11797 results about "Economic benefits" patented technology

A power grid planning method and system adapted to multiple uncertainties and multi-objective requirements, which relate to the technical field of power grid planning. The method comprises: constructing a deterministic multi-objective coordinated planning model with the objectives of minimizing economic cost, reliability risks and environmental impact; on the basis of an IGDT, constructing a multi-uncertainty model considering wind power, photovoltaic power and load; converting a model into a second-order cone programming model by using variable substitution, absolute value linearization, second-order cone relaxation and McCormick envelopes relaxation methods; and solving the second-order cone programming model, in order to obtain a power grid planning scheme that adapts to uncertainties in wind and photovoltaic power output and load demand growth and meets the requirements for the economy, reliability and environmental friendliness of power grid operation. Thus, economic benefits of power grid planning are improved, carbon emissions from power grid operation are reduced, the reliability of a power grid is enhanced, and an obtained planning scheme has high robustness within expected objectives of planners.

The invention relates to the technical field of ship propulsion, and discloses an energy-saving control method for a pre-rotating guide wheel of a ship propulsion system and a fluid optimization system. According to the invention, through a multi-dimensional cooperative control strategy and fluid structure optimization, an integrated system with dynamic prerotation compensation, self-adaptive flow regulation and vortex suppression functions is constructed. The control method comprises a multi-physical field sensor array, a guide wheel angle dynamic compensation algorithm and an energy efficiency optimization decision model, a deep learning algorithm is adopted to establish a mapping relation between a propeller wake flow field and guide wheel parameters, and 0.1-second-level dynamic response is achieved. According to the fluid optimization system, an asymmetric variable-curvature guide vane structure is innovatively designed, the optimal space distribution of guide vanes is determined through three-dimensional flow field simulation, and a flow guide channel with the pressure gradient self-adaptive characteristic is formed. A real ship verifies that the propulsive energy consumption can be reduced by 10%-25%, the propulsive efficiency can be improved by 15%-30%, the cavitation effect is effectively restrained, vibration noise is reduced, and remarkable economic benefits and environmental protection values are achieved.

The invention discloses a multi-user energy storage power station charging and discharging control method and system based on electricity price, and the method comprises the steps: arranging a monitoring terminal at energy storage equipment, and collecting the load data of each energy storage equipment; carrying out data preprocessing on the collected load data; constructing a cascade neural network prediction model, training the model by using the preprocessed load data, and generating a regional total load demand prediction value in a future time period; designing a deep reinforcement learning model DRL by using the regional total load demand predicted value to obtain a preliminary power adjustment amount; designing a target function considering the battery aging cost by using the initial power adjustment amount, and determining an initial value range of a coefficient in the target function of the optimization model according to a constraint condition; and a multi-objective optimization problem is considered, a Pareto optimal solution set is obtained based on the initial value range, and a final power distribution scheme is determined. The method can accurately predict the load, optimize the charging and discharging strategy, reduce the power consumption and operation cost, balance the service life of the battery and the economic benefit, and improve the intelligent and efficient level of the energy storage power station.

The invention provides a crop accurate management method and system based on a crop growth model. The method comprises the following steps: collecting environment data of a crop growth period by integrating a remote sensing technology, a field intelligent sensor and an unmanned aerial vehicle, and carrying out space-time alignment to form a comprehensive environment data set; and dynamically adjusting parameters of the crop growth model by using a Bayesian rule, evaluating the influence of environmental uncertainty on crops in combination with Monte Carlo simulation, and providing yield prediction with a confidence interval. Based on the prediction, a particle swarm optimization algorithm is applied to formulate management schemes such as irrigation, fertilization, pest control and harvesting opportunities, and meanwhile, a multi-objective optimization strategy is adopted to balance economic benefits and environmental protection. After the scheme is implemented, the crop growth response is monitored in real time so as to reinforce learning and automatically adjust management measures, and precise agricultural management is realized. According to the technical scheme provided by the embodiment of the invention, the crop management precision and efficiency are improved.

The invention provides an intelligent optimization method and system for a revenue mode of a commercial energy storage power station, and relates to the technical field of energy storage power stations. Establishing a multi-dimensional life loss coefficient matrix by analyzing the health state data of the energy storage equipment; a candidate charging and discharging scheduling strategy is generated based on a deep reinforcement learning model, and a double-constraint optimization charging and discharging strategy with predicted income and life loss as a reward function is adopted; and finally generating a control instruction to realize optimized operation of the energy storage power station. According to the method, equipment life loss and economic benefits are comprehensively considered, and long-term benefit maximization of the energy storage power station is realized.

