4109results about "Market predictions" patented technology

The invention discloses a multi-dimensional intelligent management method and system for whole-process cost, and the method comprises the steps: generating a time-space associated structured cost data cube according to heterogeneous cost data of the stages of project planning, design, construction and completion; outputting a dynamic cost prediction curve and deviation sensitive nodes based on the structured cost data cube; according to the dynamic cost prediction curve, performing multi-party task allocation optimization by using a block chain enabled BIM / CIM collaboration platform, and generating a collaboration instruction set of smart contract coding; outputting a risk probability matrix and an advanced early warning signal based on the collaborative instruction set and the real-time engineering data flow; and according to the risk probability matrix, adopting a multi-objective optimization algorithm to generate an anti-interference decision scheme set, and outputting an optimal cost control strategy after digital twinborn simulation verification. By using the embodiment of the invention, the cost data of each stage of the project can be efficiently integrated, dynamic cost prediction is realized, and collaborative decision and effective risk management are optimized.

The invention discloses a multi-microgrid cooperative scheduling method and system based on the game theory, and the method comprises the steps: generating a dynamic game initial strategy set through a multi-agent strategy network according to the charge state of user energy storage equipment, the charge and discharge efficiency and the real-time scheduling demands of a power grid; based on the dynamic game initial strategy set, adopting an asymmetric Nash bargaining model to carry out distributed negotiation, and generating a balanced benefit distribution scheme; according to the equilibrium benefit distribution scheme, iteratively correcting the energy storage priority index by using a time decay type reinforcement learning algorithm, and generating a dynamic bidding rule containing a supply and demand elastic coefficient and risk compensation; and based on a dynamic bidding rule, energy storage resources are allocated in real time through a decentralized gradient consensus mechanism, and a final collaborative scheduling instruction is generated and synchronized to each micro-grid terminal. According to the embodiment of the invention, the operation efficiency and the self-adaptive capability of the multi-microgrid system can be improved, and the requirements of a future intelligent power distribution network are met.

A method and a system for providing at least one of a process automation and artificial intelligence (PAAI), a market aggregation, or an embedded marketplace relating to transactions are disclosed. In particular, a method and a system for automation of transactions in a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed. A method and a system for managing transactions in a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed. A method and a system for automating processing of transactions in a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed. A method and a system for automated orchestration of a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed. A method and a system for augmenting of services in a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed.

The invention discloses a marketing decision analysis system and method based on artificial intelligence, and aims to improve the intelligent level of marketing decision, optimize resource allocation and improve the rate of return on investment. The system comprises a data analysis module, a prediction evaluation module, an AI decision engine and a knowledge management module. The data analysis module obtains marketing-related data from a plurality of data sources and generates a market insight result. And the prediction evaluation module predicts the potential effect of the marketing scheme by adopting a statistical analysis method based on the market insight result and the historical marketing data, and obtains a marketing prediction result. And the AI decision engine receives the market insight result and the marketing prediction result, and determines an optimal marketing decision scheme based on multi-round dialogue management, knowledge graph construction and decision reasoning technologies. According to the marketing decision analysis system and method, accurate, efficient and reusable marketing decisions are realized in a data driving mode, and the scientificity and the performability of enterprise marketing strategies are improved.

The provided are a price elasticity analysis and prediction method and model based on deep learning. The model consists of a CNN layer and an RNN layer. The method comprises the following steps: S1, collecting historical data and merging the historical data into a multi-dimensional time series dataset; S2, extracting sentiment data and trend data from market news and social media; S3, inputting the data obtained into CNN for data preprocessing and feature extraction; S4, inputting the feature extracted by CNN into RNN for time series analysis; S5, training and optimizing model: using Adam algorithm to adjust the learning rate adaptively, and combining the momentum method and RMSProp algorithm to improve the generalization ability and prediction accuracy of the model. The provided combines the advantages of CNN and RNN, which can understand and predict the complex relationship between price and market behavior more comprehensively and accurately.

