93results about How to "Profit maximization" patented technology

The invention discloses a new energy electric power energy storage collaborative scheduling method and system based on a spot market, and relates to the technical field of electric power system optimization energy storage, and the method comprises the steps: collecting the historical data of an electric power system and spot market prediction data; performing target modeling, and inputting initial data; optimizing the model, and performing day-ahead scheduling and intra-day scheduling; and real-time monitoring and prediction updating are carried out, and rolling optimization scheduling is carried out. The method has the beneficial effect that charging and discharging optimization is carried out in combination with the state of the energy storage system. The energy storage system can participate in frequency modulation service and quickly respond when frequency fluctuation occurs in the power grid. According to the process, the economic benefit of the energy storage system is maximized through real-time scheduling and frequency modulation service, and the stability of the power grid is improved. Through combination of electricity price arbitrage, frequency modulation service and rolling optimization, the energy storage system can effectively deal with uncertainty in a complex market environment, and the goal of revenue maximization is achieved.

The invention provides a new energy green electricity transaction quantity reporting strategy algorithm based on deep learning. The new energy green electricity transaction quantity reporting strategy algorithm based on deep learning comprises the following steps: a, data collection: acquiring multi-source data from an electricity market, a weather forecast system, historical power generation data and a load prediction system, the data including historical electricity price, weather conditions, power generation power and power grid load data, and recording as X = {x1, x2,..., xn}, wherein xi is a time sequence data feature vector. According to the new energy green power transaction quantity reporting strategy algorithm based on deep learning, the problem of prediction errors caused by randomness and intermittency in new energy power generation is innovatively solved through a deep learning technology, and the prediction accuracy of the new energy green power transaction quantity reporting strategy algorithm based on deep learning is improved. Power generation fluctuation and weather change features are extracted through a multilayer convolutional neural network, and extreme weather events are automatically identified in combination with an attention mechanism, so that the algorithm can more accurately capture market fluctuation.

The invention relates to a large-scale new energy base scheduling method based on uncertainty of an electric power spot market, and the method comprises the steps: collecting historical data of spot market node marginal electricity price and wind-solar power generation power, and generating a day-ahead market decision scene set comprising the spot market electricity price and new energy unit output through a Monte Carlo sampling method; establishing a two-stage stochastic optimization model for the large new energy base to participate in the day-ahead market by considering the uncertainty of the spot market, and establishing a model for participating in the real-time market; calculating a day-ahead market bidding strategy, a thermal power generating unit start-stop plan and a wind turbine generator maintenance plan in combination with the uncertain day-ahead market decision scene set; and according to high-precision short-period price prediction and renewable energy power prediction values, a real-time market bidding strategy and a charging and discharging strategy of energy storage equipment are calculated, and profit maximization in a large-scale new energy base market environment is realized. The problem of low economic benefit during energy storage scheduling of a large-scale new energy base in an electric power spot market is solved.

The invention belongs to the technical field of electric power system operation control, and relates to a unit operation strategy optimization method under cooperation of an electric energy market and a capacity market, which comprises the following steps: 1, constructing a decision optimization model of an upper-layer power generation enterprise participating in an electric energy spot market and an electric power capacity market, 2, constructing a lower-layer electric energy spot market clearing model, the method comprises the steps of 1, establishing a lower-layer power capacity market clearing model, 4, converting a lower-layer market clearing problem into a KKT optimality condition, 5, carrying out model linearization processing, and 6, calling a Gurobi solver to solve a linearized mixed integer linear programming problem. According to the method, the collaborative operation relationship between the electric energy spot market and the electric power capacity market is fully considered, short-term optimization of the electric energy market and long-term planning of the electric power capacity market supplement each other, the linkage between the markets is enhanced, the overall market efficiency is further improved, and the reliability adequacy of the system is effectively guaranteed.