The invention relates to the technical field of power dispatching, and provides a multi-electricity energy storage participation power dispatching system based on an optimal charging demand, which comprises a dynamic price adjustment module, a multivariate data fusion module, a collaborative optimization intelligent dispatching module, an energy storage resource management optimization configuration module and a safety and reliability prompt module, a dynamic price adjustment module, a data fusion module, a collaborative optimization scheduling module, an energy storage resource optimization configuration module and a safety and reliability prompt module are integrated, global optimization of electric power resources is achieved through the intelligent and automatic technology, an intelligent bidding strategy is provided for dynamic price adjustment, a comprehensive information platform is constructed through data fusion, flexible scheduling is achieved through collaborative optimization, and safety and reliability are improved. The energy storage resource management platform monitors and optimizes configuration in real time, safety and reliability are prompted to ensure stable operation of the system, the system is intelligent, the automation integration degree is high, the power dispatching precision, the response speed and the economic benefits are remarkably improved, and the problem of insufficient economic benefits in the prior art is solved through the technical scheme.

The invention belongs to the field of wind power plant grid-connected control, particularly relates to a wind power plant active power efficient distribution method considering the fatigue damage degree of fans, and aims to solve the problem that scheduling instruction power is uniformly distributed to all fans in the prior art. And the fan fault probability is increased due to the fact that the influence of power adjustment on fan fatigue damage is not considered. The method comprises the steps of fan load estimation based on stress analysis and energy conservation, low-complexity fatigue damage quantification based on an improved energy method, and construction of a multi-target optimization model with the minimum total fatigue load of a wind power plant and the most uniform fatigue load distribution between fans as targets based on a Gulin method, an entropy weight method and an improved matter element extension model. According to the method, rapid and accurate power distribution is realized, so that the fan runs in a proper state, the accumulation of fatigue damage and the risk of failure occurrence are reduced, and the overall running efficiency and economic benefit of a wind power plant are improved.

The invention discloses an energy equipment carbon emission scheduling optimization method in an integrated energy system, and the method comprises the steps: determining an energy equipment carbon emission factor, constructing a carbon emission cost function, employing Monte Carlo simulation to generate a carbon transaction price fluctuation scene, and carrying out the carbon transaction price fluctuation scene. The method comprises the following steps: calculating a carbon transaction risk index of equipment in each scene according to a cost function, determining start-stop time by economic benefit optimization, constructing a comprehensive energy system topology network structure, updating a carbon flow path set according to the start-stop time, calculating a comprehensive carbon emission coefficient, establishing an energy flow and carbon flow coupling model between a high emission path and a low emission path, and constructing a comprehensive energy system topology network structure. A scheduling strategy feasible space is obtained through a cooperation strategy, and an optimal cooperation scheduling operation strategy is obtained through optimization of a particle swarm algorithm by taking the minimum carbon emission as a target. The method can effectively reduce the carbon emission / optimize the start and stop of the equipment, improves the economical efficiency and environmental protection property of system operation, achieves the dual goals of energy conservation and emission reduction and benefit improvement, and has good interpretability at the same time.

The invention discloses a multi-objective optimization decision control method for shrimp and silkworm polyculture environment parameters, and relates to the technical field of polyculture control. The method comprises the following steps: deploying a water quality sensor network, and combining prawn and nereis growth monitoring equipment; extracting criticality of environmental parameters and biological growth indexes by using historical breeding data and experimental data; constructing a dynamic coupling model, and establishing a dynamic model of the polyculture system based on a differential equation; developing a hybrid optimization algorithm to balance the competition and symbiotic relationship between the prawns and the nereis; and generating a control instruction set based on an optimization result, and adjusting the control equipment through a fuzzy PID controller. According to the invention, intelligent regulation and control of shrimp and silkworm polyculture environment parameters are realized, and economic benefits of shrimp and silkworm polyculture are improved.

