Marketing management method, device and equipment based on watershed power stations and medium

Abstract

The invention relates to the technical field of watershed power station management, and provides a watershed power station-based marketing management method, device and equipment and a medium, and the method comprises the steps: obtaining power industry policy information, historical power market information and market transaction rule information corresponding to a watershed power station; according to the power industry policy information, the historical power market information and the market transaction rule information, performing power market analysis and prediction to obtain a market analysis and prediction result; according to the historical electricity market information, making a management strategy and performing corresponding risk assessment to obtain a management strategy scheme and a risk assessment result; and outputting target marketing management strategy information according to the market analysis prediction result, the management strategy scheme and the risk assessment result. Through the technical scheme, the accuracy and timeliness of a marketing strategy are ensured, the authenticity, integrity and safety of data are ensured, the marketing management process is optimized, the management efficiency is improved, and the management cost is reduced.

Description

[Technical field]

[0001] The present application relates to the technical field of river basin power station management, and in particular to a marketing management method, device, equipment and medium based on river basin power stations. [Background technology]

[0002] With the gradual development and improvement of the power market, the power industry has transformed from a "seller's market" to a "buyer's market", and the business objectives of power companies have gradually shifted from focusing on power generation and transmission to market marketing and management services on the power demand side.

[0003] Traditional power marketing management systems, such as power consumption MIS, are mainly oriented to information management of basic business, but they are unable to cope with the complex and changing power market environment and growing customer demand. These systems often lack sufficient intelligent decision-making support capabilities, making it difficult to dig out potential business value and market trends from massive data, thus limiting the initiative and flexibility of power companies in market competition. Specifically, the current power marketing management system has certain design defects, resulting in system instability or a large number of security vulnerabilities. These vulnerabilities are easily exploited by illegal users and hackers to invade and damage the system, thereby threatening the information security and operational stability of power companies. Due to the imperfection of the system, operators are prone to misoperation during use, which in turn brings risks. In addition, some employees with ulterior motives may take advantage of their positions to deliberately tamper with important data, causing new economic risks to the company. At the same time, due to the relatively low level of computer application in power companies, they are unable to fully and deeply understand the market, resulting in a lack of customer resources and market insight. This makes the marketing decisions of power companies often lack scientificity and rationality, and it is difficult to adapt to the rapidly changing market environment. At the same time, due to problems such as the management system and service concepts, the operating system of the power market is relatively backward, the power market cannot be opened more effectively, and the power market marketing work is difficult to carry out smoothly. [Summary of the invention]

[0004] The embodiments of the present application provide a marketing management method, device, equipment and medium based on a river basin power station, aiming to solve many problems existing in the related technology.

[0005] In a first aspect, an embodiment of the present application provides a marketing management method based on a river basin power station, comprising:

[0006] Obtaining power industry policy information, historical power market information and market transaction rule information corresponding to the power stations in the basin;

[0007] Conducting power market analysis and forecasting based on the power industry policy information, historical power market information, and market transaction rule information to obtain market analysis and forecasting results, wherein the market analysis and forecasting results include at least one of the following: power supply and demand forecasting results, power generation cost budget results, power price forecasting results, ancillary service supply and demand forecasting results, ancillary service cost budget results, and ancillary service price forecasting results;

[0008] According to the historical power market information, management strategy formulation and corresponding risk assessment are performed to obtain a management strategy scheme and a risk assessment result, wherein the management strategy formulation includes at least one of the following: power generation quotation strategy formulation, auxiliary service quotation strategy formulation and power generation auxiliary service coordination strategy formulation;

[0009] According to the market analysis and forecasting results, the management strategy plan and the risk assessment results, target marketing management strategy information is output.

[0010] In one embodiment, optionally, the power industry policy information includes at least one of the following: power industry regulatory information, power source construction planning information, grid construction planning information and power policy information;

[0011] The historical electricity market information includes at least one of the following: economic situation information, power grid information, power supply information, supply and demand information, transaction information, auxiliary service information and meteorological climate information;

[0012] The market transaction rule information includes at least one of the following:

[0013] Target area trading rules and cross-region trading rules.

[0014] In one embodiment, optionally, according to the power industry policy information, historical power market information and market transaction rule information, power market analysis and forecasting are performed to obtain market analysis and forecasting results, including:

[0015] According to the power industry policy information, historical power market information and market transaction rule information, a pre-trained power market analysis and prediction model is used to perform predictions to obtain market analysis and prediction results, wherein the power market analysis and prediction model includes a large model constructed using at least one of time series analysis, regression analysis and neural network.

[0016] In one embodiment, optionally, when the market analysis and forecasting results include power supply and demand forecasting results, the power market analysis and forecasting is performed according to the power industry policy information, historical power market information and market transaction rule information to obtain the market analysis and forecasting results, including:

[0017] Based on the historical supply and demand information, meteorological climate information and market trading rules of the power market, the pre-trained time series analysis model is used to make predictions to obtain the power supply and demand forecast results within the preset time period in the future;

[0018] When the market analysis and forecasting result includes a power generation cost budget result, the power market analysis and forecasting is performed according to the power industry policy information, historical power market information and market transaction rule information to obtain the market analysis and forecasting result, including:

[0019] Based on the historical operating cost information and cost influencing factor information in the transaction information, the pre-trained comprehensive cost model is used to make predictions to obtain the power generation cost budget results within the preset time period;

[0020] When the market analysis and prediction results include power price prediction results, the power market analysis and prediction is performed according to the power industry policy information, historical power market information and market transaction rule information to obtain the market analysis and prediction results, including:

[0021] According to the supply and demand information, power policy information, economic situation information, transaction information and meteorological climate information, a pre-trained comprehensive price prediction model is used to make a prediction to obtain the power price prediction result within a preset time period in the future, wherein the comprehensive price prediction model uses at least one of a time series analysis algorithm, a machine learning algorithm and a deep learning algorithm;

