452results about How to "Increase income" patented technology

The invention provides a private domain live broadcast user behavior analysis and drainage recommendation method based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: collecting user behavior data, constructing a multi-mode deep neural network for processing, extracting user time sequence behavior features, extracting user text intention features through employing a residual connection structure and comparative learning, and carrying out the drainage recommendation of the private domain live broadcast user. User behavior link characteristics are acquired through a time sequence diagram attention network, user layering is performed through a Gaussian hybrid clustering model, a user loss probability is predicted by using a deep cross neural network, future activeness and purchasing power of a user are predicted in combination with a recurrent neural network, and commodities are matched through a diagram attention commodity knowledge network. And performing recommendation by using the improved double-tower deep neural network in combination with difficult sample mining and a variational auto-encoder, and searching an optimized push strategy to obtain an optimal push strategy.

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 discloses an intelligent sensing algorithm and system for crop growth vigor, computer equipment and a medium, and the algorithm comprises the following steps: 1, carrying out the image collection of a farmland, and collecting the spectral data of different wavebands in the farmland; 2, preprocessing the collected images and spectral data, and extracting multi-dimensional feature information of the crops; 3, fusing the extracted multi-dimensional feature information of the crops to obtain comprehensive and accurate crop growth feature information; 4, performing data analysis on the crop growth feature information through deep learning, and constructing an intelligent decision model; through the implementation of the invention, the characteristics and beneficial effects of multi-information fusion, high-precision perception, cost reduction, quality improvement, wide application and the like are realized through the crop growth vigor intelligent perception algorithm, and the method plays an important role in improvement of agricultural production and agricultural product quality.

The invention belongs to the technical field of island integrated energy systems, and particularly discloses a short-time scale low-carbon optimal scheduling method and system for an island integrated energy system, and the method comprises the steps: constructing an electricity-hydrogen-heat multi-energy coupled island integrated energy system model; constructing a real-time stepped carbon transaction model based on the interval to which the carbon emission quota participating in the carbon emission transaction belongs by using an island integrated energy system model; on the basis of a real-time stepped carbon transaction model, deviation penalty of new energy prediction is considered, one day is divided into n scheduling cycles, n is an integer larger than 1, a target function is constructed with the highest total income of a system as an optimization target, the optimization target is solved, and an optimized scheduling result is obtained. According to the invention, wind energy, solar energy, wave energy, stored energy, a photo-thermal power station and other energy sources are co-scheduled with electricity, hydrogen and water loads to form a dynamically balanced energy system; and high consumption and low carbon emission are kept as far as possible.

The invention provides a bidding method for virtual power plants participating in a joint market based on a master-slave non-cooperative game. The bidding method comprises the following steps: constructing a double-layer game framework in which multiple virtual power plants participate in a day-ahead joint market; carrying out Wasserstein distance uncertainty modeling on the wind power, the photovoltaic power and the electric load power; based on the multi-source load power uncertainty set, an upper-layer multi-virtual power plant two-stage distribution robust bidding model is constructed, and the bidding model comprises a first objective function with the minimum sum of pre-scheduling cost for making a bidding strategy in a day-ahead stage and rescheduling cost expectation in an intra-day stage and a corresponding first class of constraint conditions; constructing a lower-layer joint market clearing model; the clearing model comprises a second objective function with the minimum day-ahead electricity-peak regulation-hydrogen combined market energy purchase cost and a corresponding second class of constraint conditions; performing equivalent conversion on the bidding model and the clearing model; and solving the converted bidding model and the converted clearing model to obtain a bidding strategy.

The invention discloses a multi-level dynamic task decision-making method for a large-scale heterogeneous unmanned aerial vehicle based on a coalition game, and relates to the field of game theory, task decision-making and multi-agent collaborative task planning, and the method comprises the following steps: 1, building a dynamic task decision-making optimization model of the heterogeneous unmanned aerial vehicle under multiple constraints, determining constraint conditions and designing an evaluation function based on the unmanned aerial vehicle income; 2, converting the dynamic task decision process into a coalition game solving process, establishing a coalition game model, determining a preference relationship of the unmanned aerial vehicle based on coalition revenue, designing a coalition switching rule of the unmanned aerial vehicle, giving a form of a coalition game stable solution, and determining a convergence condition of the game; and step 3, designing a multi-level dynamic task decision algorithm based on the established coalition game model. According to the method, a complex dynamic task decision process is decomposed into a plurality of stages, global revenue maximization is realized, and the method has good expandability for heterogeneous unmanned aerial vehicle clusters.