The invention relates to the technical field of optimization operation, in particular to a virtual power plant low-carbon optimization operation method considering carbon transaction, which comprises the following steps: S1, virtual power plant framework design and modeling: designing an operation transaction framework of a low-carbon virtual power plant, and modeling each unit in the virtual power plant; s2, depicting the uncertainty of wind and light output: depicting the uncertainty of wind and light output through scene generation and reduction; s3, obtaining an income and carbon emission optimization objective: constructing an objective function taking income maximization and carbon emission minimization as objectives; s4, optimizing an output scheme by using a DQN algorithm: optimizing a next-day output scheme by using the DQN algorithm; s5, a deviation punishment mechanism is implemented, wherein when the actual wind and light output deviates from the prediction, the deviation punishment mechanism is implemented; according to the method, the next-day output prediction and scheduling optimization capability of the virtual power plant is improved, and achievement of revenue maximization and carbon emission minimization is ensured.

The invention relates to an electricity consumption real-time scheduling method, system and equipment for a machine room. A load electricity consumption prediction curve of a load in a peak and flat valley time period of one day is established according to historical data of load electricity consumption in the machine room; the time period is judged, and the real-time energy storage capacity of the energy storage system in the machine room and the real-time power consumption of the load are obtained in real time; and analyzing and comparing the real-time data with the load power consumption prediction curve, estimating whether the energy storage system can meet the power consumption demand of the load and the charging time required by the energy storage system, reducing the power consumption of the load when the energy storage system cannot meet the estimated power consumption demand of the load in the sharp time period and the peak time period, and reducing the charging time required by the energy storage system in the valley time period and the flat time period. Charging the energy storage system, and if the actual charging time is less than the estimated charging time, reducing the power consumption of the load; meanwhile, the invention discloses a system and equipment adopting the method. The power utilization in the machine room can be adjusted in real time, and benefit maximization is ensured.

The invention relates to a system for electric energy comprehensive regulation and control of a park power grid and a regulation and control method using the system, and the regulation and control method comprises the steps: S1, a data collection module of a terminal collection and regulation system collects environment data related to energy efficiency, and transmits the data to an energy management system through a communication module, thereby achieving real-time data synchronization; s2, the energy management system receives and gathers the energy consumption data transmitted by the terminal acquisition and regulation system, and performs data analysis; s3, the energy management system predicts photovoltaic power generation and load power based on historical data, environmental data and market information; s4, the park energy management system gives a dynamic time-of-use electricity price according to the day-ahead net load power prediction curve in combination with the supply-demand relationship and each distributed energy price; s5, based on a pre-established model, combining the dynamic time-of-use electricity price, and adopting a master-slave game method to solve a scheduling scheme; and S6, the terminal acquisition and regulation system receives the control instruction issued by the energy management system.

The invention provides a load balancing routing method and system of a satellite network. The method comprises the following steps: performing satellite operation state acquisition on satellite nodes through a satellite sensor network to obtain satellite original state data; performing network state sensing on the satellite original state data to obtain a satellite network state data set; performing task demand mining on the communication participation nodes to obtain a task demand trend data set; and performing task demand prediction modeling and task demand prediction according to a preset deep learning model and the task demand trend data set to obtain a task demand prediction data set. Through the technical methods of satellite state perception, task demand prediction, distributed resource optimization, reinforcement learning resource scheduling, a privacy protection mechanism and the like, the energy efficiency of a multi-satellite system is improved, task load balance and coordination in a cloud edge collaborative environment are realized, and privacy security in an air-space-ground integrated network is guaranteed.

The invention provides a packaging structure of an intelligent power module, a driving circuit and an electronic product. The packaging structure comprises a packaging body, a first base island, a second base island, a third base island, a driving chip, a first power tube and a second power tube, the driving chip, the first power tube and the second power tube are respectively arranged on the first base island, the second base island and the third base island, the driving chip, the first power tube and the second power tube form a half-bridge driving structure, and each signal end is connected with a corresponding chip pin; wherein the first base island and the second base island are arranged on the same side of the third base island and are sequentially arranged along the side edge of the third base island, and the projection of the first base island, the projection of the second base island and the projection of the third base island on the side edge are partially or completely overlapped. The original structure and pin functions of the intelligent power module are reserved, the size of the module is greatly reduced, and the intelligent power module has the advantages of small size, good heat dissipation, high power and the like, and is suitable for realizing the design of a corresponding miniaturized circuit board.

