290 results about "Plant production" patented technology

The invention relates to the technical field of thermal power plant production management, in particular to a thermal power plant DCS production process intelligent early warning system based on artificial intelligence, which comprises a data acquisition module for receiving real-time production data of each production link of a thermal power plant acquired from a DCS; the data storage module is connected with the data acquisition module and is used for storing the acquired real-time production data; the data preprocessing module is used for preprocessing the stored data; the feature extraction module is used for extracting key feature parameters from the preprocessed data; the model training module is used for training an artificial intelligence model based on historical data and the extracted key feature parameters; the real-time monitoring module is used for monitoring the production process in real time; according to the invention, the method captures the dynamic change of the time sequence data and the mutual relation between the multi-dimensional features, and achieves the more accurate prediction of the future time.

The invention provides a sewage plant parameter prediction method and system based on machine learning, and belongs to the technical field of sewage plant production management. The method comprises the following steps: reading historical sewage data stored in a database under a time sequence, and importing the historical sewage data into a selective state space model Mamba for training; collecting real-time sewage data under the time sequence of the previous stage; reading real-time sewage data, and performing parameter prediction by the trained selective state space model Mamba to obtain predicted sewage data for sewage plant operation control under the time sequence of the next stage; and storing. The system comprises an acquisition module, a database storage module and a prediction module. According to the method, an abnormal data processing technology based on the Kalman filtering algorithm and an innovative time sequence prediction technology based on the selective state space model Mamba are combined, and the method has remarkable advantages in prediction of the key operation parameters of the sewage treatment plant.

The invention discloses a centralized control system and method for production equipment of a thermal power plant, relates to the technical field of power plant monitoring, and solves the problems that when an existing thermal power plant control system monitors the production equipment in real time, due to the fact that the number of the production equipment is large and the number of monitoring items is large, the data size needing to be monitored is large, and the monitoring efficiency is high. The technical problem that the safety of the thermal power station is reduced due to the fact that part of emergency data cannot be quickly processed due to the fact that congestion possibly occurs during data transmission is solved. The data acquisition module is used for acquiring corresponding monitoring data in real time; the central processing module is used for generating a priority grade score corresponding to each piece of production equipment and generating an abnormal coefficient score corresponding to each piece of production equipment; generating a real-time transmission priority of each piece of monitoring data according to the priority grade score and the abnormal coefficient score; and the data transmission module is used for transmitting the real-time monitoring data to the data supervision module according to the real-time transmission priority.

The invention provides seeds and plants of pepper hybrid SVPB9503, pepper line SBR-E719-1015, and pepper line SBY-HJ17-3100. The invention thus relates to the plants, seeds, plant parts, and tissue cultures of pepper hybrid SVPB9503, pepper line SBR-E719-1015, and pepper line SBY-HJ17-3100 and to methods for producing a pepper plant produced by crossing such plants with themselves or with another plant, such as a pepper plant of another genotype. The invention further relates to seeds and plants produced by such crossing. The invention further relates to plants, seeds, plant parts, and tissue cultures of pepper hybrid SVPB9503, pepper line SBR-E719-1015, and pepper line SBY-HJ17-3100 comprising introduced beneficial or desirable traits.

The invention relates to a method and a device for predicting the service life of an electromagnetic valve, which are applied to a normal excitation electromagnetic valve or a loss-of-excitation electromagnetic valve and comprise the following steps of: determining key components of the electromagnetic valve; executing an aging test and analysis on the coil, and determining the service life of the coil according to an analysis result; carrying out compression deformation and aging tests on the rubber sealing element, and determining the service life of the rubber sealing element according to test results; executing a stress relaxation test on the spring, and determining the service life of the spring according to a test result; predicting the service life of the electromagnetic valve according to the service life of the coil, the service life of the rubber sealing piece and the service life of the spring. According to the method, the effective service life of the normal excitation-loss or excitation solenoid valve can be effectively evaluated, the accuracy of predicting the service life of the normal excitation-loss or excitation solenoid valve in the industrial production process is improved, the equipment management level of a power plant for the type of solenoid valve can be improved, a proper maintenance strategy can be formulated, and the economical efficiency and safety of power plant production are improved.

