3290results about "Market predictions" patented technology

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 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.

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.

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.

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 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.

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 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 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.

The invention relates to the technical field of public transportation systems, and particularly discloses a bus departure scheduling method, which comprises the following steps of S1, integrating multi-dimensional data; s2, a dynamic prediction model containing machine learning parameters is adopted to calculate the passenger demand in the future period; s3, calculating the number of required vehicles according to the predicted demand, the vehicle capacity and the dynamic load coefficient; s4, constructing a multi-objective function including energy consumption optimization, and solving the optimal departure interval and route; and S5, according to the real-time data, correcting a scheduling scheme, collecting real-time feedback data through a passenger mobile application, analyzing the emotion and demand of the passenger by using a natural language processing technology, based on feedback intention recognition of an emotion analysis model, constructing a passenger demand knowledge base in combination with historical complaint data, and optimizing a dynamic prediction model and a scheduling strategy. Through technology integration and system innovation, the static and single bottleneck of traditional scheduling is broken through, and an intelligent solution considering efficiency, low carbon and user experience is provided for urban buses.

A system for managing future costs associated with a product includes a future requirement system programmed to estimate an amount of resources required for manufacturing, distributing, and selling the product at a future point in time. The system includes an adverse contingency system configured to identify adverse contingencies and calculate changes in costs associated with obtaining the amount of resources at the future point in time. The system includes a smart contract system programmed to autonomously configure and execute a smart futures contract based on the amount of resources required and on the changes in costs to manage the future costs associated with the product.

The invention provides a medicinal and edible product screening method and system based on artificial intelligence, and the method comprises the steps: building a user physique and demand portrait through collecting user health condition data and food preference data; acquiring medicinal and edible raw material information and nutriology data based on the portrait, and constructing a medicinal and edible knowledge graph; analyzing market product information based on the knowledge graph, and generating a market insight report; generating an initial product formula scheme by using a large language model and a binary activation recurrent neural network; and outputting a mature product formula through trial production, user feedback and iterative optimization. According to the method, the problems that traditional medicinal and edible product development depends on expert experience and is lack of personalized and scientific basis are solved, intelligentization and data driving of the whole process of product development are realized, and the success rate and market adaptability of product development are improved.

The invention discloses a real-time cost management and control method and system for a pig raising enterprise. The method comprises the following steps: collecting structured data of a whole breeding period of boars, piglets and pork pigs in real time; based on the structured data, real-time cost indexes of the boars, the piglets and the pork pigs are calculated through a preset algorithm; comparing the real-time cost index with a theoretical standard value based on experimental field data; comparing the real-time cost index with a future cost threshold predicted by machine learning; when the real-time cost index exceeds the theoretical standard value or the future cost threshold value, it is judged that the cost is abnormal; and executing a corresponding exception adjustment operation according to the type of the exception cost. According to the invention, the technical problems of fragmentization of data acquisition, lagging of cost accounting, insufficient anomaly detection precision and low manual response efficiency in the prior art are solved.

The invention provides a new energy station digital intelligent operation system, a control method, equipment and a medium, and relates to the technical field of new energy. According to the system, a marketization electricity price prediction result and a new energy output prediction result are output through an electricity price prediction module and a new energy output prediction module respectively, and an energy storage optimization scheduling module and a planning layout decision module work cooperatively. And combining the marketization electricity price prediction result and the new energy output prediction result to realize operation strategy optimization and capacity configuration decision of the new energy station, and solving and outputting a capacity configuration scheme corresponding to the new energy device and the energy storage device. According to the technical scheme, the multi-module collaborative new energy station digital intelligent operation system is constructed, so that full-link closed-loop optimization from prediction to scheduling to planning is realized, the adaptability of the system to power market fluctuation can be remarkably enhanced, and the operation income of the new energy station is ensured.