The embodiment of the invention provides an AIGC service-based pricing method and related equipment, which are used for determining an efficient prompt pricing algorithm for an AIGC service platform. Comprising the steps of obtaining a generative artificial intelligence (GAI) model set, and determining all GAI sub-models and model prompt information in the GAI model set; obtaining a user prompt set and the prompt ambiguity of each user prompt intention; determining expected utility mapping according to the GAI model set and the user prompt set; according to the expected utility mapping, the model prompt information, all the GAI sub-models and the prompt ambiguity, determining a target GAI model, and determining a target prompt strategy; and determining isomorphic pricing information corresponding to the prompt pricing information under the same prompt ambiguity, thereby determining a target association relationship between the target prompt strategy and prompt cost information under the target GAI model according to the isomorphic pricing information, and determining target pricing information corresponding to the prompt pricing information under different prompt ambiguity according to the target association relationship.

The invention discloses a multi-virtual power plant intelligent scheduling decision-making method and system considering uncertain risks, and the method comprises the steps: constructing an objective function, and comprehensively optimizing the response degree, the total response cost and the uncertain risks of provincial scheduling demands; setting constraint conditions in the virtual power plant intelligent scheduling decision model, wherein the constraint conditions comprise setting of upper and lower limit constraints of response power, setting of response rate constraints, setting of response time constraints and setting of load state constraints of an energy storage system; data collection and preprocessing: the virtual power plant collects diversified data through a user layer measurement unit, and a decision-making layer preprocesses the data; and scheduling and adjusting in real time. The model proposed by the invention not only ensures that each virtual power plant can maximize own profit when participating in market bidding through an upper layer optimization strategy quotation coefficient, but also gives consideration to the stability, safety and economic benefits of the whole power grid, and the design ingeniously balances the local benefit of a single VPP and the global optimality of the power grid, and improves the market bidding efficiency. And the decision-making process is more scientific and reasonable.

The invention provides a railway transportation pricing method, device and equipment based on a graph neural network, and relates to the technical field of data analysis, and the method comprises the steps: initializing a population of a genetic algorithm based on freight rate time sequence data; obtaining a predicted traffic volume; calculating the fitness of each individual in the population; and iterating the initial population based on the plurality of fitness values and the genetic operation of the genetic algorithm, and repeatedly inputting the iterated population into the traffic volume prediction model until a target individual meeting a set condition exists in the initial population. According to the method, the dynamic demand function between the railway freight pricing and the freight volume is accurately fitted based on the graph neural network, a global optimization solution model is designed in combination with the genetic algorithm, and the optimal railway pricing strategy can be obtained through the solution of the model, so that the overall income of the railway freight business is maximized. The method not only can effectively capture non-linear changes of market demands, but also has strong adaptability, and can be flexibly adjusted under different market conditions.

The invention discloses a large-electric-quantity range-extending type electric vehicle calibration method. An overall calibration framework comprises driving mode calibration, energy management calibration, battery low-temperature protection calibration, battery discharge limit calibration and electric drive external characteristic limit calibration. Adaptive calibration is carried out on functional modules such as mode management and energy management of a large-electric-quantity extended-range electric commercial vehicle, the whole vehicle only discharges a battery under most working conditions, the consumption ratio of electricity and oil is reasonably distributed, meanwhile, the system configuration advantages are exerted, the dynamic property of the whole vehicle is guaranteed as much as possible, meanwhile, overcharge protection of the battery is considered, and the system reliability is improved. The car using income of a user is maximized, greater value is created for the user, and core parts are also protected; the VCU dynamically adjusts the allowable power generation power of the APU in real time according to the available charging power of the battery, it is guaranteed that the battery cannot be overcharged under the dynamic working condition, the braking energy efficiency and the braking performance are guaranteed, abrasion of mechanical braking is reduced, and the battery is protected.