The invention provides an application method for constructing a plant nutrition expert knowledge graph based on an artificial intelligence technology. The method comprises the following steps: collecting phenotype group data and environmental data of growth and development of a target plant; performing data annotation on the phenotype group data and the environmental data for an optimal form factor and a final yield quality result factor in a plant process, and constructing a neural network model; performing causal effect analysis and anti-fact analysis on the labeled data by using the neural network model, eliminating irrelevant factors, and searching invisible variables and potential results; through optimization learning of neural network parameters, a thinking chain of the plant nutrition expert knowledge graph is remodeled, and the plant nutrition expert knowledge graph is updated, so that a plant growth and development nutrition management theory system is optimized, and planting production and research are accurately guided. According to the application, the action mechanism of plants and the environment in the growth and development process can be clarified, invisible factors in natural ecological fertility can be found, the development efficiency of special fertilizers for different crops is improved, and the technical standard of crop cultivation management is optimized.

System, methods, and devices may control air flow, temperature, relative humidity, substrate temperature, substrate moisture, carbon dioxide concentration, photoperiod, irrigation, light intensity, spectrum, and vapor pressure deficit. The system may comprise water and air pumps, sensors, air and water distribution apparatus, lights, heaters, foggers, sensors, and electronic system control dedicated to control the environment on individual or multiple growing platforms. The present disclosure provides the ability to provide microclimate control to optimize the environment at the plant or shelf level to optimize plant yields and production resources. The present disclosure may utilize an open or closed plant production configuration depending on the desired plant outcome and plant species.

The invention belongs to the technical field of industrial automation, and particularly relates to an efficient thermal power plant production process control system and method. The method comprises the steps that a temperature sensor, a pressure sensor and coal consumption instrument equipment are installed to collect original key parameter data of temperature, pressure and coal consumption in real time, the original key parameter data are processed and stored, the accuracy and reliability of the data can be guaranteed, adverse effects of data missing on follow-up analysis and decision making are avoided, and the accuracy and reliability of the data are improved. A linear regression model is established to describe the technological process of the thermal power plant, guidance and support are provided for technological optimization and improvement, the technological process of the thermal power plant is described based on the linear regression model to execute an optimal control strategy, a decision tree is utilized to construct a control strategy model, the efficiency and performance of the technological process are improved, data can be organized and managed more finely, and the method is suitable for popularization and application. Key parameter data in the control system and actuator output are adjusted, a visual interface is created, and a user is allowed to control the key parameter data of the process control system.

The present invention relates to the technical field of power plant production inspection. The present invention provides a power plant intelligent inspection system based on a large model. The present invention realizes data transmission and processing through communication connection between the server side and multiple inspection devices and the inspected equipment. The present invention includes multiple units such as sensor fault detection, drone inspection optimization, data fusion analysis, and data acquisition fault repair, aiming to solve the inspection distortion problem caused by sensor failure or data transmission interference, and at the same time improve the inspection stability and accuracy of drones under harsh conditions. The present invention uses a random forest algorithm to construct a sensor data model, dynamically adjusts the drone inspection path, and performs data fusion analysis through deep learning to generate accurate inspection conclusions and disposal plans. The present invention not only improves the accuracy and efficiency of inspections, but also further enhances the intelligence and adaptability of the system.

The invention discloses a multi-dimensional data monitoring and collaborative management and control method and system for a plant intelligent factory, and belongs to the technical field of collaborative control, and the method specifically comprises the steps: collecting multi-dimensional data of the plant intelligent factory in real time, the multi-dimensional data comprising plant physiological parameters, environmental factor parameters, nutrient solution parameters and equipment and operation state parameters, and based on the multi-dimensional data, constructing a dynamic multi-level causal association model among equipment, plant physiology, environment and operation, identifying a potential cooperative influence path in an abnormal state, and automatically performing cross-level management and control when an abnormality is detected or a growth process is predicted to deviate from a trend. The cross-level management and control instructions are distributed to an equipment level, a cultivation unit level and a workshop level according to levels, and multi-link linkage regulation and control are carried out on the intelligent plant factory; according to the method, by establishing a causal association and path cooperation mechanism, regulation and control are more accurate, flexible and adaptive, and the stability, resource utilization rate and intelligent level of plant production are effectively improved.