The invention aims to provide a hotel price prediction system combining reinforcement learning and a long short-term memory network, and relates to the technical field of neural networks, and the system comprises a data acquisition multi-dimensional time sequence feature extraction unit which is used for acquiring historical operation data of a hotel, carrying out the multi-scale decomposition of a multi-dimensional time sequence through wavelet transform, and obtaining a multi-dimensional time sequence feature extraction unit; obtaining a wavelet coefficient as a multi-dimensional time sequence feature; the long and short-term memory network processing unit is used for inputting the multi-dimensional time sequence features into a long and short-term memory network as input and outputting a hidden state; and the price distribution prediction unit is used for using the price prediction model according to the hidden state, generating predicted price distribution, defining a Markov decision process, using the predicted price distribution as a reinforcement learning model, and generating a final price prediction result according to the predicted price distribution. According to the invention, the precision of price prediction is improved, and the coping capability of the system for market fluctuation is greatly enhanced.

The invention discloses a crop economic fertilization recommendation method based on multi-source remote sensing data. The method comprises the following steps: establishing a nitrogen fertilizer effect curve by using a WOFOST model and an isoline; an economic nitrogen application amount equation is established, and the optimal economic nitrogen application amount is calculated; comprehensive crop growth monitoring indexes are made, and crop growth is graded; and distributing the nitrogen application amount according to a nutrient measurement and regulation method. According to the crop economic fertilization recommendation method based on the multi-source remote sensing data, aiming at the problem that corresponding yields under different nitrogen application amounts are difficult to obtain, a WOFOST model is provided, the nitrogen application amount is used as a unique variable, and the yield is recommended to be more accurate. The problem that corresponding yields are difficult to obtain under different nitrogen application amounts can be effectively solved, the advantages of a nitrogen fertilizer effect curve are obtained through a crop model, marginal cost is obtained according to the nitrogen fertilizer effect curve, a fertilization decision is made in combination with marginal income, farmland refined fertilization management can be achieved, the input cost is reduced, and the economic benefit is improved. And a new method is provided for refined farming operation of a large-area farmland.

The invention relates to the technical field of shared energy storage optimization regulation and control, and discloses a shared energy storage and new energy coordinated regulation and control method based on a profit distribution strategy, and the method comprises the steps: constructing a total alliance model comprising a distributed wind power model, a distributed photovoltaic model and a shared energy storage model; based on the difference of the subjects participating in the alliance, constructing a plurality of operation scenes of the alliance; taking collaborative operation of all the main bodies as a maximum alliance scene, and calculating the total alliance income of the main bodies; for each operation scene of the alliance, calculating the historical credibility, deviation adjustment contribution degree and profit increase contribution degree of each subject to construct a Shapley value distribution correction factor of each subject, and constructing a correction profit distribution model corresponding to each subject in the alliance with the total alliance profit in the maximum alliance scene; and calculating the actual revenue of the shared energy storage station, maximizing the actual revenue of the shared energy storage station based on preset alliance operation constraints, and obtaining a shared energy storage optimal regulation and control strategy to carry out coordinated regulation and control on the shared energy storage station.

The invention discloses a day-ahead optimal scheduling method for a virtual power plant containing multiple types of loads. The day-ahead optimal scheduling method comprises the following steps: step 1, investigating and analyzing the power plant; step 2, collecting load data; 3, constructing a model; step 4, optimizing a scheduling strategy; step 5, plan transmission response; according to the method, the time-of-use electricity price of the user side is associated with different types of users, so that the electricity utilization cost of the users is reduced while the benefits of the virtual power plant are improved; establishing a virtual power plant day-ahead optimization scheduling model containing multiple types of loads and designing time-of-use electricity price pricing strategies of three different types of users to obtain an optimal scheduling strategy based on the time-of-use electricity price; and transmitting the peak-valley flat price data to different types of users for load response, distributing a next-day output plan to each power generation resource for actual power generation, calling an internal power generation side and a user side of the virtual power plant while considering carbon emission permit transaction, realizing optimal configuration of internal resources of the virtual power plant, and promoting low-carbon economic operation of the virtual power plant.