The invention relates to the technical field of power grid power, in particular to a power distribution network power control method and system, intelligent equipment and a readable storage medium, and aims to solve the technical problems of ensuring power maximization and realizing lowest cost of a power distribution network while realizing zero static error of voltage control of the power distribution network. In order to achieve the purpose, according to the power distribution network attributes of the power distribution network, the power compensation strategy of the power distribution network can be determined, according to the power distribution network voltage and the power compensation strategy, the power compensation instruction of the power distribution network is obtained, and according to the power compensation instruction, power control is performed on the power distribution network. Different power compensation strategies are determined, and a power compensation instruction is obtained according to the voltage of the power distribution network and the power compensation strategies, so that power compensation of the power distribution network is realized, power control of the power distribution network is realized, zero-static-error adjustment of the voltage of the power distribution network can be realized, and the power compensation efficiency is improved on the premise of ensuring that the voltage of the power distribution network is not out of limit. The voltage and power coordination control of the power distribution network is realized, and the benefit maximization of the power distribution network is ensured.

The invention discloses a parking lot management efficiency evaluation method based on parking frequency, and the method comprises the steps: collecting vehicle access time, license plate number, parking space number, vehicle type and parking lot real-time occupancy rate data through sensors, cameras and intelligent barrier gate systems which are disposed at an entrance, an exit and parking spaces of a parking lot; performing data preprocessing on the acquired data to form a standardized data set; key indexes are extracted from the data set; based on the historical data and the external influence factors, establishing a parking demand prediction model by adopting a time sequence analysis method, and predicting the parking demand in a specified time period in the future; based on the extracted key indexes, corresponding weight factors are set, a management efficiency evaluation model is constructed, and a parking lot management efficiency score is calculated; comparing the calculated efficiency score with historical data to generate an evaluation report; and according to the predicted parking demand and the evaluation report, implementing a dynamic pricing model, and optimizing the utilization rate of the parking lot.

The invention discloses a clustering method, system and device based on double-layer optimization, and belongs to the technical field of information processing. Comprising the following steps: obtaining product quality data, consumer category data and product promotion cost data in a database; according to the obtained data and based on the position decision variable, obtaining probability data of each product selected by each category of consumers; an upper-layer optimization model and a lower-layer optimization model are constructed to form a double-layer optimization model, an equilibrium strategy set is obtained through the double-layer optimization model, and the double-layer optimization model is linearized to obtain a solved optimal strategy set. Compared with the prior art, the method has the advantages that by comprehensively considering merchant behaviors and applying a game theory method and a mathematical optimization technology, the accuracy of ranking data and results is remarkably improved, meanwhile, the efficiency of a ranking algorithm is greatly improved through algorithm optimization, and the capacity of quickly responding to market changes is achieved.

The invention discloses a dynamic optimal matching method for an online car-hailing aggregation platform. The dynamic optimal matching method comprises the following steps: S1, defining variables required by approximate dynamic planning; s2, constructing an approximate dynamic programming matching model for maximizing the total revenue of the aggregation platform; and S3, based on the real-time analyzed variables required by the approximate dynamic programming, value approximation is performed on the expected profit in the approximate dynamic programming model by adopting an approximate dynamic programming algorithm, so that a platform vehicle driver and passenger matching strategy can be determined, and dynamic optimal matching can be realized. The dynamic approximate programming algorithm is introduced into the online car-hailing driver and passenger pricing matching strategy, more accurate pricing matching strategy adjustment is realized through real-time analysis of passenger demands, driver supply and market dynamics, the platform can continuously optimize the pricing matching strategy, the profit is maximized, the travel experience of the user is maintained, and the user experience is improved. The method not only improves the efficiency and stability of the whole system, but also can promote the reasonable utilization of resources, creates a win-win situation for drivers and passengers, and plays a positive role in the sustainable development of the platform.