[0022] When the market analysis and forecasting results include ancillary service supply and demand forecasting results, the power market analysis and forecasting is performed according to the power industry policy information, historical power market information and market transaction rule information to obtain the market analysis and forecasting results, including:

[0023] According to the ancillary service information, power grid information, power industry policy information and power supply information, a pre-trained ancillary service supply and demand forecasting model is used to make forecasts to obtain ancillary service supply and demand forecasting results;

[0024] When the market analysis and forecasting results include ancillary service cost budget results, the power market analysis and forecasting is performed according to the power industry policy information, historical power market information and market transaction rule information to obtain the market analysis and forecasting results, including:

[0025] According to the auxiliary service cost information, supply and demand information and market transaction rule information in the transaction information, the cost allocation and benefit evaluation method is used to predict the auxiliary service cost budget;

[0026] When the market analysis and prediction results include ancillary service price prediction results, the power market analysis and prediction is performed according to the power industry policy information, historical power market information and market transaction rule information to obtain the market analysis and prediction results, including:

[0027] Based on supply and demand information, electricity policy information, economic situation information, transaction information and meteorological climate information, a pre-trained ancillary service price prediction model is used to perform predictions to obtain ancillary service price prediction results for a preset time period in the future, wherein the ancillary service price prediction model uses at least one of a time series analysis algorithm, a machine learning algorithm and a deep learning algorithm.

[0028] In one embodiment, optionally, the formulating a management strategy and a corresponding risk assessment based on the historical power market information to obtain a management strategy solution and a risk assessment result includes:

[0029] According to the historical power market information and the market analysis and prediction results, multiple management strategy solutions are obtained through different target mathematical models;

[0030] Conduct transaction simulations and evaluate various management strategy options to determine the optimal management strategy option;

[0031] A risk assessment is performed on the optimal management strategy scheme to obtain a risk assessment result.

[0032] In one embodiment, optionally, when the management strategy formulation includes power generation quotation strategy formulation, the management strategy formulation and corresponding risk assessment are performed according to the historical power market information to obtain a management strategy solution and a risk assessment result, including:

[0033] According to the historical electricity market information, a pre-trained power generation capacity prediction model is used to predict the power generation capacity of the basin power station in a future preset time period to obtain a power generation capacity prediction result;

[0034] According to the market transaction rule information, the market analysis forecast results and the power generation capacity forecast results, at least one first target mathematical model is established to obtain multiple electricity quantity and price combination declaration plans;

[0035] Conducting transaction simulation according to the market transaction rule information, and evaluating each electricity quantity and price combination declaration scheme according to different first objective functions;

[0036] According to the evaluation results, select the best electricity quantity and price combination declaration plan;

[0037] Get updated information and data from other companies;

[0038] Based on the updated information and data information of other enterprises, a risk assessment is performed on the optimal electricity quantity and electricity price combination application plan to determine a final electricity quantity and electricity price combination application plan and a first risk assessment result;

[0039] When the management strategy formulation includes auxiliary service quotation strategy formulation, the management strategy formulation and corresponding risk assessment are performed according to the historical power market information to obtain a management strategy solution and risk assessment results, including:

[0040] According to the historical power market information, a pre-trained auxiliary service capacity prediction model is used to predict the auxiliary service capacity of the power station in the basin in a preset time period in the future to obtain an auxiliary service capacity prediction result;

[0041] Establishing at least one second objective mathematical model according to the market transaction rule information, the market analysis prediction results, the auxiliary service price prediction results and the auxiliary service capacity prediction results, so as to obtain multiple auxiliary service quantity and auxiliary service price combination declaration plans;

[0042] Conducting transaction simulation according to the market transaction rule information, and evaluating various auxiliary service quantity and auxiliary service price combination declaration plans according to different second objective functions;

[0043] According to the evaluation results, select the best ancillary service quantity and ancillary service price combination declaration plan;

[0044] Get updated information and data from other companies;

[0045] Conducting a risk assessment on the optimal auxiliary service quantity and auxiliary service price combination declaration plan based on the updated information and data information of other enterprises to determine a final auxiliary service quantity and auxiliary service price combination declaration plan and a second risk assessment result;

[0046] When the management strategy formulation includes power generation auxiliary service coordination strategy formulation, the management strategy formulation and corresponding risk assessment are performed according to the historical power market information to obtain a management strategy solution and risk assessment results, including:

[0047] According to the market analysis prediction results, the power generation capacity prediction results and the auxiliary service capacity prediction results, different target coordination models are established to obtain power generation and auxiliary service coordination strategies;

[0048] A risk assessment is performed on the power generation and ancillary service coordination strategy to obtain a corresponding third risk assessment result.

[0049] In one embodiment, optionally, the method further comprises:

[0050] Obtain the power market information of other enterprises, analyze it and obtain the analysis results;

[0051] Based on the analysis results, the target marketing management strategy information is optimized.

[0052] In a second aspect, an embodiment of the present application provides a marketing management device based on a river basin power station, including:

[0053] An acquisition module, used to acquire power industry policy information, historical power market information and market transaction rule information corresponding to the power station in the basin;

[0054] A prediction module, used to perform power market analysis and prediction based on the power industry policy information, historical power market information and market transaction rule information, and obtain market analysis and prediction results, wherein the market analysis and prediction results include at least one of the following: power supply and demand prediction results, power generation cost budget results, power price prediction results, auxiliary service supply and demand prediction results, auxiliary service cost budget results and auxiliary service price prediction results;

[0055] An evaluation module, used to formulate management strategies and corresponding risk assessments based on the historical power market information to obtain management strategy solutions and risk assessment results, wherein the management strategy formulation includes at least one of the following: power generation quotation strategy formulation, auxiliary service quotation strategy formulation, and power generation auxiliary service coordination strategy formulation;

[0056] The output module is used to output target marketing management strategy information based on the market analysis and prediction results, the management strategy plan and the risk assessment results.