The invention discloses a power plant production data real-time question answering and early warning method based on a multi-modal large model, and the method comprises the following steps: collecting image, time sequence and text data in the operation process of a power plant, fusing multi-modal information through a Perciver IO model, and generating time-synchronized semantic vector representation for dynamically monitoring state changes and constructing task query. Historical annotation events are modeled in combination with the Hawkes process, the time sequence induction relation between the events is captured, the future risk intensity is calculated, and the query generation and decoding process is optimized in combination with initial output, so that an enhanced question and answer and early warning result is generated. Meanwhile, an abnormal causal chain is constructed through event path contrastive analysis, and closed-loop feedback and optimization of system response are achieved. According to the invention, dynamic identification and intelligent response to complex working conditions are realized, and the intelligent level of operation and maintenance of a power plant is enhanced.

The invention relates to the technical field of power plant data processing, and discloses a power plant abnormal data processing method based on a knowledge graph, which comprises the following steps: identifying entities and relationships from structured and unstructured data by utilizing a knowledge graph structured knowledge representation method of the knowledge graph; a complex data association relationship in power plant production is comprehensively captured and expressed, meanwhile, the occurrence condition of abnormal data is matched by utilizing knowledge fusion of a knowledge graph, and a corresponding reference processing method is provided; according to the method, more accurate operation data analysis is realized, and the processing strain capacity of abnormal conditions of the basic-level power plant is improved.

The present disclosure provides a method for producing a polyisoprenoid, which makes it possible to synthesize in vitro a polyisoprenoid having an unprecedented structure, such as a 100% cis-polyisoprenoid or a polyisoprenoid containing an allylic diphosphate derivative as an initiating terminal. The present disclosure relates to a method for producing a polyisoprenoid in vitro, which employs a gene coding for a neryl diphosphate synthase and rubber particles bound to a protein encoded by the gene, or a method for producing a polyisoprenoid, which includes introducing into a plant a vector in which a gene coding for a neryl diphosphate synthase is linked to a promoter having a promoter activity that drives laticifer-specific gene expression to express a protein encoded by the gene specifically in laticifers.

The invention relates to the technical field of industrial digital twinning, and discloses a factory production monitoring method and system based on digital twinning, and the method comprises the steps: collecting the operation characteristic parameters of an entity production line and the simulation parameters of a digital twinning model, and constructing an entity-virtual mapping data set; calculating a structure coupling deviation rate and a time phase drift rate based on the time-structure mapping matrix; determining virtual-real mismatch types including a local lag type, a synchronous drift type and an energy reverse type according to the combination relation of the two types; performing regional response correction, time phase compensation or energy inversion correction for different types of mismatch; the correction result is fed back to the digital twin model to generate a corrected virtual operation curve, the stability index of the model is calculated, when the stability index does not reach a convergence interval, the deviation analysis and mismatch recognition steps are repeatedly executed, and dynamic coordination and high-precision production monitoring between the virtual model and the real model are achieved. According to the method, the dynamic coordination and high-precision production monitoring capability between virtual and real models is improved.

The methods disclosed herein provide for clonal plant production using a morphogenic gene to produce non-transgenic and transgenic plants wherein the morphogenic gene does not integrate into the genome of the plant.

Methods and materials for modulating biomass levels in plants are disclosed. For example, nucleic acids encoding biomass-modulating polypeptides are disclosed as well as methods for using such nucleic acids to transform plant cells. Also disclosed are plants having increased biomass levels and plant products produced from plants having increased biomass levels.

