environment data, growth data of kiwifruit plants of different germplasms, and sample test data through the data acquisition module. The intelligent screening module assesses the degree of waterlogging stress in the field environment, generating a waterlogging index that considers both the average levels of key stress factors such as water depth and soil moisture content, as well as dynamic trends, comprehensively reflecting the overall impact of waterlogging on kiwifruit growth. It provides a strong quantitative assessment of environmental adaptability. The intelligent screening module also assesses the growth health of kiwifruit plants of different germplasms, generating a health index that accurately reflects the degree of damage to plants under waterlogging stress. Furthermore, it assesses the waterlogging resistance of kiwifruit plants of different germplasms, generating a waterlogging resistance score that reflects the plant's physiological response to waterlogging stress. This multi-dimensional screening method demonstrates high accuracy.

The application provides a teaching quality continuous improvement system and method based on AI and PDCA nested cycles, and relates to the technical field of teaching quality improvement.The application comprises an outer, middle and inner three-layer PDCA cycle framework nested with each other, and a student personal ability growth data bus; the inner cycle constructs a three-dimensional ability knowledge correlation graph with weights through an intelligent planning module, and calculates the value-added amount of each ability point and forms a value-added vector according to the score of the examination items and the preset weight by using an intelligent diagnosis module and an ability value-added evaluation model.The inner cycle data is gradually aggregated upwards through the bus to drive the continuous improvement of the middle and outer cycles, and the outer cycle macroscopically adjusts and transfers new targets downwards step by step.The application can break the data island, realize the objective quantitative evaluation of teaching effectiveness, and realize the organic linkage of micro teaching optimization and macro talent training system.

This invention relates to the field of agricultural planting technology, specifically disclosing a method for increasing quinoa yield by improving drought and lodging resistance. The method includes: acquiring quinoa taproot growth data within a historical planting monitoring period, analyzing the root growth data for obstruction to determine the periods of root obstruction, analyzing changes in soil compaction data within the historical planting monitoring period to determine the periods of soil compaction, and comparing these periods with those of root obstruction to determine whether the obstruction is caused by soil compaction. This method can accurately pinpoint key factors affecting quinoa growth, avoiding unnecessary measures and improving the efficiency and accuracy of planting management. Furthermore, in periods and areas where soil compaction is more pronounced, concentrated human, material, and financial resources can be invested in soil improvement, while in other periods and areas, resources can be used for other aspects beneficial to quinoa growth, rationally allocating planting resources and improving resource utilization efficiency.

In a cultivation management system 1 for managing open-field crop cultivation, the future growth trajectory of crops can be estimated. [Solution] The cultivation management system 1 of the present invention comprises a state data acquisition unit 111 that acquires state data A1, a growth distribution calculation unit 112 that calculates crop growth distribution data A2, a disease and pest distribution calculation unit 113 that calculates crop disease and pest distribution data A3, a first growth data calculation unit 114 that calculates first growth data A5, and a growth state evaluation unit 115 that evaluates the quality of the first growth data A5. The state data A1 includes at least time-series imaging data Aa of the crop captured by an imaging device 31 mounted on an unmanned mobile body 30, fertilization data Ab, pesticide application data Ac, crop variety data Ad, and crop sowing time data Ae.

The application discloses a kind of based on deep learning's seedling mortality prediction method in flower cultivation process, comprising: obtaining the characteristic data of seedling in whole growth cycle and its corresponding mortality, generates the death characteristic data set of seedling;According to death characteristic data set, establish first mortality prediction model, second mortality prediction model;Obtain the environmental data and growth data of seedling in current growth cycle, generate the current characteristic data set of seedling;According to current characteristic data set, first mortality prediction model predicts output first predicted mortality, and second mortality prediction model predicts output second predicted mortality;First weight, second weight are respectively given to first predicted mortality, second predicted mortality, and the integrated mortality of seedling in next growth cycle is obtained by accumulating both.The application can predict the mortality of seedling in advance, and the grower can take rescue measures according to the integrated mortality of seedling, to improve the survival rate of flower seedling.

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.

The application provides a farm animal growth data management method and system, and relates to the technical field of animal breeding data management. The method comprises the following steps: collecting a plurality of weight readings of a main test sheep at a preset frequency within a preset time window, and forming an original weight reading sequence; performing numerical sorting on the original weight reading sequence to obtain a sorted weight reading sequence; removing extreme readings in the sorted weight reading sequence to obtain a core reading sequence; and performing arithmetic averaging on the core reading sequence to obtain a final body weight value. The method aims to solve the problem that the existing average value calculation method is invalid due to the short and irregular physical contact between the accompanying sheep and the main test sheep, which introduces external load, and thus causes growth data distortion, thereby ensuring the authenticity and reliability of the weighing data.