[0057] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned marketing management method based on river basin power stations when executing the computer program.

[0058] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned marketing management method based on river basin power stations are implemented.

[0059] In the above-mentioned scheme implemented by the marketing management method, device, equipment and medium based on the river basin power station, the power industry policy information, historical power market information and market transaction rule information corresponding to the river basin power station are obtained; according to the power industry policy information, historical power market information and market transaction rule information, power market analysis and forecasting are carried out to obtain market analysis and forecasting results, wherein the market analysis and forecasting results include at least one of the following: power supply and demand forecasting results, power generation cost budget results, power price forecasting results, auxiliary service supply and demand forecasting results, auxiliary service cost budget results and auxiliary service price forecasting results; according to the historical power market information, management strategy formulation and corresponding risk assessment are carried out to obtain management strategy schemes and risk assessment results, wherein the management strategy formulation includes at least one of the following: power generation quotation strategy formulation, auxiliary service quotation strategy formulation and power generation auxiliary service coordination strategy formulation; according to the market analysis and forecasting results, the management strategy scheme and risk assessment results, the target marketing management strategy information is output. In the present invention, by obtaining the power industry policy information, historical power market information and market transaction rules information of the basin power station, a comprehensive market analysis result forecast is conducted, and management strategies are formulated according to the forecast results, while risk assessment is conducted, and finally target marketing management strategy information is given, thereby ensuring the accuracy and timeliness of the marketing strategy, and the entire process is completed by the computer to ensure the authenticity, integrity and security of the data. At the same time, the marketing strategy is determined in combination with the actual situation of the basin power station, which optimizes the marketing management process, improves management efficiency and reduces management costs.

Brief Description of the Drawings

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A schematic flow chart of a marketing management method based on a river basin power station according to an embodiment of the present application is shown.

[0062] Figure 2 A schematic flow chart of step S103 in a marketing management method based on a river basin power station according to an embodiment of the present application is shown.

[0063] Figure 3 A flow chart of a marketing management method based on a river basin power station according to an embodiment of the present application is shown.

[0064] Figure 4A block diagram of a marketing management device based on a river basin power station according to an embodiment of the present application is shown. [Specific implementation method]

[0065] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0066] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0067] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0068] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0069] See also Figure 1 , Figure 1 A schematic flow chart of a marketing management method based on a river basin power station according to an embodiment of the present application is shown. The marketing management method based on a river basin power station is used to solve many technical problems existing in related technologies.

[0070] like Figure 1 As shown, according to an embodiment of the present application, a marketing management method based on a river basin power station includes:

[0071] Step S101, obtaining power industry policy information, historical power market information and market transaction rule information corresponding to the power station in the river basin;

[0072] In one embodiment, optionally, the power industry policy information includes at least one of the following: power industry regulatory information, power source construction planning information, grid construction planning information and power policy information;

[0073] In this embodiment, electric power industry regulations, planning information (including power generation construction, grid construction), power safety, energy conservation, environmental protection, electricity prices, reform-related policy information and new technology applications are collected, and the above information is reorganized according to issuing department, issuance time, type, etc., with new policy reminders and link functions, so that operational decision makers can keep abreast of power industry trends.

[0074] The historical electricity market information includes at least one of the following: economic situation information, power grid information, power supply information, supply and demand information, transaction information, auxiliary service information and meteorological climate information;

[0075] In this embodiment, market information such as economic situation (GDP, CPI, etc.), power grid (grid structure, line flow, line maintenance, substation, interconnection line power, interconnection line electricity, congestion information, node information, etc.), power supply (distribution, capacity, installation composition, affiliated power generation group, own power generation capacity, competitor power generation capacity, etc.), demand side (large user power consumption, social power consumption, etc.), transaction (medium- and long-term, short-term, real-time transaction volume, transaction electricity composition, clearing price, transaction price, historical quotation, marginal transaction bid price of supplier, marginal transaction bid price of demand side, transaction scale, main supplier information, etc.), auxiliary services (the quantity and price of auxiliary services, information of power generation enterprises that can provide auxiliary services, supply quantity, etc.), meteorological climate, etc. are collected, and the above information is classified and compiled to provide a basis for electricity price and supply and demand forecasts.

[0076] The market transaction rule information includes at least one of the following:

[0077] Target area trading rules and cross-region trading rules.

[0078] In this embodiment, information such as intra-provincial trading rules and cross-regional trading rules are collected, and the above information is compiled according to issuing department, issuing time, type, etc., with new policy reminders and link functions so that operational decision-makers can keep abreast of the latest electricity market trading rules.

[0079] Step S102, performing power market analysis and forecasting according to the power industry policy information, historical power market information and market transaction rule information, and obtaining market analysis and forecasting results, wherein the market analysis and forecasting results include at least one of the following: power supply and demand forecasting results, power generation cost budget results, power price forecasting results, auxiliary service supply and demand forecasting results, auxiliary service cost budget results and auxiliary service price forecasting results;

[0080] In one embodiment, optionally, step S102 includes:

[0081] According to the power industry policy information, historical power market information and market transaction rule information, a pre-trained power market analysis and prediction model is used to perform predictions to obtain market analysis and prediction results, wherein the power market analysis and prediction model includes a large model constructed using at least one of time series analysis, regression analysis and neural network.

[0082] In this embodiment, the main prediction models used in the power market analysis include time series analysis, regression analysis, neural network, etc. These models predict the supply and demand balance state and electricity price trend of the future power market based on historical power transaction data, market policies, new energy development trends and other information.