The invention discloses a new energy project intelligent planning method and system based on a large language model, and relates to the technical field of new energy planning, and the method comprises the steps: constructing a new energy project planning knowledge base; the new energy project planning knowledge base comprises basic data required by project planning and is used for providing data support for the optimization model; based on the training data in the new energy project planning knowledge base, performing field adaptability training on the large language model to obtain a special large language model in the new energy project planning field; and based on a project demand input by a user, analyzing project parameters through the special large language model, inputting an analysis result into the optimization model, and generating a planning scheme of a new energy project. According to the method, automatic conversion from a natural language demand to a professional planning scheme is realized, the accuracy of energy balance constraint is ensured through the topological structure diagram of the energy system, a flexible switching optimization target of economic benefits and environmental benefits is supported, and finally intelligent generation and evaluation of the planning scheme are realized.

The invention discloses an intelligent fishery comprehensive evaluation system based on multi-dimensional indexes, and belongs to the field of intelligent fishery. The intelligent fishery comprehensive evaluation system based on the multi-dimensional indexes comprises a water quality monitoring unit, a prediction and early warning unit, a breeding regulation and control unit, an ecological evaluation unit and a decision output unit. The method solves the problem of hysteresis of the detection result in the prior art, can advance the water quality change trend and the red tide occurrence probability in the future by monitoring the water temperature, the dissolved oxygen, the pH value and other key water quality parameters in real time and by means of the prediction model, provides a key basis for breeding decision making, and improves the breeding quality. Execution equipment is flexibly adjusted according to real-time data and early warning information to improve the breeding environment and improve breeding benefits, meanwhile, multi-source data is integrated to comprehensively evaluate the influence of breeding activities on the ecological environment, ecological balance maintenance is assisted, comprehensive evaluation results are integrated to generate ecological improvement suggestions, resource allocation is optimized, and the economic benefit is improved. And the intelligent and ecological level of fishery is integrally improved.

The invention discloses an intelligent early warning car washer, which relates to the technical field of car washers, and comprises a data acquisition module, a data processing and feature extraction module, an anomaly detection module, a fault prediction and early warning module and a false alarm correction and verification module: the data acquisition module acquires operation data of the car washer through a preset sensor and monitoring equipment; according to the invention, through adaptive threshold adjustment, multi-dimensional data fusion and machine learning technologies, the intelligent early warning system significantly improves the accuracy of fault early warning, reduces false alarm and missing alarm, and ensures that an alarm is triggered only when a real fault occurs. The system can adjust the threshold value in real time, integrates a plurality of key parameters, accurately predicts equipment faults, and helps to discover potential risks in time. And through accurate early warning and intelligent maintenance, the downtime of the equipment is reduced, major faults and high maintenance cost are avoided, the service life of the equipment is prolonged, and the working efficiency and the economic benefits of enterprises are improved.

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention relates to the technical field of equipment risk management and control, and particularly discloses an equipment risk management and control system based on model prediction control, which comprises a data acquisition processing module, a data analysis module, a model construction prediction module and an analysis regulation and control module, obtaining an equipment health condition score through standardization processing and weighted calculation; the data analysis module is used for distinguishing linear and nonlinear characteristic signals according to the score change trend; the model construction and prediction module selects corresponding algorithms to establish a prediction model according to different types of signals, so as to predict the health state of the equipment in the future; the analysis regulation and control module formulates a reasonable equipment maintenance plan based on the prediction result, and dynamically adjusts the production arrangement, the equipment state is continuously monitored and intelligently analyzed, faults can be effectively prevented, the probability of occurrence of non-planned shutdown events is reduced, and finally the purpose of improving production safety and economic benefits is achieved.

The invention relates to a superconductive annular air-core reactor structure optimization method, a medium and a program product, and the optimization method comprises the steps: optimizing the structure parameters of a superconductive reactor through an MATLAB and COMSOL joint simulation technology, and carrying out the optimization of the structure parameters of the superconductive reactor through a multi-objective genetic algorithm, thereby achieving the optimization of the structure parameters of the superconductive reactor. Iterative optimization is conducted on structural parameters such as the coil inner diameter, the surrounding radius, the number of turns of the coils, the number of the coils and the number of parallel branches of the electric reactor, the optimization target comprises the minimum strip use amount and alternating current loss, and in the optimal solution set, according to the strip use amount and the alternating current loss, the strip cost and the refrigeration cost are calculated. And finally, outputting an optimal structure parameter meeting a design index so as to realize the minimization of the total cost. Compared with the prior art, the method can be used for structural optimization of the superconducting reactors with different capacities, structural parameters with the lowest cost under different indexes can be obtained by setting constraint conditions, and economic benefits of reactor design are improved.