The invention provides an electric vehicle charging station charging control method and system based on deep reinforcement learning, and relates to the technical field of electric vehicle charging control. Setting a reward function and constructing an electric vehicle charging coordination control model by taking a control objective of realizing profit maximization of a charging station while meeting the charging demand of an electric vehicle driver; converting the electric vehicle coordinated charging coordination control model in the charging station into a Markov random game model; a multi-agent reinforcement learning algorithm of a centralized Critic network with an attention mechanism is introduced, a Markov random game model is solved, and through continuous interaction with dynamic environment information, an updating strategy of each charger agent is optimized so as to maximize an expected accumulated reward, so that the charging efficiency is improved. And the optimal electric vehicle charging coordination control strategy is obtained.

The invention discloses a customized order receiving method and system for a designated driver based on a saas platform. The invention relates to the technical field of designated driving services. Receiving demand data of a driver, wherein the demand data comprises order receiving preference, a service area and service time; receiving personal information input by a driver, wherein the personal information comprises basic information and order receiving preference setting; wherein the demand data comprises order receiving preferences, service areas and service time of drivers and users; wherein the basic information comprises a name, a driving age and a vehicle type; according to the invention, the driver is allowed to set personalized order receiving conditions according to own order receiving preference, service area and time, so that the working flexibility of the driver is greatly improved. The driver can autonomously select the order suitable for himself / herself, so that the rejection rate of improper orders is reduced, and the working efficiency and the satisfaction degree are improved. And through an intelligent order receiving matching algorithm, the system can quickly match the most appropriate order for the driver, so that the empty driving time and waiting time of the driver are reduced, and the working efficiency and income are further improved.

One or more embodiments of the invention provide a network switching method based on a satellite-ground convergence network and related equipment. The method comprises the following steps: according to user information at the current moment, obtaining predicted received signal strength of a network adjacent to a user and a predicted operation state of the user relative to a corresponding base station; and obtaining an optimal network switching scheme through a deep reinforcement learning model according to the predicted received signal strength and the predicted operation state. According to the technical scheme, the switching offline rate can be effectively reduced, the switching frequency is reduced, the system benefit is effectively improved, and the method is suitable for a high-speed motion scene.

The invention discloses a collaborative scheduling method and system for a centralized energy storage power station to participate in a joint market, and the method comprises the steps: enabling an energy storage station and a new energy station to form a joint system, taking the maximization of the income of the joint system in a green power and spot market as an optimization target, and constructing a joint system optimization scheduling model considering a green power transaction electric quantity decomposition curve; carrying out optimization scheduling on the capacity of the shared part of the energy storage; independently participating in power market competition by using the residual capacity after energy storage sharing, constructing a double-layer optimization model of participating in spot goods-peak regulation-green power combined market by energy storage, and solving the double-layer optimization model to obtain an energy storage operation strategy; and calculating the contribution degree level of each subject in the joint system, calculating a profit distribution proportion according to the contribution degree level, and further performing profit distribution on each subject in the joint system. According to the invention, flexible diversity of participation of centralized energy storage in multiple types of markets and multiple operation modes can be brought into full play, so that the energy storage utilization rate and the operation income are improved.

The invention discloses an electric vehicle ordered charging and discharging method and system, and the method comprises the steps: obtaining the number of electric vehicles meeting the requirements in a target range, the number of agents, and the parameter information of the electric vehicles, and building a V2G-based electric vehicle response capability constraint condition according to the parameter information; establishing a response capability value acquisition rule of the electric vehicle under an electric vehicle response capability constraint condition based on V2G, and establishing an electric vehicle charging and discharging strategy double-layer optimization model containing the response capability value of the electric vehicle; and according to an improved multi-population genetic optimization algorithm, solving the electric vehicle charging and discharging strategy double-layer optimization model to obtain the optimal charging power of each time period so as to obtain a final electric vehicle charging and discharging strategy. By optimizing the scheduling strategy, the power grid load fluctuation can be effectively reduced, and the power grid operation stability is improved. Meanwhile, by considering the scheduling willingness and scheduling capability of the electric vehicle user, the user can obtain the maximum income when participating in the V2G plan, so that the participation enthusiasm of the user is improved.