A dynamic real estate valuation system based on multiparametric market indicators, which includes the following: a valuation engine configured to generate real-time results for property valuation; a multitude of distributed data ingestion and processing units configured to capture heterogeneous data sources, including historical property transaction data, real-time property listings, zoning and land use records, macroeconomic indicators, geospatial data, environmental sensor outputs, and sentiment-derived metrics; a model orchestration control unit comprising a stack of machine learning models, wherein the models include at least a gradient boosting decision tree model, a long-short-term memory (LSTM) time series forecaster, and an enhancement learning module that iteratively optimizes the model parameters based on the observed evaluation accuracy; a data contextualization controller configured to apply dynamic weighting to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) that includes an edge processing unit (EPU), geolocation circuitry, secure communication interfaces and a touch-based user interface; a valuation book subsystem configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger; wherein the system is designed to continuously recalibrate its valuation results by comparing the predicted valuations with the actual sales or rental prices, and wherein the physical terminal is designed to produce a legally certifiable valuation document with embedded provenance data.

The invention provides an elastic computing power scheduling method and device based on space-time prediction and a medium, and belongs to the technical field of cloud computing and edge computing. The method comprises the following steps: acquiring a historical load, a satellite cloud picture, meteorological data, a burst flow mark, an energy price and a green electricity proportion in real time; constructing a fusion prediction model which comprises a multi-modal feature coding module, a space-time attention fusion module and a prediction output layer; a multi-objective optimization function is constructed, cost, delay and resource fluctuation are minimized, and green rewards are maximized; solving a task allocation variable and a resource adjustment amount by adopting mixed integer programming; the actual green electricity utilization rate is monitored, and the green reward coefficient is dynamically adjusted to correct prediction and scheduling deviations. According to the method, the computing power demand fluctuation of different areas in the future time period can be predicted in advance, so that resource allocation is dynamically adjusted, resource idleness and waste are reduced, and the utilization rate of computing power resources is remarkably improved.

The invention discloses a charging pile management method and system based on data analysis, and belongs to the field of charging management.The charging pile management method based on data analysis comprises the following steps that a time sequence prediction model is constructed based on historical charging session data, environmental factors, special time period information and the real-time occupancy state of a charging pile, predicting peak periods and valley periods of charging demands in different areas from a few hours to one day in the future; the method has the beneficial effects that the charging service price is dynamically adjusted by predicting the charging demand peak period and the charging demand valley period and combining the overall utilization rate prediction result of the charging piles in the region in the period, so that the regional charging price in the charging demand peak period is relatively high, and the regional charging price in the charging demand valley period is relatively low; therefore, the user is guided to actively perform peak shifting charging or select other areas for charging, the load distribution of the charging piles is optimized, the queuing pressure in the peak period of the charging demand is relieved, and the infrastructure utilization rate in the valley period of the charging demand is improved.

The invention belongs to the field of automobile evaluation, and provides an automobile live risk evaluation system and method based on a large model, and the method comprises the steps: receiving voice input information of an evaluator in a vehicle on-site inspection process; extracting component state information of the target vehicle based on the structured check item text; acquiring historical maintenance record data of the target vehicle, and performing association fusion on the historical maintenance record data and the component state information; based on a large language model, performing joint reasoning on a currently input inspection label and the historical maintenance record, constructing a causal chain related to an accident risk, and generating at least one subsequent to-be-inspected item; generating voice guide content and feeding back the voice guide content to the evaluator so as to prompt the evaluator to continue to check the target item of the target vehicle; gradually perfecting the risk check path of the target vehicle until the risk chain closed loop is completed; and calling an estimation formula to calculate a residual estimation value of the target vehicle, and outputting a structured risk assessment report.

The invention discloses an electric power spot market electric charge settlement system and method fused with a block chain technology, and the system comprises a core settlement layer which is used for storing the electricity price and user identity data, and executing an electric charge settlement rule through a built-in intelligent contract module; the high-frequency transaction processing layer is used for processing a transaction process through an under-chain caching technology and uploading data in the transaction process to the core settlement module; an external data interface is integrated on one side of the high-frequency transaction processing module and used for acquiring electric quantity and load data in real time, and a cross-chain gateway is arranged on the other side of the high-frequency transaction processing module and used for achieving synchronous settlement of electric charge and capital flow. According to the method, the efficiency and transparency defects of a traditional mode are overcome, the performance and energy consumption bottleneck of the block chain technology in an electric power scene is broken through, high efficiency, safety, expandability and environmental protection benefits are achieved, and a credible, real-time and compliant digital settlement infrastructure is provided for an electric power spot market.