[0083] Time series analysis: Collect historical supply and demand data of the power market, including power generation, power consumption, grid load, etc. These data will serve as the basis for time series analysis. Preprocess the data, including data cleaning, missing value processing, outlier detection, etc., to ensure the accuracy and consistency of the data. Because hydropower stations are affected by obvious seasonal changes, the SARIMA time series analysis model is used to capture the trend, seasonality and cyclical characteristics of time series data. The model is trained using historical data to determine the parameters of the model, such as autoregressive coefficients, moving average coefficients, etc. The time series analysis model is used to predict the supply and demand balance of the future power market based on the time series characteristics of historical power trading data. By analyzing the power demand data of the past few years, the seasonal fluctuation pattern of power demand can be found, thereby predicting the trend of future power demand.

[0084] Regression analysis: Collect historical data of the power market, including power generation, power consumption, power prices, weather conditions, economic indicators, etc. These data will be used as input variables of the regression analysis model. The regression coefficient (β) is the inverse matrix of X'X through the formula β = (X'X)(-1), and the intercept is through the formula Calculate, where is the mean of the dependent variable, is the mean of the independent variable, b is the regression coefficient (slope), and by substituting these power market data into the formula, the specific regression coefficient and intercept can be obtained. Then, the model is trained and verified. Through training, the model can learn the relationship between the input variables and the output variables, and verification is used to evaluate the predictive performance of the model to ensure that the model has sufficient accuracy and stability. Use the trained regression model to predict the impact on the power market. Input new data, and the model will output prediction results, such as future electricity price trends, changes in electricity consumption, etc. These prediction results can provide decision support for power market participants.

[0085] Neural network: Collect historical data of the power market, covering multi-dimensional information such as supply and demand, electricity prices, weather, and economic indicators as input features of the neural network. Power market forecasting involves multiple input features, such as supply and demand, electricity prices, weather, and economic indicators. Therefore, a multi-dimensional input neural network architecture that combines a multi-layer perceptron (MLP) and RNN is used to meet the needs of time series forecasting and complex relationship modeling. For time series data, RNN and its variants (such as LSTM and GRU) are the preferred models for processing time series data. They can capture the time dependency in the sequence and adapt to input sequences of different lengths. The network weights are optimized through the back propagation algorithm to minimize the prediction error. During the training process, cross-validation, early stopping and other techniques are used to prevent overfitting and improve the generalization ability of the model. The trained neural network model is used to predict the supply and demand balance and electricity price trend of the future power market. When new feature data is input, the model will output the prediction results, including the supply and demand balance and electricity price change trend in a certain period of time in the future. The forecast results are evaluated and verified. By comparing the differences between actual data and forecast data, the forecast accuracy and reliability of the model are evaluated to provide decision support for power market operators.

[0086] Specifically, the processing procedures are different for different market analysis and forecasting results.

[0087] In one embodiment, optionally, when the market analysis forecast result includes a power supply and demand forecast result, step S102 includes:

[0088] Based on the historical supply and demand information, meteorological climate information and market trading rules of the power market, the pre-trained time series analysis model is used to make predictions to obtain the power supply and demand forecast results within the preset time period in the future;

[0089] In this embodiment, when conducting electricity supply and demand forecasts, based on the collected electricity market information, big data analysis and machine learning algorithms are used to predict future electricity demand and basin hydropower supply, and electricity supply and demand forecasts at different time scales are conducted. By constructing a refined electricity supply and consumption forecast model, seasonal changes, weather influences, and coordination between upstream and downstream power stations are taken into consideration, accurate supply and demand forecasts are achieved for electricity price forecasts, providing a scientific basis for optimizing scheduling and marketing strategies of basin power stations.

[0090] When the market analysis and prediction results include power generation cost budget results, step S102 includes:

[0091] Based on the historical operating cost information and cost influencing factor information in the transaction information, the pre-trained comprehensive cost model is used to make predictions to obtain the power generation cost budget results within the preset time period;

[0092] In this embodiment, the power generation cost calculation focuses on accurately estimating the operating costs of the basin power station. By collecting key data such as environmental protection fees, water fees and water resource fees, electricity purchase fees, labor costs, management costs, depreciation costs, operation and maintenance costs, financial costs, and tax costs, combined with factors such as equipment efficiency, power generation forecasts, and market electricity price fluctuations, a comprehensive cost model is constructed using cost allocation and sensitivity analysis methods. The aim is to achieve transparency in the cost structure and provide decision support for optimizing power station operations, formulating reasonable electricity prices, and financial planning.

[0093] When the market analysis prediction result includes an electricity price prediction result, step S102 includes:

[0094] According to the supply and demand information, power policy information, economic situation information, transaction information and meteorological climate information, a pre-trained comprehensive price prediction model is used to make a prediction to obtain the power price prediction result within a preset time period in the future, wherein the comprehensive price prediction model uses at least one of a time series analysis algorithm, a machine learning algorithm and a deep learning algorithm;

[0095] In this embodiment, the electricity price forecast focuses on the integration of multi-source data and market dynamics, and uses time series analysis, machine learning algorithms (ARIMA, random forest) and deep learning models to conduct in-depth analysis of factors such as electricity market supply and demand trends, policy changes, and weather impacts. Through the study of historical price data and the simulation of future scenarios, accurate prediction of electricity prices is achieved, providing strong support for power stations to formulate marketing strategies, optimize power sales strategies and risk management.

[0096] When the market analysis forecast result includes ancillary service supply and demand forecast result, step S102 includes:

[0097] According to the ancillary service information, power grid information, power industry policy information and power supply information, a pre-trained ancillary service supply and demand forecasting model is used to make forecasts to obtain ancillary service supply and demand forecasting results;

[0098] In this embodiment, the supply and demand forecast of auxiliary services emphasizes the accurate prediction of the demand and supply of auxiliary services such as frequency regulation, peak regulation, and standby in the power system. By integrating power grid operation data, load characteristics, new energy output forecasts, and policy guidance, and using advanced forecasting models and optimization algorithms, the potential and dynamic changes of the auxiliary service market are evaluated, providing a scientific basis for power stations to formulate strategies for participating in the auxiliary service market and optimize resource allocation.