The invention discloses a cloud energy storage operation mechanism based on thermal inertia of an urban building air conditioning system, which relates to the field of energy storage and comprises the following steps: (1) establishing a fixed-frequency air conditioner virtual energy storage monomer model and an aggregation model based on the thermal inertia of the urban building air conditioning system; (2) designing a cloud energy storage operation mechanism taking air conditioner aggregation virtual energy storage as a main leasing resource, and establishing a cloud energy storage operator model, a cloud energy storage user model and a transaction pricing strategy; and (3) solving the cloud energy storage operation model by taking the minimum power consumption cost of the user as a target, and distributing and evaluating the social total welfare brought by the cloud energy storage mechanism. According to the energy storage operation mechanism provided by the invention, the electricity consumption cost of the subscribing user can be effectively reduced, and the early-stage investment and the later-stage operation and maintenance cost of an operator are reduced; and under the dual-carbon background, the strategy is also beneficial to the real-time consumption of new energy on the user side.

The invention belongs to the technical field of compressed air energy storage, and discloses a salt cavern compressed air energy storage reservoir stability grade evaluation method and system, and the method comprises the steps: obtaining salt rock physical and mechanical parameters, stratum structure parameters and salt cavern internal structure parameters; selecting a stability evaluation index of the salt-cavern compressed air energy storage library; establishing a three-dimensional geomechanical model of the salt cavern energy storage library and performing simulation calculation; constructing an evaluation model; determining an index weight value of the stability of the salt-cavern compressed air energy storage library; calculating an evaluation grade; and adjusting and optimizing operation parameters of the salt cavern energy storage bank. According to the method, by providing a systematic quantitative analysis process and a unified evaluation index system, the accuracy and reliability of stability evaluation of the salt-cavern compressed air energy storage library are remarkably improved, and an important technical support is provided for development and application of a salt-cavern compressed air energy storage technology. Important technical support is provided for development and application of a salt cavern compressed air energy storage technology, and commercialization and large-scale development of the technology can be promoted.

The invention provides a community electric vehicle charging station target optimization method and system based on an evolutionary game, and the method comprises the steps: building an information transmission mechanism between an agent and a user, obtaining an agent operation mode based on the information transmission mechanism, and formulating the electricity price of each time period of the next day; establishing an evolutionary game model between the agent and the user demand according to the electricity price in each time period of the next day and the user charging demand feedback; performing target optimization on the evolutionary game model to obtain an optimal community electric vehicle charging electricity price; and dynamically adjusting the number and distribution of the charging piles, the charging speed and / or the power grid load according to the optimal community electric vehicle charging electricity price. According to the invention, the evolutionary game between the agent and the user is established to obtain the global optimal pricing and power purchasing strategy, so that the number and distribution of the charging piles are optimized, the charging speed is improved, the power grid load is balanced and stabilized, and the benefits of the charging station and the satisfaction degree of the user are improved.

The invention belongs to the technical field of solid waste treatment, and particularly discloses an iron tailing and coal gangue synergistic magnetizing roasting iron recovery and activity improvement optimization method which comprises the following steps: S1, taking coal gangue doping amount, carbon powder doping amount, heating temperature and heating time in magnetizing roasting as influence factors; designing a response surface test scheme by taking the iron grade and the iron recovery rate of the reduced iron powder and the tailing activity index as response values; s2, iron tailings, coal gangue and carbon powder are mixed, magnetization reduction roasting, ball milling and magnetic separation are conducted, reduced iron powder is recovered, and the iron grade and the iron recovery rate of the reduced iron powder are tested; s3, testing the activity index of the tailings; s4, establishing a response surface model of three response values, and verifying the reliability of the model; and S5, obtaining an optimization technical scheme which simultaneously meets the multi-objective optimization demand conditions of the three response surface models. The application value of the reduced iron powder of the iron tailings and the application value of the tailings are synergistically improved, the overall income of iron tailing treatment products is improved, and solid waste co-treatment is achieved.

The invention relates to the technical field of aluminum bar processing, in particular to a battery positive and negative electrode connecting piece aluminum bar counter bore production process which comprises a bottom plate and further comprises a controller, a punching mechanism and an automatic demolding mechanism, and the punching mechanism comprises a punching lower die, a punching upper die, a top plate and a driving assembly; the automatic demolding mechanism comprises a lifting frame, a rotating assembly, two pushing assemblies and a plurality of ejector rods, the ejector rods are arranged at the top of the lifting frame at equal intervals, the top ends of the ejector rods are flush with the bottoms of the inner sides of cavities in the tops of the punching lower molds, and the driving assembly and the rotating assembly are electrically connected with the controller. According to the battery positive and negative electrode connecting piece aluminum bar counter bore production process, the aluminum bar can be formed on the outer wall of the material belt in a stamping mode, meanwhile, counter bore, bending, chamfering and shaping on the surface of the aluminum bar are completed at a time, the next process is not needed, the intermediate turnover process and time are reduced, the one-stop machining effect of a mold is achieved, and the production efficiency is improved. And the production quality of the aluminum bar is improved.