The application relates to the field of big data, and discloses a big data analysis processing method, device, equipment and medium, which comprises the following steps: acquiring time slices of label data in an enterprise storage big disk constructed based on a preset algorithm; based on the time slices, determining storage media for storing the label data according to a preset cold and hot backup data storage mode; calling the label data in the storage media based on a preset label preloading mechanism; and performing data analysis on the label data to acquire target customer group data. The enterprise storage big disk and the label data for storing data are compressed and converted through the preset algorithm, the storage space for storing data is saved, the efficiency of system query is improved through the cold and hot backup data and the preset label preloading mechanism, and the label and the label value are dynamically increased, so that the current real-time growth data, such as big data analysis processing requirements under the condition of 100 million data, are solved.

This invention relates to the field of quality information traceability technology, and discloses a method and system for tracing the quality information of Epimedium sagittatum. The method includes acquiring basic data and planting parameters of the current batch of Epimedium sagittatum seedlings; processing the basic data and planting parameters using a hash algorithm to generate a basic query code; using the basic query code as a parent node and combining it with growth data to generate a growth code; generating a raw material code for the current batch by combining it with process parameters, and associating the raw material code with the growth code; generating an outbound code using a timestamp and the raw material code; generating auxiliary material codes for auxiliary materials involved in deep processing, associating the auxiliary material codes with the outbound code, and generating a product code by combining it with production parameters. This invention facilitates rapid identification of the source and responsible link of quality fluctuations, sets incremental collection frequencies and differentiated collection indicators for different stages, improves the sufficiency of quality assessment basis, and reduces the risk of tampering or falsification during the process.

This application discloses a method and system for regional cotton yield prediction by fusing multi-source remote sensing data. The method first acquires satellite weather data and ground soil data and performs multimodal fusion. Then, it uses a random forest algorithm to assess feature importance and screen out key influencing factors. Subsequently, it trains an SVM model using historical growth data to output a quantitative growth potential value. If this value exceeds a threshold, time series analysis is introduced for dynamic correction, resulting in an adjusted potential index. Next, farmland management data is fused, and a comprehensive yield level estimate is calculated using linear regression. Finally, the planting area is gridded and subdivided into heterogeneous regions based on the coefficient of variation, generating a refined regional yield distribution map. This invention effectively solves the problem of spatiotemporal inconsistency in data through deep multi-source data fusion and dynamic correction mechanisms, significantly improving the accuracy and spatial granularity of regional cotton yield prediction.

The present application relates to the technical field of charger control, and particularly relates to a charger charge-discharge control method and system, the method comprising obtaining real-time charging data and performing impedance calculation to obtain internal resistance growth data; obtaining capacity attenuation records and performing alignment to obtain an aging data sequence; calculating a deviation degree based on the aging data sequence and performing threshold comparison to obtain a comparison result, obtaining real-time heat data based on the comparison result and performing calculation to obtain a balance adjustment parameter; generating and sorting a particle swarm according to the balance adjustment parameter to obtain a charging current scheme, performing data acquisition, and calculating an optimization effect information based on frequency response data; performing matching calculation according to the optimization effect information to obtain matching degree data, updating algorithm parameters according to the matching degree data and iteratively optimizing to obtain a final instruction sequence. The method realizes accurate grasping of the mutual correlation law between capacity attenuation and internal resistance growth in the battery aging process, and effectively delays the battery aging speed.

This utility model discloses an orchid growth environment monitoring device, belonging to the field of orchid technology. It includes a base plate, a support column welded to the top surface of the base plate, and a monitoring device body welded to the top of the support column. A touch screen is installed on the front side of the monitoring device body. The protective plate prevents the touch screen from being damaged by rain, dust, scratches, or other physical damage, thus preventing blurring or screen breakage. This facilitates data reading and normal device use, improving practicality. When the environmental monitoring device is struck by lightning, the lightning rod will transmit the current through a conductor and then through a grounding post to the ground, preventing damage to the environmental monitoring device due to lightning strikes. This provides lightning protection, reduces equipment maintenance costs, and may also prevent data interruption, ensuring that critical growth data is not missed.