[0099] When the market analysis and prediction results include auxiliary service cost budget results, step S102 includes:

[0100] According to the auxiliary service cost information, supply and demand information and market transaction rule information in the transaction information, the cost allocation and benefit evaluation method is used to predict the auxiliary service cost budget;

[0101] In this embodiment, the auxiliary service cost calculation focuses on accurately evaluating the economic cost of auxiliary services such as power system frequency regulation, peak regulation, and standby. Based on economic factors such as environmental protection fees, water fees and water resource fees, electricity purchase fees, labor costs, management costs, depreciation costs, operation and maintenance costs, financial costs, and tax costs, combined with market supply and demand relationships and transaction rules, cost allocation and benefit evaluation methods are used to provide key data support for reasonable pricing of power stations, optimization of auxiliary service participation strategies, and financial planning.

[0102] When the market analysis prediction result includes an auxiliary service price prediction result, step S102 includes:

[0103] Based on supply and demand information, electricity policy information, economic situation information, transaction information and meteorological climate information, a pre-trained ancillary service price prediction model is used to perform predictions to obtain ancillary service price prediction results for a preset time period in the future, wherein the ancillary service price prediction model uses at least one of a time series analysis algorithm, a machine learning algorithm and a deep learning algorithm.

[0104] In this embodiment, the ancillary service price forecast aims to accurately predict the price trend of ancillary services such as frequency regulation, peak regulation, and standby in the power market. By integrating market supply and demand analysis, historical price data, policy guidance, and new energy development trends, a price forecasting model is constructed using time series analysis and machine learning algorithms to capture market dynamics and provide forward-looking price references for power stations to participate in the ancillary service market, formulate bidding strategies, and manage risks.

[0105] Step S103, according to the historical power market information, a management strategy is formulated and a corresponding risk assessment is performed to obtain a management strategy scheme and a risk assessment result, wherein the management strategy formulation includes at least one of the following: power generation quotation strategy formulation, auxiliary service quotation strategy formulation and power generation auxiliary service coordination strategy formulation;

[0106] like Figure 2 As shown, in one embodiment, optionally, step S103 includes:

[0107] Step S201, obtaining multiple management strategy solutions through different target mathematical models according to the historical power market information and the market analysis and prediction results;

[0108] Step S202, conducting transaction simulation and evaluating various management strategy solutions to determine the optimal management strategy solution;

[0109] Step S203: performing risk assessment on the optimal management strategy solution to obtain a risk assessment result.

[0110] Specifically, when the management strategy formulation includes power generation quotation strategy formulation, step S103 includes:

[0111] According to the historical electricity market information, a pre-trained power generation capacity prediction model is used to predict the power generation capacity of the basin power station in a future preset time period to obtain a power generation capacity prediction result;

[0112] In this step, the core of power generation capacity prediction is to accurately predict the power output capacity of the power plant. By collecting and analyzing historical power generation data, water and meteorological conditions, unit conditions, transmission lines (transmission capacity, congestion, flow, etc.) and market supply and demand information, using big data processing and machine learning algorithms, dynamic evaluation and prediction of power generation potential are carried out. By optimizing the prediction model and improving the prediction accuracy, a scientific basis is provided for power grid dispatching, power trading and power generation planning, ensuring stable and efficient power supply.

[0113] According to the market transaction rule information, the market analysis and forecasting results, and the power generation capacity forecasting results, at least one first target mathematical model is established to obtain multiple electricity quantity and electricity price combination declaration schemes;

[0114] In this step, according to the transaction rules, transaction scale, power generation capacity forecast, power generation cost forecast and electricity price forecast, mathematical models with different objectives (based on cost, based on clearing price, based on historical transactions, based on market game, based on market share, etc.) are established (the model can be manually intervened) to obtain multiple electricity quantity and electricity price combination declaration plans.

[0115] Conducting transaction simulation according to the market transaction rule information, and evaluating each electricity quantity and price combination declaration scheme according to different first objective functions;

[0116] In this step, transaction simulation is carried out according to the transaction rules, the application plans are evaluated according to different objectives (maximum possible profit, maximum loss, etc.), and the optimal combination bidding plan and the over- and under-generation processing plan are proposed.

[0117] According to the evaluation results, select the best electricity quantity and price combination declaration plan;

[0118] Get updated information and data from other companies;

[0119] Based on the updated information and data information of other enterprises, a risk assessment is performed on the optimal electricity quantity and electricity price combination application plan to determine a final electricity quantity and electricity price combination application plan and a first risk assessment result;

[0120] In this step, the risk assessment of power generation bidding strategy focuses on evaluating the potential risks and uncertainties of the formulated bidding strategy. By collecting key information such as market data, policy changes, competitor behavior, etc., using statistical analysis, scenario simulation and expert judgment, a comprehensive assessment of the profitability, market acceptance and potential losses of the bidding strategy is conducted. By quantifying risks, identifying sensitive factors and formulating countermeasures, strong support is provided for optimizing bidding strategies, enhancing market adaptability and protecting corporate interests.

[0121] When the management strategy formulation includes auxiliary service quotation strategy formulation, step S103 includes:

[0122] According to the historical power market information, a pre-trained auxiliary service capacity prediction model is used to predict the auxiliary service capacity of the power station in the basin in a preset time period in the future to obtain an auxiliary service capacity prediction result;

[0123] In this step, the auxiliary service capabilities of itself and its competitors are predicted based on the power generation plan, water and meteorological information, unit conditions, and transmission lines (transmission capacity, congestion, tidal currents, etc.).