The invention relates to a power distribution system optimization method considering a space-time game under vehicle-station-network interaction, and the method employs a double-layer optimization framework integrating the space-time game and dynamic electricity price, an upper decision maker is a power distribution system operator, and the power distribution system operation cost is minimized and the renewable energy consumption rate is maximized. Generating a dynamic electricity price signal in a time-sharing and partition manner; the lower-layer main body comprises an electric vehicle user and a charging station operator, and the electric vehicle user optimizes a charging time period and a charging station and forms charging demand distribution through a non-cooperative game based on a dynamic electricity price signal and a charging service charge by taking the maximum self-charging decision utility as a target; and a charging station operator optimizes and adjusts a charging service fee decision by taking charging demand distribution as input and taking revenue maximization as a target. Compared with the prior art, the method can collaboratively optimize the economic operation of the power grid and the consumption of renewable energy sources, promotes the efficient flow and balanced distribution of resources among regions, and provides decision support for the dispatching of the power distribution network.

The invention relates to the technical field of data processing, and discloses an intelligent supply chain optimization management method and system based on digital AI analysis. The method comprises the steps of collecting supply chain data to construct a resource library; analyzing a sales mode and K-means + + clustering customer features by using LSTM, and predicting demands; inventory parameters and supplier scores are calculated, and a collaborative data set is formed; commodity association is analyzed through a knowledge graph, and a commodity selection decision is generated; resource allocation is carried out by applying multi-objective optimization, and cross-channel inventory collaboration is realized; and an integral rule and a merchant enabling scheme are constructed based on the transaction data, and a digital asset closed-loop mechanism is formed. According to the intelligent supply chain optimization management method based on AI analysis, global visualization, dynamic scheduling and accurate allocation of inventory resources are realized, the technical problem of cross-channel inventory collaborative allocation is solved, and the operation efficiency and the resource utilization rate of the whole supply chain are remarkably improved.

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention, which relates to the technical field of air transportation and income management, discloses a dynamic generation method and system for an air travel price, and the system comprises a user behavior fine-grained tracking module, a dynamic pricing decision engine module, a cross-channel cooperative control module, and a compliance auditing and feedback module. Through a real-time data stream fusion technology, multivariate signals such as competition dynamic signals, user behavior signals and external environment signals are integrated, and second-level strategy response is realized in combination with the online training capability of a reinforcement learning model; through dynamic state space modeling, variables such as demand popularity, user sensitivity and external risk are coded into six-dimensional vectors, and the limitation of a fixed formula is broken through in combination with the nonlinear mapping capability of a deep Q network; through three measures of dynamic modeling, elastic constraint and cross-chain cooperation, the problems of response lag, high compliance risk and split user experience of a traditional pricing technology are solved.

In some embodiments, a configured artificial intelligence system includes a plurality of intelligence models; a scoring system configured to generate know-your-model scores that quantify suitability for specific tasks of each model; a model execution system configured to provide standardized execution environment for the plurality of intelligence models; a training and reinforcement system configured to monitor outcomes relating to decisions or predictions made by the plurality of intelligence models and use outcome data as feedback to reinforce model performance; and a governance and analysis system configured to ensure model operations comply with governance standards. The intelligence controller may be configured to receive task requests, analyze task complexity, decompose tasks into manageable subtasks, and dynamically select appropriate models from the plurality of intelligence models to execute each subtask based on model suitability and performance characteristics.

The invention relates to the technical field of electric power system risk assessment, and discloses a risk assessment system based on an electric power transaction market, which realizes real-time acquisition and dynamic processing of electric power transaction price data through a price data real-time acquisition module in combination with a real-time deviation analysis module, calculates a price deviation value in real time and captures related influence factors, thereby realizing risk assessment of the electric power transaction market. The dynamic parameter adjustment module can establish a correlation model, automatically trigger parameter adjustment when real-time factor fluctuation exceeds a threshold value, and realize automatic updating of computational logic by combining scene adaptation and emergency adjustment subunits, so as to ensure that a price deviation value accurately reflects an actual risk; the risk assessment module combines dynamic parameters to construct a model, calculates risk indexes and divides grades, the risk early warning module generates early warning information in time, and the risk conduction analysis and pressure test unit provides comprehensive risk pre-judgment, assists market subjects to scientifically make plans, and maintains the stability of the power transaction market.

