174 results about "Plant production" patented technology

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.

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.

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.

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.

The contamination sanitation inspection system (CSIS) allows a user to capture an image of a large scene (such as a food processing line, a food service facility, a food products storage area, or a plant production area / facility) and identify contamination within the scene and preferably represent the contamination on a spatially accurate map (or floorplan) so that contamination within the inspection map area is clearly identified and recorded for subsequent treatment. In an alternative embodiment, the CSIS also includes a decontamination system to sanitize any identified contamination.

To provide a cultivation cup which improves the quality and productivity of a plant produced by hydroponics and is excellent in handleability, and to provide a method for producing the plant.SOLUTION: The cultivation cup 1 is used for hydroponics of a plant such as lettuce, and has a support part 2 for supporting the plant and a medium attaching part 3 to which a sponge medium 20 is attached. The support portion 2 has a first internal space 7 that is surrounded by the first peripheral wall 5 and is open at the top and bottom. One or a plurality of openings 8 are provided in the first peripheral wall 5, and the inside and outside of the support portion 2 communicate with each other through the openings 8. The culture medium-mounting part 3 has a second inner space 15 surrounded by a second peripheral wall 12 and opened at the top and bottom. The lower side of the support part 2 and the upper side of the medium-mounting part 3 are connected, and the first internal space 7 and the second internal space 15 communicate with each other SELECTED DRAWING: Figure 1

The invention relates to the technical field of agricultural management, in particular to a digital agricultural automatic control management system, which comprises the following steps of: acquiring a plant production condition picture in a region by using a satellite, segmenting the picture, dividing the picture into different regions, numbering the regions, and storing the numbered regions into a database; utilizing a GPS positioning technology to match farmland data sensors into the divided areas; obtaining a plant growth image training set, establishing a plant growth prediction model of an ST-LSTM architecture, and inputting the plant growth images into the prediction model to obtain a trained model; the method comprises the steps that a satellite picture is divided for three times, the divided picture is input into a trained model, a plant growth state prediction result is obtained, a plant growth model is compared according to the plant growth state prediction result, the plant growth state is judged, a corresponding area is matched according to the result, and nutrients are supplemented in time according to data. Pictures are finely divided, so that the soil condition of each planting area is conveniently obtained, and a corresponding cultivation scheme is provided.

The invention discloses a photovoltaic base biomass energy plant selection method and application thereof, and aims to solve the problem of land resource shortage in current thermal power plant energy transformation and renewable energy utilization based on the condition of combination of photovoltaic power generation vegetation lacking and energy plant planting. The feasibility of ecological restoration and biomass energy plant production by planting energy plants in a photovoltaic base is researched, a model plant screening standard is established by indexes such as strong stress resistance, drought tolerance, shade tolerance, carbon content and cellulose content, and the growth performance, self population update and influence of perennial energy plants on the photovoltaic base are investigated. The interaction mechanism between the photovoltaic base and crops is analyzed, the improvement effect of different planting modes on the land production efficiency is analyzed, the growth effect of different plants is evaluated, the optimal configuration scheme of energy plants planted in the photovoltaic base is determined, and an efficient photovoltaic-agricultural comprehensive utilization mode is constructed. The double improvement of economic benefits and ecological benefits is realized.

The invention provides a low-temperature absorption and adsorption coupling recovery treatment process for VOCs (volatile organic compounds) oil gas in a tank field, and relates to the technical field of VOCs treatment, which comprises the following steps: (1) low-temperature absorption: the VOCs oil gas in the tank field enters an absorption tower and is contacted with an intermediate material absorbent of a factory production device at-5-15 DEG C, and 80-90% of non-methane total hydrocarbon and organic sulfide are removed; (2) adsorption and removal: the absorbed oil gas enters an adsorption unit with three tanks connected in parallel, residual hydrocarbons are deeply removed through an adsorbent, and the hydrocarbon concentration of outlet tail gas is smaller than or equal to 60 mg / m; through the synergistic effect of low-temperature absorption and adsorption, various organic components in the VOCs oil gas in the tank field can be deeply captured, non-methane hydrocarbon can be efficiently removed, refractory components such as organic sulfide can be treated in a targeted manner, available resources in the oil gas are ensured to be fully recycled, and loss of valuable components is avoided.

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

The invention relates to a discrete manufacturing workshop production simulation modular modeling method, which comprises the following steps of: establishing a modular model of production elements, and setting in functional attributes and a control method; establishing a modular model of a product, and setting in technological process attributes; based on the workshop layout, taking out a corresponding production element model from the module library, and constructing a production simulation model; on the basis of the technological process, a proposed discrete manufacturing workshop production and logistics simulation control method is adopted, corresponding production and logistics resources are called, production and circulation of products among different working procedures are achieved, and workshop full-process and full-factor production simulation is achieved; production simulation information is counted, production performance is analyzed, man-machine interaction adjustment and multi-factor experiments are carried out, and the production process and production resource configuration are optimized. The problems that current production simulation modeling is large in difficulty and long in period, and accuracy is difficult to guarantee are solved, discrete manufacturing workshop production and logistics simulation model modularization rapid construction is achieved, modeling efficiency is improved, and workshop production performance is optimized.

The invention provides seed and plants of watermelon line TCS-EJ09134. The invention thus relates to the plants, seeds, and tissue cultures of watermelon line TCS-EJ09134 and to methods for producing a watermelon plant produced by crossing such plants with themselves or with another watermelon plant, such as a 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 watermelon line TCS-EJ09134 comprising introduced beneficial or desirable traits.

The invention provides seeds and plants of tomato hybrid SVTM9037, tomato line PSQ-9Z16-9140, and tomato line PSQ-9Z20-9250. The invention thus relates to the plants, seeds, plant parts, and tissue cultures of tomato hybrid SVTM9037, tomato line PSQ-9Z16-9140, and tomato line PSQ-9Z20-9250 and to methods for producing a tomato plant produced by crossing such plants with themselves or with another plant, such as a tomato 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 tomato hybrid SVTM9037, tomato line PSQ-9Z16-9140, and tomato line PSQ-9Z20-9250 comprising introduced beneficial or desirable traits.

The invention relates to the technical field of factory production scheduling, and provides a factory production scheduling plan automatic generation method and device and electronic equipment, task information of a to-be-scheduled task and production elements of a production factory are obtained, and the production factory is used for producing the to-be-scheduled task; and based on the task information of the to-be-scheduled task and the production elements of the production factory, determining an optimal scheduling plan of the production factory for the to-be-scheduled task through mixed integer programming and a hill climbing algorithm. By combining the accuracy of the mixed integer programming and the high efficiency of the hill climbing algorithm, a high-quality production scheduling scheme can be found, and calculation can be completed within acceptable time, so that automatic and efficient production scheduling is realized, and the production efficiency and the management level of a factory are improved.