[0124] Establishing at least one second objective mathematical model according to the market transaction rule information, the market analysis prediction results, the auxiliary service price prediction results and the auxiliary service capacity prediction results, so as to obtain multiple auxiliary service quantity and auxiliary service price combination declaration plans;

[0125] In this step, according to the ancillary service transaction rules, ancillary service transaction scale, ancillary service provision capacity, ancillary service cost, ancillary service price forecast, etc., mathematical models with different objectives (based on cost, based on clearing price, based on historical transactions, based on market game, based on market share, etc.) are established (the model can be manually intervened) to obtain multiple ancillary service quantity and ancillary service price combination declaration plans.

[0126] Conducting transaction simulation according to the market transaction rule information, and evaluating various auxiliary service quantity and auxiliary service price combination declaration plans according to different second objective functions;

[0127] In this step, transaction simulation is carried out according to the transaction rules, the application plans are evaluated according to different objectives (maximum possible profit, maximum loss, etc.), and the optimal combination quotation plan is proposed.

[0128] According to the evaluation results, select the best ancillary service quantity and ancillary service price combination declaration plan;

[0129] Get updated information and data from other companies;

[0130] Based on the updated information and data information of other enterprises, a risk assessment is performed on the optimal auxiliary service quantity and auxiliary service price combination declaration plan to determine the final auxiliary service quantity and auxiliary service price combination declaration plan and a second risk assessment result.

[0131] In this step, the risk assessment of the ancillary service quotation strategy aims to comprehensively evaluate the market risks and financial impact of the strategy. By analyzing the market supply and demand dynamics, policy orientation, competitor strategies and historical transaction data, using risk identification, quantitative analysis and sensitivity testing methods, the feasibility, profit potential and potential losses of the quotation strategy are evaluated. By identifying key risk factors and formulating risk mitigation measures, the scientificity, rationality and robustness of the ancillary service quotation strategy are ensured.

[0132] When the management strategy formulation includes power generation auxiliary service coordination strategy formulation, step S103 includes:

[0133] According to the market analysis prediction results, the power generation capacity prediction results and the auxiliary service capacity prediction results, different target coordination models are established to obtain power generation and auxiliary service coordination strategies;

[0134] In this step, according to factors such as the unit status, power generation capacity, ancillary service provision capacity, power generation cost, ancillary service cost, power generation demand, ancillary service demand, electricity price, ancillary service price, etc., a coordination model with different objectives (based on cost, based on clearing price, based on historical transactions, based on market game, based on market share, etc.) is established to formulate a power generation and ancillary service coordination strategy.

[0135] A risk assessment is performed on the power generation and ancillary service coordination strategy to obtain a corresponding third risk assessment result.

[0136] In this step, the risk assessment of power generation and ancillary service bidding strategies comprehensively considers the dual impact of the power generation and ancillary service markets. By analyzing market supply and demand, policy environment, competitive situation and internal cost-effectiveness, using risk identification, quantitative assessment and sensitivity testing, we comprehensively examine the profitability, market adaptability and potential risks of the bidding strategy. By accurately locating risk points and formulating targeted prevention and control measures, we ensure that the bidding strategy is stable and effective in a complex market environment and maximize the benefits of power generation and ancillary services.

[0137] Step S104, outputting target marketing management strategy information according to the market analysis and prediction results, the management strategy plan and the risk assessment results.

[0138] In the above technical scheme, in the present invention, by obtaining the power industry policy information, historical power market information and market transaction rules information of the basin power station, a comprehensive market analysis result forecast is conducted, and management strategies are formulated according to the forecast results, and risk assessment is conducted at the same time, and finally the target marketing management strategy information is given, thereby ensuring the accuracy and timeliness of the marketing strategy, and the entire process is completed by the computer to ensure the authenticity, integrity and security of the data. At the same time, the marketing strategy is determined in combination with the actual situation of the basin power station, which optimizes the marketing management process, improves management efficiency and reduces management costs.

[0139] In one embodiment, optionally, the method further comprises:

[0140] Obtain the power market information of other enterprises, analyze it and obtain the analysis results;

[0141] In this step, the electricity market information of the enterprise and other enterprises can be analyzed, such as statistics, analysis and management of data such as declared electricity volume, declared electricity price, transaction volume, transaction price, settled electricity volume and settled electricity price.

[0142] Based on the analysis results, the target marketing management strategy information is optimized.

[0143] In this step, you can collect operating data of other companies, such as competitors' market electricity, transaction electricity prices, power generation costs, etc., to conduct a comprehensive and systematic comparison and analysis. Clarify the benchmarking goals, collect comparative data, build an analysis framework, identify gaps and advantages, and formulate improvement strategies. By deeply analyzing the successful experiences and best practices of benchmarking companies, you can provide your own company with improvement directions and strategies that can be used as reference to promote competitiveness and sustainable development.

[0144] In summary, if Figure 3 As shown, the marketing management method based on river basin power stations includes power market analysis, power generation quotation strategy formulation, ancillary service quotation strategy formulation, power generation ancillary service coordination and benchmarking management, etc.

[0145] Through the above technical solution of the present invention, the following technical effects can be achieved:

[0146] 1. Improve system design and stability

[0147] Intelligent system design: Introducing a more intelligent system design concept, using advanced software architecture and algorithm optimization to ensure high availability and stability of the system. Through strict security testing and vulnerability scanning, potential security issues can be discovered and fixed in a timely manner to ensure the information security of the system.

[0148] Reduce operational risks: By optimizing the user interface and operating procedures, the risk of operator error is reduced. At the same time, strict data permission management is implemented to prevent internal personnel from illegally tampering with data and ensure the authenticity and integrity of the data.

[0149] 2. Enhance decision support capabilities

[0150] In-depth data mining: Integrate advanced data mining and machine learning technologies to conduct in-depth analysis of massive power market data to uncover potential business value and market trends. This will provide a scientific and reasonable basis for the marketing decisions of power companies and improve the accuracy and timeliness of decisions.

[0151] Intelligent prediction and recommendation: Based on the results of data mining, this patent will realize intelligent market prediction and customer demand prediction. Through the construction and optimization of the prediction model, it can provide accurate marketing strategy recommendations for power companies, helping them gain an advantage in the fierce market competition.