The invention provides a data asset assessment method and system based on multilevel analysis, and relates to the technical field of data processing, the method comprises the steps that an initial population of a genetic algorithm is set, each individual represents a data processing parameter combination, and the data processing parameter combination is a combination of weight coefficients in data application scores; calculating the fitness of the individual according to the data application score; selecting the corresponding individual to enter the next generation according to the fitness of the individual; performing crossover and mutation operation on the selected individuals to generate new individuals, and repeating selection, crossover and mutation operation until a termination condition is met to obtain corresponding optimized parameters; and calculating a regional index according to the quality analysis score and the final data application score. The processing accuracy can be improved.

The invention discloses a multi-agent implicit collaborative decision-making method based on dark tactical guidance, which is used for solving the problems of unreliable communication and even complete lack of collaboration in a multi-agent system. A'dark tactical 'concept is introduced to guide behaviors of the intelligent agent. The method comprises three core components: a tactical distribution network, an intelligent agent strategy network and a group of dark tactics, and the three components are synchronously optimized in a training stage; during execution, each agent selects a dark tactics which is most adaptive to the current overall and individual states through the tactics distribution network according to information of other agents which are observed by the agent and can be acquired by the agent; the selected dark tactics are then entered into the policy network along with individual observations of the agents to generate action decisions. According to the method, cooperation and competition among the intelligent agents can be considered, the benefit of the overall strategy of the intelligent agent cluster is maximized, and the cooperation efficiency of the multiple intelligent agents in an uncertain environment is improved.

The invention belongs to the technical field of data processing, and particularly discloses a real-time stowage scheduling method and device based on passenger roll cabin continuous space simulation, and the method comprises the steps: obtaining the ship name of a stowage ship, the lane line length of the ship and the quota stowage weight, and determining the vehicle-mounted configuration weight corresponding to each stowage condition queue; determining that the current vehicle arrives at a ship gate, and determining vehicle specification information and weight information based on the license plate number; updating a stowage condition queue according to the parking position of the current vehicle, and generating a stowage scheme of a next to-be-stowage vehicle under a preset limiting condition; updating the real-time stowage data based on the stowage data correction factor to obtain an updated stowage condition queue to be displayed to a dispatcher; and circularly generating a real-time stowage scheme of each vehicle based on the updated stowage condition queue until the ship loading of the vehicle is completed or the ship warehouse capacity is fully loaded. According to the invention, the ship stowage efficiency can be improved, and the balance between the stowage efficiency and earnings can be realized.

The invention provides a mobile energy storage scheduling method and system and a readable storage medium, and the method comprises the steps: obtaining the node distance between any two nodes in a target power distribution network and the node time consumed when mobile energy storage moves to the space between any two nodes, respectively constructing a distance matrix and a time matrix according to the node distance and the node time; assuming that an equivalent switch exists between the mobile energy storage and accessible nodes thereof, and constructing a scheduling equivalent scheme of the mobile energy storage; obtaining the total revenue of the operator according to the distance matrix and the time matrix, and constructing a target function by taking the maximization of the total revenue of the operator as a target; constructing constraint conditions of the target function, wherein the constraint conditions comprise a power distribution network operation constraint, a renewable energy output constraint and a mobile energy storage operation constraint; and solving the objective function to obtain an optimal scheduling planning scheme of mobile energy storage. According to the invention, the optimal scheduling planning scheme of mobile energy storage can be obtained, so that the economic benefit of a power distribution network operator is improved.