A park-factory-coordinative optimized scheduling method based on a Jacobi iteration method, which belongs to the technical field of energy scheduling optimization. A park-factory hierarchical game model is established, and by means of coordination and optimization between energy scheduling under a complex production constraint and production management and control that takes profit optimization as the goal, an optimal price regulating strategy and an optimal production scheduling strategy are obtained, such that a lower industrial park energy consumption and a higher factory production profit are achieved. A coordinative optimized scheduling model for a park control center and a factory management and control center is a multi-variable multi-constraint complex optimization issue; the solution of a high-order partial differential equation set involved in the model is calculated, and a cyclic iterative optimization algorithm is designed on the basis of a Jacobi iteration matrix, thereby greatly reducing the operation complexity; moreover, such a cyclic iterative algorithm requires a smaller storage capacity of a computer, and is thus suitable to be applied in practice by an engineer.

The invention discloses an electric power spot day-ahead market auxiliary quotation method, and relates to the technical field of electric power system automation, and the method comprises the steps: collecting power grid operation, weather, market and carbon market data through multi-source data fusion preprocessing, and carrying out the processing through a space-time generative adversarial network and a natural language processing model, so as to form a multi-dimensional data set; embedding a power grid power balance constraint by using a graph neural network to carry out topology modeling, and extracting spatio-temporal characteristics by combining a time sequence neural network and an attention mechanism; and a double-layer optimization model is constructed to realize collaborative decision-making of futures and spot markets, and carbon-electricity joint optimization and a dynamic shipping space adjustment mechanism are synchronously integrated. According to the method, the problems of data isolation, inaccurate prediction, insufficient risk control and the like of a traditional quotation method are solved, the quotation precision and the market competitiveness are improved, an auxiliary quotation scheme giving consideration to benefits and risks is provided for power generation enterprises, and the method is suitable for quotation strategy optimization of the current market of electric power spot goods.

The invention provides a new energy output prediction method and system based on Monte Carlo Dropout, and the method comprises the steps: carrying out the probabilistic prediction of new energy output through a Monte Carlo Dropout technology, generating a dynamic prediction result containing a confidence interval, and quantifying the uncertainty of meteorological sudden change and equipment state; secondly, constructing a multi-stage random dynamic programming model, discretizing a prediction interval into multi-scene input, designing a non-linear objective function based on the discharge depth, and synchronously optimizing the electricity purchase cost and the energy storage aging cost; and finally, realizing rolling optimization of the system in combination with a model prediction control framework, and dynamically adjusting an energy storage aging cost weight by updating prediction data and a scheduling instruction on line and embedding an energy storage health state real-time feedback mechanism. The photovoltaic and wind power consumption rate can be remarkably improved, the full life cycle cost of an energy storage system is reduced, and meanwhile, the robustness of a scheduling strategy in extreme weather is ensured.

The invention relates to the technical field of data processing, and discloses a project cost prediction method and system based on a large model, and a storage medium. The method comprises the following steps: standardizing historical cost data to construct a dynamic cost feature knowledge base; inputting the knowledge base into a Transform pre-training large model, and performing fine tuning to obtain a predicted value; obtaining an adjustment coefficient matrix through item similarity clustering and error learning; the design parameters are matched with a knowledge base, and a hierarchical cost table is calculated by using a heterogeneous graph network; and performing time correction on the cost table based on the adjustment coefficient to obtain a prediction result. According to the method, accurate prediction and dynamic adjustment of the project cost are realized, the problem of insufficient element association expression in traditional cost prediction is solved, targeted correction can be carried out according to time and project features, and the prediction accuracy and adaptability are remarkably improved.

The invention relates to the technical field of agricultural intelligent decision making, and discloses an agricultural intelligent analysis and decision making method based on multi-modal data, which comprises a multi-modal agent, an agent collaborative decision making technology, an intelligent decision making method and a crop identification and diagnosis method. The multi-modal intelligent agent comprises an intelligent agent framework and multi-modal capability construction; the intelligent agent collaborative decision-making technology comprises intelligent agent ecological construction, a land parcel analysis method and a land parcel economy evaluation model; the intelligent decision-making method comprises the steps of agricultural knowledge graph construction, understanding and decision making. The crop identification and diagnosis method comprises vision-language model preprocessing and intelligent identification and diagnosis. According to the agricultural intelligent analysis and decision-making method based on the multi-modal data, the technical problem in traditional agricultural decision-making is solved through processing, analysis and technical innovation of the decision-making process of agricultural multi-source data, and accurate analysis and scientific decision-making support is provided for all stages of agricultural production before production, during production and after production.