[0152] 3. Improve management system and service concept

[0153] Optimize management process: Based on the actual situation of the power stations in the basin, optimize the marketing management process and improve management efficiency. Through process reengineering and standardized management, reduce management costs and improve service quality.

[0154] Strengthen customer service: Introduce a customer-centric service concept and build a complete customer service system. By providing personalized and customized service solutions, we can meet the diverse needs of customers and improve customer satisfaction and loyalty.

[0155] 4. Improve the quality of the marketing team

[0156] Professional training and knowledge updating: Promote power companies to strengthen professional training and knowledge updating of marketing teams. Through regular training courses, seminars and other activities, improve the professional skills and comprehensive quality of marketing personnel, and provide strong talent guarantee for the company's marketing work.

[0157] To sum up, through intelligent system design, enhanced decision-making support capabilities, improved management system and service concepts, and improved the quality of the marketing team, we can comprehensively solve the problems existing in the existing power marketing management system and promote the sustainable and healthy development of power companies.

[0158] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0159] Figure 4 A block diagram of a marketing management device based on a river basin power station according to an embodiment of the present application is shown.

[0160] like Figure 4 As shown, in the second aspect, the embodiment of the present application provides a marketing management device 40 based on a river basin power station, comprising:

[0161] An acquisition module 41 is used to acquire power industry policy information, historical power market information and market transaction rule information corresponding to the power station in the river basin;

[0162] The prediction module 42 is used to perform power market analysis and prediction based on the power industry policy information, historical power market information and market transaction rule information to obtain market analysis and prediction results, wherein the market analysis and prediction results include at least one of the following: power supply and demand prediction results, power generation cost budget results, power price prediction results, auxiliary service supply and demand prediction results, auxiliary service cost budget results and auxiliary service price prediction results;

[0163] An evaluation module 43 is used to formulate a management strategy and a corresponding risk assessment based on the historical power market information to obtain a management strategy solution and a risk assessment result, wherein the management strategy formulation includes at least one of the following: power generation quotation strategy formulation, auxiliary service quotation strategy formulation, and power generation auxiliary service coordination strategy formulation;

[0164] The output module 44 is used to output target marketing management strategy information based on the market analysis and prediction results, the management strategy plan and the risk assessment results.

[0165] In one embodiment, optionally, the power industry policy information includes at least one of the following: power industry regulatory information, power source construction planning information, grid construction planning information and power policy information;

[0166] The historical electricity market information includes at least one of the following: economic situation information, power grid information, power supply information, supply and demand information, transaction information, auxiliary service information and meteorological climate information;

[0167] The market transaction rule information includes at least one of the following:

[0168] Target area trading rules and cross-region trading rules.

[0169] In one embodiment, optionally, the prediction module is used to:

[0170] According to the power industry policy information, historical power market information and market transaction rule information, a pre-trained power market analysis and prediction model is used to perform predictions to obtain market analysis and prediction results, wherein the power market analysis and prediction model includes a large model constructed using at least one of time series analysis, regression analysis and neural network.

[0171] In one embodiment, optionally, the prediction module is used to:

[0172] Based on the historical supply and demand information, meteorological climate information and market trading rules of the power market, the pre-trained time series analysis model is used to make predictions to obtain the power supply and demand forecast results within the preset time period in the future;

[0173] When the market analysis forecast result includes a power generation cost budget result, the forecast module is used to:

[0174] Based on the historical operating cost information and cost influencing factor information in the transaction information, the pre-trained comprehensive cost model is used to make predictions to obtain the power generation cost budget results within the preset time period;

[0175] When the market analysis forecast result includes an electricity price forecast result, the forecast module is used to:

[0176] According to the supply and demand information, power policy information, economic situation information, transaction information and meteorological climate information, a pre-trained comprehensive price prediction model is used to make a prediction to obtain the power price prediction result within a preset time period in the future, wherein the comprehensive price prediction model uses at least one of a time series analysis algorithm, a machine learning algorithm and a deep learning algorithm;

[0177] When the market analysis forecast result includes ancillary service supply and demand forecast result, the forecast module is used to:

[0178] According to the ancillary service information, power grid information, power industry policy information and power supply information, a pre-trained ancillary service supply and demand forecasting model is used to make forecasts to obtain ancillary service supply and demand forecasting results;

[0179] When the market analysis forecast result includes ancillary service cost budget result, the forecast module is used to:

[0180] According to the auxiliary service cost information, supply and demand information and market transaction rule information in the transaction information, the cost allocation and benefit evaluation method is used to predict the auxiliary service cost budget;

[0181] When the market analysis prediction result includes ancillary service price prediction result, the prediction module is used to:

[0182] Based on supply and demand information, electricity policy information, economic situation information, transaction information and meteorological climate information, a pre-trained ancillary service price prediction model is used to perform predictions to obtain ancillary service price prediction results for a preset time period in the future, wherein the ancillary service price prediction model uses at least one of a time series analysis algorithm, a machine learning algorithm and a deep learning algorithm.

[0183] In one embodiment, optionally, the evaluation module is used to:

[0184] According to the historical power market information and the market analysis and prediction results, multiple management strategy solutions are obtained through different target mathematical models;

[0185] Conduct transaction simulations and evaluate various management strategy options to determine the optimal management strategy option;

[0186] A risk assessment is performed on the optimal management strategy scheme to obtain a risk assessment result.