The invention relates to the field of electricity transaction, in particular to an electricity transaction intelligent algorithm platform based on block chain consensus. The method comprises the following steps: acquiring a real-time green electricity encrypted data set, uploading the real-time green electricity encrypted data set to a block chain node, and generating an unactivated digital green certificate data set; based on the non-activated digital green certificate data set, generating a grid-connected verification instruction, updating the non-activated digital green certificate data set to a tradable state, and generating a tradable digital green certificate data set; obtaining a power grid dynamic parameter set, and generating a green certificate time pricing data set through a space-time right dynamic pricing strategy based on the power grid dynamic parameter set and the tradable digital green certificate data set; and according to the green certificate time pricing data set, the digital green certificate is cancelled after verification in the power utilization process of the user, and a green power consumption traceability report is output. In the green electricity transaction process, fair and orderly development of the green electricity transaction market is promoted.

The invention discloses an intelligent supply and demand recommendation matching system based on scientific and technological achievement conversion, and relates to the technical field of resource services, the system comprises an information input module, an information management module, a supply and demand matching module, a pricing prediction module and a conversion recommendation module, user input information is subjected to distributed storage and classified labeling, and a knowledge graph is constructed; after preliminary supply and demand matching is carried out, semantic path reasoning is used for calculating the matching degree and sorting is carried out, a reinforcement learning model is adopted, the transaction price is dynamically predicted based on the transaction frequency, the industry permeability and the technical life cycle, based on a multi-factor evaluation algorithm, the matching degree, the transaction price satisfaction degree and the historical transaction rate are integrated, and a personalized recommendation list is generated; the problems that in the existing scientific and technological achievement conversion process, information of a recommendation matching system is scattered, opaque and single in evaluation dimension, and deep semantic association and user demand diversity are neglected are solved, and accurate and efficient matching services are provided for a scientific and technological achievement provider and a technical demander.

The invention discloses a Nash negotiation-based energy management scheduling method for an optical storage and charging integrated energy station, and the method comprises the steps: constructing a multi-target optimization model which comprehensively considers the interest demands of an electric vehicle user, a charging station operator and a power grid operator; determining participants of Nash negotiation as a charging station operator, a power grid operator and an electric vehicle user, and determining a strategy space of each participant; on the basis of the Nash negotiation theory, all the benefit subjects are regarded as negotiation participants, utility functions of all the benefit subjects are constructed, and negotiation reference points of all the benefit subjects are determined; solving a Nash equilibrium solution of the multi-objective optimization model, and determining an optimal energy management scheduling strategy through iterative negotiation; and according to the optimal energy management scheduling strategy, real-time control and scheduling are carried out on the optical storage and charging integrated energy station. According to the invention, the flexibility, adaptability and economy of an energy scheduling scheme can be improved, and multi-party win-win and stable and efficient operation of the system are realized.

The invention discloses an electric power spot market electric power transaction intelligent electricity price prediction and electric power dispatching system and method. The system comprises a data acquisition module, a preprocessing module, an electricity price prediction module, a load prediction module, a dispatching optimization module, a transaction decision module, a real-time monitoring module and a risk management module. The method comprises the following steps: step 1, multi-source data acquisition and standardization processing; 2, carrying out the collaborative prediction of the electricity price and the load; step 3, scheduling optimization and transaction strategy generation; 4, real-time monitoring and risk hedging feedback are carried out; according to the method, nonlinear and high-dimensional data can be effectively processed, the adaptability to distributed energy access and real-time meteorological changes is improved, new energy power generation data and market transaction strategies are effectively integrated, data processing can be fully carried out when complex fluctuation of an electric power spot market is coped, the model generalization ability is improved, and the method is suitable for large-scale popularization and application. Optimization of multiple time scales is achieved, and supplement of electricity price prediction and power dispatching is achieved.