The invention discloses a factory data management system and method based on digital twinning, relates to the technical field of factory management, and solves the problems that in the prior art, equipment operation parameters are collected mostly through a sensor, abnormity is judged based on a static threshold value, but coupling influence of environment variables on faults is not fused, misjudgment is easily generated due to environment fluctuation, and meanwhile, the fault rate is low. The comprehensive optimization of the production efficiency is neglected, so that the monitoring and early warning precision is not high; the method comprises the following steps: constructing a digital twinborn model of factory production equipment; the method comprises the following steps: acquiring simulation data during simulation operation of production equipment and environment data at a corresponding moment; calculating an evaluation coefficient based on the simulation data; based on the environment data, constructing a correction model to correct the fault evaluation coefficient, and obtaining a target fault coefficient; predicting the current simulation data based on a pre-trained fault prediction model to obtain a prediction result; and finally, whether early warning is carried out is judged based on a prediction result. The technical problem is solved.

The invention relates to the technical field of thermal power plant abnormal data processing, in particular to a thermal power plant production monitoring system network security data set abnormal data identification and cleaning method, which comprises the following steps: mapping a spatial-temporal feature vector to a preset thermal power plant multi-level causal graph to obtain a data causal graph; performing information propagation and node updating on the data causal graph, and calculating an abnormal score of each node in a preset thermal power plant multilevel causal graph; identifying abnormal data according to the abnormal score and the network security data set; and cleaning the abnormal data according to the abnormal type and a preset data cleaning strategy. According to the method, correlation modeling of data on time and a system topological structure is realized through a multi-level cause and effect graph; an abnormal score is calculated through information spreading and node updating, and an abnormal event in the network security data can be accurately identified; the abnormal data is cleaned based on the preset data cleaning strategy, redundant, wrong and abnormal data can be effectively removed, and meanwhile key risk information is reserved.

Methylobacterium strains that enhance early growth of plants, improve propagation / transplant vigor, increase nutrient uptake, improve stand establishment, improve stress tolerance, and / or increase a plant's ability to utilize nutrients are provided herein. Also provided are methods to identify Methylobacterium strains that improve nitrogen use efficiency in various crop plants.

The invention belongs to the technical field of microorganisms, and particularly discloses a phytobacterium pentosus HHTMQ4010-1 and application of the phytobacterium pentosus HHTMQ4010-1. The lactobacillus pentosus HHTMQ4010-1 is separated and screened from pickled Chinese cabbage water, can produce gamma-aminobutyric acid, promote plant production and increase plant yield, and has great development and application prospects in the fields of food, health care products, medicines or agriculture.

An intelligent production scheduling optimization method for environment-friendly power generation waste incineration comprises the steps that S1, according to boiler operation and related data of waste, an objective function and constraint conditions which contribute to maximum total gross benefit (TCM) are established; s2, judging whether the next state of the boiler meets a constraint condition or not according to the related data of the boiler; s3, generating a start-stop scheduling sequence of each boiler; s4, calculating a start-stop sequence and an inventory ratio with maximized revenue; the algorithm is combined with the continuous start-stop period of each boiler, the daily start-stop state is judged and selected, and the loss caused by the fact that the boiler is turned from shutdown to operation is reduced. The boiler efficiency is associated with the inventory ratio, so that the boiler efficiency during incineration is improved, and the heat value of garbage combustion is increased. The data volume for arranging the start-stop state of equipment in a long time period is huge, so that the algorithm greatly reduces unreasonable data by optimizing the generation of a start-stop matrix, and an efficient and economical environment-friendly power plant production scheduling mode is realized by calculating and solving the total contribution gross benefit (TCM) through multiple iterations.

Methods and materials for modulating biomass levels in plants are disclosed. For example, nucleic acids encoding biomass-modulating polypeptides are disclosed as well as methods for using such nucleic acids to transform plant cells. Also disclosed are plants having increased biomass levels and plant products produced from plants having increased biomass levels.