The invention discloses a wind power plant dual-mode dynamic active wake flow control method and system and a medium, and the method comprises the steps: taking the maximum power output, power balance and fatigue load stabilization as optimization targets for solving and obtaining an optimal control instruction if a current control mode is a power efficiency improvement mode; otherwise, taking the maximum power output as an optimization target to solve and obtain an optimal control instruction; issuing the optimal control instruction to the virtual wind power plant, and delaying to obtain a delayed control instruction; if the mode is a power efficiency increasing mode, directly issuing to a real wind power plant; and if the mode is a power tracking mode, solving a final optimal control instruction by taking power reference tracking, control reference tracking and fatigue load stabilization as optimization targets, and issuing the final optimal control instruction to the real wind power plant. The method aims at weakening the influence of the wake effect in the wind power plant through dynamic active wake control, improving the generating capacity, maintaining the fatigue load balance and bringing higher economic benefits to operators.

The invention relates to the technical field of temperature control, in particular to a temperature control method and system for a crop greenhouse cultivation environment, and the method comprises the following steps: calculating the growth rates of crops under various temperature conditions based on the variety information of greenhouse crops, recognizing ideal temperature ranges of a plurality of growth stages, and calculating the temperature of the greenhouse crops according to the ideal temperature ranges; and a crop temperature management model is constructed by integrating ideal temperature range data of a plurality of stages. According to the invention, through calculation of growth rates of crops under various temperature conditions, identification of temperature requirements of the crops in multiple growth stages, calibration of control parameters of a temperature adjusting device, improvement of adaptability and efficiency of a temperature control system, and analysis of an illumination intensity change trend, prediction of internal temperature of a greenhouse is realized. The temperature regulation meets the actual illumination condition, energy is saved, the growth environment is optimized, it is ensured that the environment in the greenhouse is kept in the optimal state, and the market competitiveness of crops and the economic benefits of agricultural producers are improved.

The invention discloses a fry culture dynamic monitoring and feeding optimization method based on deep learning. The method comprises the following steps: S1, installing a water quality sensor and a camera to collect water quality data and fry image data; s2, performing feature extraction on the fry image data by using an improved convolutional neural network; s3, analyzing a growth trend and an activity mode of the fry by using a long-short-term memory network to obtain a comprehensive score; s4, obtaining a feeding strategy based on a multi-target adaptive PPO algorithm, and forming a personalized feeding plan; s5, performing detection and early warning on abnormal data through an anomaly detection algorithm, and adjusting a breeding environment; and S6, continuously optimizing the fry culture dynamic monitoring model. According to the method, an efficient and scientific optimization scheme can be provided in dynamic monitoring and feeding of fry culture, and remarkable technical value and economic benefits are brought to practical application.

The embodiment of the invention provides a wind turbine generator control method and system based on wind power plant wind regime prediction, and belongs to the technical field of wind power. The method comprises the following steps: collecting operation condition information of a target wind turbine generator, and preprocessing the operation condition information; taking the preprocessed operation condition information as a model input parameter, performing wind speed prediction based on a pre-trained wind speed prediction model in real time, and obtaining a time sequence wind speed prediction value; performing average turbulence intensity prediction within a predetermined time period based on the time sequence wind speed prediction value; and matching and executing a corresponding wind turbine generator control scheme based on the wind speed numerical interval where the time sequence wind speed predicted value is located and the turbulence intensity numerical interval where the average turbulence intensity is located. According to the scheme, through accurate wind speed and turbulence prediction, accurate wind power equipment self-adaptive adjustment strategy intervention is achieved, and the operation stability and economic benefits of a wind turbine generator are improved.

The invention relates to the technical field of production scheduling of manufacturing workshops, in particular to a distributed intelligent production scheduling decision-making system based on cloud computing. The system is composed of a cloud computing platform, a data acquisition module, a data processing module, an intelligent production scheduling engine and a distributed execution control module. The problems of performance bottleneck, data acquisition limitation, lack of flexibility and adaptability of a production scheduling scheme and the like in a traditional production scheduling system are solved, intelligent distribution and scheduling of production tasks can be realized through fusion application of advanced technologies such as cloud computing, big data and artificial intelligence, the production efficiency and the product quality are improved, and the production cost is reduced. And greater economic benefits and social benefits are created for enterprises.