The invention provides a vehicle network interaction regulation and control method based on power grid multi-agent cooperation and related equipment. The method comprises the steps of determining weather information, vehicle information, vehicle owner information, a prediction agent, a regulation and control agent and a pricing agent; according to the weather information, the vehicle information, the vehicle owner information and the prediction agent, determining a regional power utilization load, a regional power generation load and a vehicle charging request; and according to the regional electricity consumption predicted quantity, the regional electricity generation predicted quantity, the vehicle charging request, the regulation and control agent and the pricing agent, regulating and controlling the charging and discharging state of the vehicle accessed to the power grid, calculating the income of the vehicle owner according to the pricing agent, and sending the income to the vehicle owner. Through organic cooperation of the multiple power grid intelligent agents, on the premise that the travel requirements of users are met, temporary power supply provided by non-renewable energy generator sets is reduced in the climbing process of the power grid, the peak regulation capacity of the power grid is improved, and the income of the users is increased.

The invention discloses a shared parking method for real-time sharing adjustment and dynamic income optimization, and belongs to the technical field of shared parking system optimization. The problem that an existing shared parking method is low in parking space utilization rate is solved. According to the method, an owner can flexibly adjust the sharing state of the parking space according to actual requirements, modification in a common mode can be carried out, instant modification in an emergency situation is also supported, and personalized requirements are met. Different pricing modes are provided, meanwhile, an overtime pricing mechanism is introduced, when the vehicle owner parks in an overtime mode, the system can increase prices according to the overtime duration, the vehicle owner is motivated to leave on time, and the circulation efficiency of the parking space is ensured. According to the invention, a bidirectional credit evaluation mechanism is also introduced, the behavior of frequently modifying the parking space state by the owner and the overtime parking behavior of the owner are recorded and brought into a credit scoring system, the occurrence of improper behaviors is reduced, the fairness of the shared environment is ensured, and the win-win situation of both parties is realized. The method can be applied to optimization of the shared parking scheme.

The invention provides an intercropping method of tobacco and waltheria indica. The intercropping method comprises the following steps that 1, before tobacco and waltheria indica are planted, soil is deeply ploughed, a composite agent is sprayed, and then base fertilizer is applied; the composite agent is prepared from the following raw materials: ammonium sulfate, tea tree oil, rhizobium and a peanut vine extract; (2) tobacco seedlings are selected and transplanted in February to March, waltheria indica is planted between rows, and after planting is conducted for 2-3 weeks, compound fertilizer is applied between the rows of the tobacco seedlings and the waltheria indica; (3) when rainstorm weather occurs, spraying a complex microbial inoculant within 24 hours after rainstorm, and (4) managing until harvesting tobacco and waltheria indica. By the adoption of the intercropping method, nematodes in soil are inhibited, the tobacco and the waltheria indica are effectively improved, symbiosis and growth of the tobacco and the waltheria indica are promoted, and the harvesting yield per mu of the tobacco and the waltheria indica is increased.

The embodiment of the invention discloses a navigation route determination method and device, equipment and a storage medium, and the method comprises the steps: determining a plurality of to-be-optimized legs in a target region, and determining to-be-optimized parameters corresponding to each to-be-optimized leg; wherein the to-be-optimized parameters at least comprise a waypoint position, a navigation speed and an optimization target parameter; performing parameter optimization on the to-be-optimized parameters corresponding to the to-be-optimized leg, and determining target navigation parameters corresponding to the to-be-optimized parameters; and according to the target navigation parameters, determining a target leg corresponding to each to-be-optimized leg, and determining a target route according to the plurality of target legs. According to the technical scheme, the problems that in the prior art, when the navigation route of the ship is determined, only a single target is optimized generally, the determined navigation route cannot adapt to the complex navigation environment, and comprehensive benefits are affected are solved, the ship route can be divided into the multiple navigation segments, navigation elements of different navigation segments are comprehensively considered, and the navigation efficiency of the ship is improved. The importance of different optimization targets is balanced, the applicability of the determined route is improved, and the comprehensive income is improved.