The invention provides a charging and discharging control strategy optimization method and system of V2G applied to a virtual power plant. The method comprises the steps of constructing a collaborative optimization strategy of comprehensive income data based on electricity price change prediction and peak regulation demand prediction; based on the user behavior mode and the user historical charging behavior data, constructing a collaborative optimization strategy matched with the user demand; constructing a collaborative optimization strategy of battery health degree management based on the health degree quantitative model of the battery; and based on a collaborative optimization strategy of comprehensive income data, user demand matching and battery health degree management, a multi-target dynamic optimization model is constructed, and charging and discharging control strategy optimization of the V2G application in the virtual power plant is realized. According to the method, full-process cooperation of strategy customization-dynamic invitation-user response is realized under the guidance of the virtual power plant, multi-mode charging and discharging control strategy combination is realized, load aggregation is carried out by effectively utilizing the distributed energy storage characteristic of the electric vehicle group, and the power grid regulation capability is maximized.

The invention relates to the technical field of engineering cost supervision, and discloses a process execution supervision system and method suitable for an engineering cost consultation project. According to the method, a project full-cycle data set is acquired through a distributed data acquisition interface, wherein the data set at least comprises a contract file and a progress payment voucher; performing structured feature analysis on the data set to generate a multi-dimensional item feature matrix including time nodes, cost indexes and resource allocation quantities; calling a pre-constructed project cost reference parameter library, and calculating a real-time deviation degree between the multi-dimensional project feature matrix and the reference parameters based on a dynamic time warping algorithm; when the real-time deviation degree exceeds a preset threshold range, automatically triggering a process abnormality recognition engine, and extracting process feature vectors including a construction period delay rate, a cost hyperbranched rate and a material loss rate from the deviation process; and inputting the process feature vector into a pre-trained compliance verification model, and outputting an abnormality diagnosis report including a violation type, a risk level and a responsibility subject.

The invention relates to an internet big data extraction method and device, equipment and a storage medium, and the method comprises the following steps: carrying out distributed crawler collection on an internet data source, obtaining original network data, and converting the original network data into a structured data matrix; and performing multi-level semantic analysis on the matrix, constructing a semantic feature map, and performing topic segmentation and classification to form a topic domain knowledge tree. Association rules in the knowledge tree are further mined, and an implicit knowledge network is constructed. Performing semantic decomposition and expansion on query conditions based on the network to generate expanded query data, and performing similarity matching with the knowledge network to obtain a candidate data set; and finally, performing multi-factor sorting and extraction on the candidate data, and outputting target data, thereby solving the technical problem that in the second-hand car market, due to wide data sources and fuzzy semantics, an existing system has relatively large deviation when performing price prediction and maintenance cost analysis.

A tokenization system / platform / method for minting asset-class backed tokens on a distributed ledger is described herein. A tokenization system / platform receives a token configuration indicating multiple revenue-generating assets of different types and a minting party. The platform configures smart contracts to manage revenue associated with revenue-generating assets, including a first function to mint asset-class backed tokens backed by the plurality of assets and a second function to computationally apportion and distribute revenue among blockchain addresses of token owners. After deploying the configured smart contracts on the distributed ledger, the platform initiates blockchain transactions to invoke the smart contract functions, including a first transaction that mints tokens and assigns them to purchasing users' blockchain addresses, and one or more second transactions that distribute cryptocurrency corresponding to revenue among token owners. The system / platform / method may provide for fractional ownership and management of different types of revenue-generating assets and / or portfolios of revenue-generating assets.