The invention provides an image detection method, system and device, a medium and a program product. The image detection method comprises the following steps: inputting a to-be-detected image into an image character classification model, and screening to obtain a first image without artificially added characters in the to-be-detected image; wherein the image character classification model is obtained based on ResNet model training. According to the invention, based on the image character classification model, the live-action image classification model and the spliced image classification model which are obtained through independent training of the ResNet model, the first image, the second image and the third image in the to-be-detected image can be obtained through screening in sequence, and the visual effect requirements of no artificial addition of characters, non-live-action images and non-spliced images are met at the same time. Images which do not have attraction and are low in value are screened out, images with potential business infringement risks are avoided, and potential losses of companies are reduced; and meanwhile, a super-resolution image can be reconstructed through the image quality improvement model, so that the visual quality and competitiveness of the image are greatly improved.

The invention relates to a quotation scheme determination method and device for an electricity market and computer equipment. The method comprises the following steps: respectively acquiring market state information of an electricity purchasing party and an electricity selling party; and processing the market state information by using a pre-constructed comprehensive bidding model to obtain profit information and candidate quotation schemes, and determining target quotation schemes of the electricity seller and the electricity buyer according to the profit information and the candidate quotation schemes. By adopting the method, the demand of the market subject in pursuing benefit maximization can be met.

The invention discloses a direct air-cooling unit cold end exhaust steam waste heat utilization system combined with an absorption heat pump, comprising a steam turbine intermediate pressure cylinder, the steam turbine intermediate pressure cylinder is respectively connected to an absorption heat pump and a steam turbine low pressure cylinder through pipelines, an exhaust device is arranged below the steam turbine low pressure cylinder, a heat exchanger is connected to the bottom of the exhaust device, the heat exchanger and the absorption heat pump form a circulation loop through pipelines, the exhaust device side wall is connected to an exhaust pipe, the other end of the exhaust pipe is connected to an air cooling island, an absorption heat pump and a mechanical ventilation tower, the air cooling island is connected to the exhaust device through a condensate pipe, the absorption heat pump is connected to the condensate pipe through a pipe, the absorption heat pump is also connected to a heat network heater through a pipe to form a circulation loop, the heat network heater is also connected to the heat exchanger through a pipe, and the mechanical ventilation tower is also connected to the condensate pipe through a pipe. The invention solves the problem that the exhaust steam waste heat existing in the prior art cannot be effectively recycled and utilized, and the exhaust steam waste heat loss causes a significant reduction in the comprehensive utilization rate of energy.

The invention discloses a commodity module sorting method and device, electronic equipment and a storage medium, when a commodity module sorting request sent by a user is received, user feature data and a commodity module candidate set are obtained, and for each candidate commodity module in the commodity module candidate set, the commodity module sorting request is sent to the user; estimating a commodity conversion rate corresponding to each commodity in a commodity set of the candidate commodity module based on the commodity module sorting model and the user feature data, and selecting a target commodity conversion rate with a high conversion rate to determine a corresponding target commodity; and estimating the module conversion rate of the candidate commodity module to which each target commodity belongs, selecting the target module conversion rate with high conversion rate to determine the corresponding target commodity module, and outputting each target commodity module and the target commodity contained in each target commodity module. According to the invention, the commodity module and the commodity row preferred by the user can be displayed at the front position, and the accuracy of obtaining the commodity preferred by the user is improved.

The application relates to the technical field of blockchains, and discloses a work authorization circulation method and system based on a blockchain, which comprises the following steps: integrating a heterogeneous cross-chain authorization network of works to be authorized; extracting work features of the works to be authorized, generating work unique identifiers of the works to be authorized, performing symmetric encryption on the works to be authorized, storing the encrypted data block set in layers into the heterogeneous cross-chain authorization network, notarizing the works to be authorized, and obtaining notarized works; configuring authorization parameters of the notarized works, calculating market supply and demand coefficients of the notarized works, calculating time decay factors and reputation decay factors of the notarized works, generating an access control matrix of the notarized works, and constructing authorization tokens of the notarized works; calculating the satisfaction degree of an authorization request and the access control matrix, reading a current token state of the authorization token, generating an authorization credential of the authorization request; decrypting the notarized works, and generating an authorization report of the notarized works. The application can improve the confidentiality and security of work authorization circulation.