The invention relates to the technical field of VR training systems, in particular to a power plant production area VR training system which comprises a digital twin modeling unit, a VR interactive operation unit, a real-time data synchronization unit, a safety training simulation unit, a cloud computing supporting unit, a training evaluation unit and a remote cooperation unit. The digital twin modeling unit is used for constructing a 1: 1 high-precision three-dimensional model of the power plant production area, completing modeling of the whole plant area by deploying a scanning vehicle, integrating equipment physical parameters of the power plant production area by utilizing field sampling equipment, including data of temperature, pressure and current, and mapping and updating the model state in real time by utilizing data of the internet of things; and finally verifying the model. The immersion and operation experience of students are enhanced, the training effect is improved, the operation ability and skill level of the students can be accurately evaluated, the students are helped to discover deficiencies and carry out targeted improvement, the students are helped to avoid dangers in actual operation, and the safety is improved.

The present invention addresses the problem of clarifying the relationship between heat stress in grain-filling stage and the control of stomatal density and producing a useful plant by a novel method different from the conventional genetic engineering techniques. Provided is a method for producing a progeny of a plant which has a controlled stomatal density, the method including a step for controlling the temperature of the grain-filling stage of the plant. The control of the stomatal density is preferably achieved by the control of the methylation of a promoter region of a gene involved in the stomatal density.

The utility model relates to the technical field of concrete production, in particular to a mixing plant production system which is provided with an aggregate storage bin, an aggregate pre-cooling bin and a mixing plant in sequence, and links of aggregate preparation, aggregate pre-cooling and aggregate feeding are separately arranged, so that the waiting time of aggregate feeding is shortened, and the production efficiency is improved.

Vaccines and methods are provided that express Eimeria polypeptides, which when administered to an animal result in a protective response against Eimeria. The vaccine provides expression of Eimeria vaccine protein 3-1e, Gam82, and / or EF-1a polypeptides in a plant or plant part linked to a promoter that directs expression preferentially to the embryonic tissue of the plant or plant part. Further embodiments provide that the polypeptides may be targeted to the apoplast / cell wall or endoplasmic reticulum. Increased expression levels are obtained in the plant or plant part. The plant or plant material in one embodiment may be administered orally.

A method for producing plant-based cheese according to an embodiment of the present disclosure comprises the following: producing a plant-based cheese composition comprising a plant-based protein and a plant-based fat; spraying oil onto a cooling plate contained in a cooling device; feeding the plant-based cheese composition to the cooling device; and cooling the plant-based cheese composition in the cooling device.

A method for producing a plant with flowering-time regulation, the method comprising introducing a mutation into the EMF3 gene in a plant.

An ensemble learning-based system (100) for predicting crop yields, wherein the system (100) comprises the following: a data acquisition unit (102) configured to acquire data sets that include information on crop production, environmental characteristics and soil fertilization; a preprocessing unit (104) connected to the acquisition unit (102) and configured for processing missing values ​​of the data set using multiple imputation by chained equation (MICE) and normalizing the data using robust scaling (RS) to handle outliers; a classification unit (106) with holdout (70%-30%), 5-fold cross-validation (5-fold CV) and 10-fold cross-validation, which is connected to the preprocessing unit (104) and is configured to split the preprocessed data into training and test datasets; an ensemble-based forecasting unit (108) comprising a bagging subunit (108a), a boosting subunit (108b) and a stacking subunit (108c), which is connected to the classification unit (106) and is configured to forecast crop yield; and an evaluation unit (110) configured to evaluate the accuracy and F1 score of the ensemble-based prediction unit (108).

The present invention discloses a method for dynamically coordinating and controlling the production capacity of MES across factories based on multi-objective optimization, which relates to the technical field of production capacity regulation and control. First, the Internet of Things is used to collect and integrate the production data of each factory to ensure the accuracy and integrity of the data. Then, algorithms are used to predict demand, allocate orders, and adjust the production plan in real time according to the production capacity assessment. Next, a multi-objective optimization model is constructed, and the optimal solution is obtained through solution for the decision maker to choose. Finally, the production is monitored through a visual interface, and when an abnormality occurs, it automatically feeds back and adjusts to achieve the dynamic coordination and control of the production capacity across factories. The present invention can improve production efficiency, reduce costs, avoid resource waste and risk losses, ensure product quality, ensure the consistency of standards among factories, optimize resource utilization, promote green manufacturing, strengthen supply chain collaboration, improve the overall operation and management level of the enterprise, and enhance market competitiveness.