The invention discloses an energy storage scheduling method and system based on a service area load and renewable energy power generation prediction, and the method carries out the prediction of the load demand of a service area based on a long short-term memory (LSTM) network, and combines the renewable energy power generation prediction with a power grid electricity price signal to dynamically adjust the charging and discharging strategy of an energy storage unit. By predicting the fluctuation of the load in the service area and the power generation condition of the renewable energy source, the optimal energy storage scheduling scheme can be automatically generated in different time periods, and the method is particularly suitable for scenes with large traffic flow fluctuation and complex power demand in the service area. According to the method, the dependence of the service area on the external power grid is effectively reduced, and the stability and economic benefits of system operation are improved.

The invention provides an innovative apple tree fruit nutritional ingredient dynamic monitoring and precise management system, and aims to solve the problem that the change of fruit nutritional ingredients cannot be mastered in real time in traditional orchard management. The system integrates spectral data, environmental data and image data, and comprehensively reflects the change of fruit nutrients and the relationship between the change and environmental factors through multi-source data fusion analysis. According to the invention, a standardized model between spectral data and nutritional ingredients is established, and a deep learning algorithm is introduced to improve the accuracy and efficiency of image recognition. The system can analyze fruit nutrients and environmental parameters in real time, send early warning information and generate personalized fertilization and health management suggestions. In addition, the system adopts a modular and extensible design, and is convenient to customize and integrate. The orchard management accuracy and efficiency can be remarkably improved, the yield and quality of fruits can be improved, the production cost is reduced, and the economic benefits are increased.

The invention relates to the technical field of intelligent control, in particular to an intelligent control method and system for heat supply parameters of a thermal power plant, and the method comprises the following steps: capturing temperature sensor data of multiple key parts of the thermal power plant, combining operation time with external weather condition data, carrying out data integration, analyzing the current operation state, and obtaining heat balance basic data. According to the method, comprehensive analysis is carried out in combination with external weather conditions, dynamic adjustment of heat supply parameters is achieved, the reaction speed is increased, the fuel ratio and load are optimized, the energy utilization efficiency is improved, long-term weather prediction data are integrated, heat supply demand changes are predicted and adapted, energy waste is reduced, and the energy utilization efficiency is improved. Real-time load adjustment and fuel efficiency calculation enable the thermal power plant to accurately match the actual heat demand, the combustion efficiency is optimized, and by predicting maintenance points and potential faults, the equipment operation reliability is improved, the maintenance plan is updated in time, the production safety is ensured, the operation flexibility and economic benefits are improved, and the environmental pollution risk is reduced.

The embodiment of the invention provides a resource management method and system based on cloud edge collaboration. According to the method, firstly, an intelligent AI management platform can be constructed to better allocate computing, storage and network resources; and then resource requirements of the static resources and the dynamic resources can be accurately estimated by monitoring calling frequencies of data interfaces of the static resources and the dynamic resources, so that dynamic allocation of the computing resources is realized, enterprises can be helped to better manage and utilize the computing resources, the stability and reliability of the system are improved, the operation cost is reduced, and the economic benefit is increased. And greater value is created for enterprises. Meanwhile, intelligent scheduling of resources can be achieved based on load balancing, tasks can be automatically distributed to nodes with low loads according to the load conditions of all the nodes, and therefore load balancing is achieved, and the overall performance of the system is improved. Through the dynamic resource allocation process, the resource allocation is more reasonable.

The invention relates to the technical field of earthquake monitoring, in particular to a geological disaster early warning device based on earthquake waveform recognition, which comprises an earthquake data receiving module, an earthquake wave type analysis module, a disaster mode recognition module, an early warning signal construction module, a risk judgment module and an early warning information issuing module. According to the invention, through identifying and analyzing the seismic waveform data, the characteristics of the earthquake and the type of the caused disaster, such as landslide, rock collapse, debris flow, ground fissure, surface collapse and the like, are rapidly judged, and through comprehensively evaluating the emergency and the influence range of the earthquake event, the targeted early warning information is generated and issued in time. The geological disaster response speed and efficiency can be remarkably improved, earthquake monitoring is not limited to recording of earthquake activities, disaster risks can be evaluated in real time, more accurate disaster prediction and early warning can be provided, for areas where geological disasters frequently occur, the early warning accuracy and timeliness can be remarkably improved, casualties and property losses can be reduced, and the economic benefit is improved. And the method has extremely high practical value and social significance.