The invention relates to the technical field of project cost management, and discloses a project cost dynamic management method. The method comprises the following steps: marking a timestamp of a data updating node in a historical cost database, extracting a consumption index of each subentry project in a specified interval before and after the timestamp, and constructing a consumption feature set dynamically associated with the cost; inputting the consumption feature set into an optimized neural network, calculating the deviation degree of the same sub-project in the construction stage and the design stage through time sequence comparative analysis, and generating the difference representation of the actual cost and the plan cost; a material price fluctuation coefficient is used as an adjusting variable, a fuzzy comprehensive evaluation model is adopted to carry out multi-dimensional reasoning on the difference representation, and the influence weight value of each cost component on the cost deviation is obtained; key parameters related to the market supply and demand index and the construction progress deviation rate are mined, the key parameters are input into a pre-trained evaluation model, a screening threshold value is dynamically adjusted according to a model output numerical value, and core parameters with influence weight values higher than the adjusted threshold value are screened out.

The invention relates to the technical field of power systems, in particular to a carbon neutralization-oriented peak-valley electricity price dynamic excitation mechanism design and user response prediction system, which comprises a data acquisition module, a load characteristic analysis module, a dynamic regulation and control module, a sample evaluation module and an execution module. The system obtains power grid operation, user power consumption behaviors and new energy power generation data through a multi-source data access interface, and realizes partition load optimization and new energy adaptation in combination with a load classification model, a dynamic excitation framework and an energy storage optimization scheduling scheme. The method can accurately predict the user response, optimizes the peak-valley electricity price strategy, improves the new energy consumption capability, and assists the realization of a carbon neutralization target.

The invention relates to the technical field of building engineering cost intelligent estimation, and discloses a building engineering cost intelligent estimation and checking method fusing a domain knowledge graph and a large language model, and the method comprises the following steps: S1, building a building engineering domain knowledge graph (KG), integrating a quota library, a material price library and historical project data, and carrying out the calculation of a building engineering domain knowledge graph (KG); and defining an entity relationship and a dynamic updating rule. According to the intelligent building engineering cost estimation and checking method fusing the domain knowledge graph and the large language model, a closed loop of knowledge structuring (KG) + semantic comprehension (LLM) + multi-modal reasoning is realized for the first time, engineering cost management is promoted to be transformed from experience driving to data-knowledge dual driving, a reusable technical normal form is provided for the field of intelligent construction, and the engineering cost is estimated and checked. The method solves the problems of efficiency, precision, dynamics and interpretability of traditional cost management, has technical innovation and practical value, and provides core technical support for digital transformation of constructional engineering.

The invention discloses a STREAM framework-based generative engine optimization method and system, and relates to the technical field of GEO generative engine optimizing.The STREAM framework-based generative engine optimization method comprises the steps of collecting content data of a brand on each platform; analyzing the semantic structuring index S of the brand content, and evaluating the semantic structuring degree of the brand content; analyzing the time correlation coefficient T, and evaluating the timeliness of the brand content; analyzing the credible source cross authentication number R, and evaluating the credibility and authority of the brand content; analyzing the user resonance index E, and evaluating the resonance degree of the brand content; analyzing the content consistency score A, and evaluating the consistency of the content in different platforms and modes; and dynamically adjusting the weight of each dimension through a multi-modal search weight dynamic fine tuning algorithm M, calculating a GEO comprehensive score based on the weight of each dimension and the scores of the five dimensions of S, T, R, E and A, and optimizing the brand content according to the GEO comprehensive score. And an operable technical path is provided for the brand to establish cognitive advantages in the generative AI era.

The invention provides a customer group dynamic division method and system, and relates to the technical field of customer group management.The method comprises the steps that firstly, commodity related data are collected in real time through a multi-channel data collection module, commodity behavior analysis is conducted in combination with service requirements, features are extracted according to analysis results, and commodities are divided into multiple groups; and then, dynamically dividing customers by combining service requirements, generating initial customer group information, and optimizing and adjusting the customer group information according to the updated user portrait information. And finally, performing preference calculation according to the optimized customer group information, and generating a personalized push strategy. According to the method, through dynamic division and optimization of customer group information, unique requirements and behavior characteristics of each customer can be better met, and personalized services are provided.