The present invention discloses a reactive power optimization method for distribution network of electric vehicle station taking into account transfer reinforcement learning, describes the charging and discharging scheduling problem of a single electric vehicle as a Markov decision process, and solves the decision process by adopting the PPO method; adopts the TRL technology to migrate the trained EV strategy model to vehicles of different types and regions to obtain the EVS model; models the EVS model into a reinforcement learning intelligent agent to participate in the reactive power optimization of the distribution network, and uses the PPO method to train the reactive power optimization strategy of the intelligent agent, and the reactive power regulation model of the distribution network based on the EVS; uses the real time-varying electricity price, EV travel behavior distribution, and PDN load to train the model of the Markov decision process, and finally obtains the charging and discharging strategy model and reactive power regulation strategy model of the EVS, and tests and deploys the model online. This method can effectively utilize the reactive power output capacity of the EVS, improve the power loss of the distribution network, and thus improve the operation efficiency and stability of the entire power grid.

The present invention discloses a multi-type electricity market joint bidding method applicable to pumped storage power stations. First, electricity price forecasting is performed to establish a CVaR risk model; the risk model is introduced into the optimization decision-making target, the objective function and constraints are set, and a risk-return model for the pumped storage power station to participate in the electricity market and the auxiliary service market is established; the constraints such as power generation / pumping rate constraints and reservoir capacity constraints are considered, and the model is solved with the goal of maximizing the benefits. There is a solution, that is, the pumping capacity, power generation capacity, frequency regulation capacity and the benefits obtained by participating in the market of the pumped storage power station in each period within an optimization cycle are obtained, and the bidding strategy with the best economic efficiency is obtained. The bidding method of the present invention introduces confidence and risk preference coefficients to consider the fluctuation and uncertainty risks of market electricity prices. By optimizing capacity allocation, the production is arranged scientifically to ensure the best economic efficiency within the feasible range, and the economic benefits of the pumped storage power station are further improved.

The invention discloses a transportation route optimization method and device, and a computer program product, and the method comprises the steps: obtaining a to-be-optimized transportation route, constructing a directed graph, and carrying out the traversal of the directed graph based on a depth-first search mode, so as to obtain all combined paths; constructing an N graph model based on all the obtained combined paths; and solving the constructed N-graph model based on a maximum flow minimum segmentation algorithm. Combinations conforming to conditions are quickly and efficiently traversed through a graph model and a DFS method, internal storage and processing speed bottlenecks caused by multiple times of connection of full-quantity orders are avoided, and then minimization of transportation cost and maximization of income are achieved by using an N graph and a maximum flow and minimum cut method. The problems that in the related technology, combination and optimization of lines often depend on experience and manual calculation, efficiency is poor, and a globally optimal solution is difficult to achieve are solved.

The present invention relates to a dynamic spectrum sharing system and method based on a two-stage Stackelberg game in a drone-assisted communication network, comprising: in the first stage of the Stackelberg game, a base station maximizes its own utility function to obtain an optimal solution of price, share reward and commission incentive, broadcasts the optimal solution to a drone, and the drone substitutes the optimal solution into its own utility function; in the second stage of the Stackelberg game, the drone substitutes the optimal solution broadcasted by the base station into its own utility function and maximizes the utility function to obtain an optimal solution of spectrum resources and price, feeds back the optimal solution of spectrum resources and price to the base station, broadcasts the optimal solution of price to a user, the user substitutes the optimal solution of price into his own utility function and maximizes the utility function to obtain an optimal solution of spectrum resources, and feeds back the optimal solution of spectrum resources to the base station and the drone; and dynamically adjusts the price, spectrum resources, commission incentive and share reward until the base station, the drone and the user obtain maximum benefits.