The invention belongs to the field of live-action three-dimensional modeling, and discloses a live-action three-dimensional modeling method and device based on cloud native task batch processing scheduling, and the method comprises the steps: calculating aerial triangulation encryption data of a three-dimensional modeling task, and carrying out the block processing of the aerial triangulation encryption data through a task segmentation module; the task scheduling module adjusts operation and task issuing of sub-tasks in real time according to available computing resources of the cloud architecture system, and optimizes the utilization rate of distributed computing resources in real time; the task state monitoring module monitors the execution state of the sub-task in real time; the subtask processing module performs corresponding generation on the image data in each block, and uploads a generation result to a storage resource pool after operation is completed; and after all the subtasks are operated, calling a model merging module to perform overall merging. According to the method, the live-action three-dimensional modeling task and the cloud native task batch processing architecture are combined, operator-level scheduling of the live-action three-dimensional modeling function is achieved through the cloud native system architecture and the resource container pool, and cloud native system resources are efficiently utilized.

The invention provides a method for optimizing reported capacity before participation of fused magnesium group furnaces in primary frequency modulation, belongs to the field of primary frequency modulation of a power system, and is applied to participation of high-energy-consumption fused magnesium furnace loads in primary frequency modulation of a power grid. The method comprises the following steps: 1) establishing an objective function, and taking overall revenue maximization as an objective; 2) determining constraint conditions, and considering frequency modulation requirements, energy demand limitation and product quality; and 3) processing the uncertainty of the adjustment capability of the electric smelting magnesium group furnace, and allowing optimization solution to not meet the constraint among part of cells so as to realize efficient processing of the uncertainty. According to the method, the uncertainty of the adjustment capability of the fused magnesium group furnace is reflected in a probability form through a chance constraint method, and the optimal report capacity participating in primary frequency modulation of a power grid in each day-ahead time period is solved. And a support is provided for a fused magnesium enterprise as a load side demand response resource, reasonable participation in primary frequency modulation of a power grid and day-ahead declaration of frequency modulation capacity.

The invention discloses a three-season cultivation method for intercropping fresh corn with green soy beans and crop rotation of vegetables, relates to the field of agricultural planting, and aims to solve the problems that an existing planting method cannot fully utilize the characteristics of the fresh corn to achieve the yield increasing target and the planting income of the fresh corn is limited. The method comprises the following steps: 1, land selection: selecting fertile, flat and well-drained soil for planting; 2, corn seed selection: selecting a suitable fresh corn variety according to climate conditions of a planting area; 3, planting two seasons of fresh corn in one year, and intercropping green soy beans while planting the fresh corn; fourthly, after the fresh corn is harvested, ploughing, soil preparation and ridging are conducted, and then winter vegetables are planted; 5, performing field management and pest control. According to the method, the growth characteristic that the fresh corn is short in growth cycle is fully utilized, two-season fresh corn can be planted in one year, the planting income of the fresh corn is increased, and the fresh corn is promoted to meet marketization requirements.

The invention relates to the technical field of electricity price interval prediction, in particular to a long-short-term memory neural network electricity price interval prediction method considering multi-source data feature selection and quantile, comprising the following steps: S1, feature selection of multi-source data of electricity price: constructing a multi-source feature factor set; s2, data normalization: performing maximum and minimum normalization processing on the selected characteristic factors and the electricity price data; s3, constructing a quantile-based long and short-term memory neural network prediction model: constructing a quantile regression model based on a long and short-term memory network, and capturing change characteristics of the electricity price; s4, interval prediction based on kernel density estimation: generating a probability density function of the electricity price by adopting a Gaussian kernel function to realize interval prediction of the electricity price; according to the invention, resource configuration is optimized, operation efficiency is improved, stability of power supply is ensured, and stability and fairness of the energy market are promoted.

The invention discloses a power scheduling method and device based on a flexible load and pumped storage, and terminal equipment, and the method comprises the steps: taking the maximum flexible load income in a scheduling period as a first target, taking the minimum output fluctuation variance in the scheduling period as a second target, constructing a cooperative scheduling model, and solving the cooperative scheduling model, obtaining a load scheduling result in the scheduling period, a power generation power value of the target pumped storage power station in the scheduling period and a power utilization power value of the target pumped storage power station in the scheduling period; and then, according to a scheduling result, regulating and controlling a reducible load, a transferable load and a transferable load, and according to the power generation power value and the power utilization power value, controlling a target pumped storage power station. According to the method, pumped storage is combined with three types of flexible loads including the transferable load, the transferable load and the reducible load to cooperatively perform power scheduling, so that the power system can keep stable power output under different working conditions.