The application relates to the technical field of plant growth regulation, in particular to a plant germination parameter regulation system and method based on big data analysis, which comprises the following steps: acquiring soil nutrient data, plant germination growth data and root system distribution data of a monitoring area; analyzing the soil nutrient distribution uniformity and the growth state consistency of plant germination of the monitoring area through the soil nutrient data and the plant germination growth data; analyzing the spatial expansion trend of the plant germination root system of the monitoring area through the root system distribution data; comprehensively analyzing the soil nutrient distribution uniformity, the growth state consistency and the spatial expansion trend analysis results to estimate the plant germination competition level of the monitoring area; and adjusting the fertilizer nutrient content of the monitoring area according to the plant germination competition level estimation results. The application quantifies the plant germination competition level to adjust the fertilizer nutrient content in a targeted manner, and effectively improves the accuracy and scientificity of the fertilizer nutrient content adjustment.

We provide the system. [Solution] A means of individually evaluating people's abilities using generative models, Means for generating personalized self-development programs based on said ability, Means for providing progress information for the program, A means for accumulating and analyzing growth data obtained based on the progress information, A means of providing leaders with insights from the growth data, A system that includes this.

This invention proposes a method and apparatus for leaf trait discrimination in safflower DUS testing. The method includes: acquiring image data of the first and sixth leaves of safflower, constructing a cross-scale three-dimensional reconstruction model, and generating a three-dimensional morphological feature set; acquiring dynamic growth data of safflower and generating a stability discrimination parameter set; constructing an association network using genome-phenome joint analysis, fusing chloroplast genome SNP data and lateral vein density variation coefficients to generate multimodal association feature vectors; classifying using a Transformer+CNN hybrid architecture model, generating classification results and statistical significance values ​​using a dynamic hyperparameter tuning module; and generating a comprehensive discrimination conclusion for DUS testing through a three-dimensional validation system. This invention uses cross-scale three-dimensional reconstruction technology to quantify leaf characteristics, combines a time-series three-dimensional scanning system to capture growth data, and solves the problems of traditional manual observation; it uses a hybrid architecture model for classification and introduces a tuning module to improve accuracy.

The application discloses a student practical accomplishment evaluation method based on digital technology, relates to the technical field of student practical accomplishment evaluation, and comprises guiding personnel empowerment, dynamic practice execution, multi-source data fusion and bidirectional iterative evaluation. The application firstly collects historical practice data of guiding personnel, analyzes and constructs a capability image through digital technology, and generates a personalized cultivation scheme; secondly, generates intelligent adaptive practice tasks based on student growth data and course demand data, collects cross-scene practice data, pre-processes, extracts features and fuses cross-domain, forms a high-value feature data set, finally calculates the student practical accomplishment score through the fusion features, and synchronously generates a guiding personnel capability optimization scheme and a practice system adjustment suggestion. The application relies on digital technology, improves the intelligentization, precision and dynamicization of the evaluation process, and effectively improves the student practical accomplishment cultivation quality and the guiding personnel capability level.

The application relates to the technical field of intelligent agriculture and wireless sensor networks, in particular to a plant factory plant growth data monitoring system based on Zigbee technology, which comprises a data acquisition module, a reference reconstruction module, a coupling verification module and a state judgment module; the data acquisition module is used for collecting environmental growth data and communication link characteristic data in real time and performing data validity preprocessing; the reference reconstruction module is used for constructing an ideal non-interference growth model and generating theoretical plant shape parameters; the coupling verification module is used for generating theoretical signal attenuation data by using biomass radio frequency attenuation logic, and calculating real and theoretical residual error vectors respectively; the state judgment module is used for performing residual error vector topological form comparison, and determining normal growth, growth stagnation or equipment fault state according to the coincidence degree and the attenuation amplitude; the application solves the pain point that signal interference and equipment failure are difficult to distinguish, and realizes low-cost monitoring of plant recessive growth stagnation by using physical layer characteristics.

This invention relates to the field of ancient and famous tree protection, and discloses a method and system for the protection and rejuvenation of ancient and famous trees. The method includes: extracting core growth data from growth status data; performing data augmentation on the core growth data to obtain enhanced growth data; extracting growth characteristics corresponding to the enhanced growth data; inputting the growth characteristics and preset health characteristics into a pre-configured growth analysis model; sampling the growth characteristics using the characteristic sampling unit in the growth analysis model to obtain growth sampling characteristics; mapping the growth sampling characteristics using the anomaly mapping unit in the growth analysis model to obtain growth anomaly characteristics; determining the anomaly location corresponding to the ancient and famous tree; calculating the soil fertility index and branch and leaf vitality value corresponding to the anomaly location; analyzing the anomaly attributes corresponding to the anomaly location; and determining the corresponding protection and rejuvenation measures for the ancient and famous tree. This invention can improve the effectiveness of protecting and rejuvenating ancient and famous trees.