The invention provides a green asset management system based on a multi-layer system structure, relates to the technical field of green asset management, and aims at obtaining green asset data; data analysis and behavior simulation verification are carried out based on the carbon emission activity nodes, carbon emission factors are obtained, carbon footprint calculation is carried out, and a first carbon circulation path is obtained; performing economic division on the quota carbon asset data by taking nodes in the first carbon flow path as constraints to obtain a plurality of node quotas; dynamically correcting each node quota in combination with carbon transaction flow and hedging influence to obtain a plurality of node quota threshold values, performing node footprint optimization by taking the node quota threshold values as a target, generating a second carbon flow path, performing association clustering on nodes in the second carbon flow path, obtaining a plurality of emission reduction management stages, and performing emission reduction management and quota optimization updating by taking the node quota threshold values as a strategy target. The problems of poor flexibility and dynamic nature of carbon asset management and untimely market response are solved, and the effects of improving the efficiency and adaptability of carbon asset management and enhancing economic benefits are achieved.

The invention relates to the technical field of power system operation and control, in particular to a medium-and-long-term marketization operation bidding strategy for a wind-light-water-storage combination. Aiming at the problems of volatility, large prediction error, limited profit space and the like of wind and light power generation in the medium-and-long-term power market, the invention proposes that wind and light power generation and cascade hydropower are combined to participate in medium-and-long-term market bidding transaction together; wind and light power generation is encouraged to fully participate in the market by introducing a carbon transaction and green certificate transaction mechanism, and a double-layer optimization model is constructed by considering the influence of current market electricity price fluctuation; the upper layer is a combination quantity report and quotation decision-making model, and the lower layer is a market clearing model so as to realize combination market expected total revenue maximization and social welfare maximization; the strategy significantly improves the medium and long term contract competitiveness and profit space of the wind and light new energy, promotes the consumption of the new energy, and has good environmental benefits and economic benefits.

The invention belongs to the technical field of oil exploitation, and particularly relates to a method for determining the application limit of conglomerate oil reservoir carbon dioxide huff and puff and carbon dioxide-water displacement. The method solves the problem of low crude oil extraction efficiency caused by unreasonable application boundaries of carbon dioxide huff and puff and carbon dioxide-water displacement of conglomerate oil reservoirs at present. According to the technical scheme, the method comprises the steps that a conglomerate oil reservoir standard matrix and fractured cores are obtained, the cores are arranged according to a harmonic average method, the cores are sequentially loaded into a long core displacement device according to the arrangement sequence, and the initial state of the oil reservoir is established; respectively carrying out carbon dioxide huff and puff and carbon dioxide-water displacement experiments to obtain the crude oil exploitation efficiency of each experiment, establishing a fracture ratio and crude oil exploitation efficiency plate, and determining the application limit of the conglomerate oil reservoir carbon dioxide huff and puff and carbon dioxide-water displacement through the plate; according to the new method, an oil reservoir development scheme can be optimized, and higher economic benefits and operation flexibility are achieved.

The invention relates to the technical field of agricultural production, and in particular to a sensor-based real-time monitoring system for the growth status of apricot and plum. A plurality of sensors are arranged to collect key growth status data in an apricot and plum planting area in real time. A data processing module is used to process the growth status data. A growth model construction module uses a long short-term memory network to construct an apricot and plum growth status prediction model, and predicts the growth trend, health status and potential risks of the apricot and plum in real time. An abnormality detection and early warning module compares the real-time data with the model prediction result, identifies abnormal situations in time and issues an early warning to a user. An irrigation and fertilization decision module generates an optimal irrigation, fertilization and environmental regulation strategy. A control execution module automatically regulates related equipment. A visualization module displays monitoring data, prediction results, early warning information and decision strategies. The invention significantly improves the economic benefits and sustainability of apricot and plum planting.

The invention relates to the field of short-term load prediction and power dispatching control of a power system, in particular to an energy output optimization dispatching method and system considering a random source load virtual power plant, and designs a virtual power plant double-layer optimization dispatching model. The lower-layer model constructs a user load prediction model of an improved Transform, and then a renewable energy output model is established by using a scene generation method; the upper layer model takes the power generation cost minimization of the wind-light storage and combustion virtual power plant as a target, considers constraint conditions such as gas turbine output power, ramp rate, energy storage charging and discharging power, electricity storage quantity and power balance, and proposes a single guide target function comparison algorithm considering random source load to carry out optimization solution on a virtual power plant optimization scheduling target function; and planning and deciding the output of each energy source in the next 24 hours. Experiments show that the new energy consumption level can be remarkably improved under the random source load condition, and better economic benefits are obtained while stable operation of a power distribution system is promoted.