The invention belongs to the technical field of power systems, and particularly relates to a transmission and distribution cooperative scheduling optimization method based on multi-agent deep reinforcement learning. The method comprises the following steps: firstly, establishing a transmission and distribution cooperative scheduling distributed framework, and respectively establishing distributed optimization models taking the minimization of the operation cost of a power transmission network and the minimization of the operation cost of each active power distribution network as targets; secondly, converting a transmission and distribution collaborative scheduling problem into a Markov game model with a multi-agent benefit balance characteristic, and designing a collaborative multi-agent architecture; then interacting with a transmission and distribution interconnection environment through each agent to obtain historical interaction information, storing the historical interaction information into an experience pool, and training by using an MADDPG algorithm to realize iterative updating and optimization of an Actor network and a Critic network of each agent; and finally, realizing local optimization control of the power transmission network and each active distribution network by using an Actor network.

The invention relates to the technical field of intelligent analysis, and particularly discloses an indoor decoration intelligent analysis system and method based on user demands and spatial data, which utilizes an artificial intelligence technology based on deep learning to analyze and understand decoration demand information of a user and a plan graph of a house. Semantic features of user decoration demands and structure size features of a house are captured, and a decoration material list and quotation thereof are intelligently generated based on semantic interaction features between the user decoration demands and the structure size of the house. Therefore, the user can quickly obtain the decoration scheme meeting the requirements of the user and know the decoration cost in advance, the decoration efficiency is improved, and the time and the cost of the owner are saved.

The invention provides a financial risk intelligent early warning method based on multi-source data fusion, and the method comprises the steps: separating the periodic characteristics of income data from the abnormal transaction influence through a time series decomposition technology if the transaction volume fluctuation ratio in an income stability index exceeds a preset threshold value; a weighted moving average algorithm is adopted to smooth records with low data alignment precision, and a smoothed income fluctuation sequence is generated; according to the node level risk set, modeling is carried out on the periodic characteristics of the dynamic risk contribution degree, the change trend of an income fluctuation sequence in a future time period is predicted through a long and short-term memory network, and a dynamic risk prediction sequence containing abnormal transaction influence is generated; and if the risk value of a certain time point in the dynamic risk prediction sequence exceeds a preset warning threshold value, identifying a high risk point driven by the abnormal transaction influence through an isolated forest algorithm, and generating an early warning signal set containing a timestamp and a regional market difference. The financial risk management capability of an enterprise can be effectively improved.

The invention provides a retail data processing method and system for retail commodity display evaluation. According to the method, customer interaction behavior data and shelf three-dimensional space data are synchronously acquired through a monitoring device deployed in a commodity display area, and a commodity displacement track and human body action characteristics are extracted from a video stream by using an image recognition technology. And establishing a space-time correlation model to match the behavior data with spatial distribution, and generating an evaluation parameter group including a space occupancy density index, a visual focus distribution rate and a contact behavior delay value. A two-factor optimization mechanism is innovatively introduced, vertical direction optimization factors are generated according to the ratio relation between the space density and the visual focus, dynamic adjustment of the goods shelf height direction distribution density and the horizontal display orientation is achieved, and a closed-loop feedback type display decision-making system is constructed. According to the technical scheme provided by the invention, adaptive optimization of the commodity display layout is realized, the commodity display efficiency is effectively improved, and the customer interaction experience is optimized.

The invention relates to the field of optical storage, and particularly discloses an optical storage system cooperative control method and device, a terminal and a medium, and the method comprises the steps: collecting multi-dimensional data in real time, including photovoltaic array data, user side load data, power grid side information data, meteorological data and weather forecast information; generating a photovoltaic power generation power prediction curve, a user load demand prediction curve and a power grid electricity price prediction curve by using the multi-dimensional data through a machine learning model; and taking the current state of the optical storage system and each prediction curve as input parameters, carrying out optimization problem solving based on a multi-objective optimization function and constraint conditions through a model prediction control algorithm, generating an optimal control sequence in a period of time in the future, and controlling corresponding equipment through the optimal control sequence. According to the method, the response speed to uncertainties such as illumination abrupt change and load fluctuation is increased, source-storage-load-network coordination is realized, the sub-optimal problem of independent control of each unit is avoided, the operation cost is reduced, and renewable energy consumption is improved.