[0187] In one embodiment, optionally, when the management strategy formulation includes power generation quotation strategy formulation, the evaluation module is used to:

[0188] According to the historical electricity market information, a pre-trained power generation capacity prediction model is used to predict the power generation capacity of the basin power station in a future preset time period to obtain a power generation capacity prediction result;

[0189] According to the market transaction rule information, the market analysis and forecasting results, and the power generation capacity forecasting results, at least one first target mathematical model is established to obtain multiple electricity quantity and electricity price combination declaration schemes;

[0190] Conducting transaction simulation according to the market transaction rule information, and evaluating each electricity quantity and price combination declaration scheme according to different first objective functions;

[0191] According to the evaluation results, select the best electricity quantity and price combination declaration plan;

[0192] Get updated information and data from other companies;

[0193] Based on the updated information and data information of other enterprises, a risk assessment is performed on the optimal electricity quantity and electricity price combination application plan to determine a final electricity quantity and electricity price combination application plan and a first risk assessment result;

[0194] When the management strategy formulation includes auxiliary service quotation strategy formulation, the evaluation module is used to:

[0195] According to the historical power market information, a pre-trained auxiliary service capacity prediction model is used to predict the auxiliary service capacity of the power station in the basin in a preset time period in the future to obtain an auxiliary service capacity prediction result;

[0196] Establishing at least one second objective mathematical model according to the market transaction rule information, the market analysis prediction results, the auxiliary service price prediction results and the auxiliary service capacity prediction results, so as to obtain multiple auxiliary service quantity and auxiliary service price combination declaration plans;

[0197] Conducting transaction simulation according to the market transaction rule information, and evaluating various auxiliary service quantity and auxiliary service price combination declaration plans according to different second objective functions;

[0198] According to the evaluation results, select the best ancillary service quantity and ancillary service price combination declaration plan;

[0199] Get updated information and data from other companies;

[0200] Conducting a risk assessment on the optimal auxiliary service quantity and auxiliary service price combination declaration plan based on the updated information and data information of other enterprises to determine a final auxiliary service quantity and auxiliary service price combination declaration plan and a second risk assessment result;

[0201] When the management strategy formulation includes power generation auxiliary service coordination strategy formulation, the evaluation module is used to:

[0202] According to the market analysis prediction results, the power generation capacity prediction results and the auxiliary service capacity prediction results, different target coordination models are established to obtain power generation and auxiliary service coordination strategies;

[0203] A risk assessment is performed on the power generation and ancillary service coordination strategy to obtain a corresponding third risk assessment result.

[0204] In one embodiment, optionally, the device further comprises:

[0205] The analysis module is used to obtain the power market information of other enterprises, analyze it and obtain the analysis results;

[0206] The optimization module is used to optimize the target marketing management strategy information according to the analysis results.

[0207] For the specific definition of the marketing management device based on the river basin power station, please refer to the definition of the marketing management method based on the river basin power station above, which will not be repeated here. Each module in the above-mentioned marketing management device based on the river basin power station can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0208] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method described in the first aspect of the embodiment is implemented when the processor executes the computer program.

[0209] In one embodiment, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the marketing management method based on the river basin power station are implemented.

[0210] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or electronic device can refer to the relevant description in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0211] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0212] It should be understood that, although the terms first, second, etc. may be used to describe the setting unit in the embodiments of the present application, these setting units should not be limited to these terms. These terms are only used to distinguish the setting units from each other. For example, without departing from the scope of the embodiments of the present application, the first setting unit may also be referred to as the second setting unit, and similarly, the second setting unit may also be referred to as the first setting unit.

[0213] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0214] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0215] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0216] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A marketing management method based on a river basin power station, characterized in that: The method comprises: Obtaining power industry policy information, historical power market information and market transaction rule information corresponding to the power stations in the basin; Conducting power market analysis and forecasting based on the power industry policy information, historical power market information, and market transaction rule information to obtain market analysis and forecasting results, wherein the market analysis and forecasting results include at least one of the following: power supply and demand forecasting results, power generation cost budget results, power price forecasting results, ancillary service supply and demand forecasting results, ancillary service cost budget results, and ancillary service price forecasting results; According to the historical power market information, management strategy formulation and corresponding risk assessment are performed to obtain a management strategy scheme and a risk assessment result, wherein the management strategy formulation includes at least one of the following: power generation quotation strategy formulation, auxiliary service quotation strategy formulation and power generation auxiliary service coordination strategy formulation; According to the market analysis and forecasting results, the management strategy plan and the risk assessment results, target marketing management strategy information is output.

2. The method according to claim 1, characterized in that The power industry policy information includes at least one of the following: power industry regulatory information, power source construction planning information, grid construction planning information and power policy information; The historical electricity market information includes at least one of the following: economic situation information, power grid information, power supply information, supply and demand information, transaction information, auxiliary service information and meteorological climate information; The market transaction rule information includes at least one of the following: Target area trading rules and cross-region trading rules.

3. The method according to claim 1, characterized in that According to the power industry policy information, historical power market information and market transaction rule information, power market analysis and forecasting are carried out to obtain market analysis and forecasting results, including: According to the power industry policy information, historical power market information and market transaction rule information, a pre-trained power market analysis and prediction model is used to perform predictions to obtain market analysis and prediction results, wherein the power market analysis and prediction model includes a large model constructed using at least one of time series analysis, regression analysis and neural network.

4. The method according to claim 1, characterized in that: When the market analysis and forecasting results include power supply and demand forecasting results, the power market analysis and forecasting is performed according to the power industry policy information, historical power market information and market transaction rule information to obtain the market analysis and forecasting results, including: Based on the historical supply and demand information, meteorological climate information and market trading rules of the power market, the pre-trained time series analysis model is used to make predictions to obtain the power supply and demand forecast results within the preset time period in the future; When the market analysis and forecasting result includes a power generation cost budget result, the power market analysis and forecasting is performed according to the power industry policy information, historical power market information and market transaction rule information to obtain the market analysis and forecasting result, including: Based on the historical operating cost information and cost influencing factor information in the transaction information, the pre-trained comprehensive cost model is used to make predictions to obtain the power generation cost budget results within the preset time period; When the market analysis and prediction results include power price prediction results, the power market analysis and prediction is performed according to the power industry policy information, historical power market information and market transaction rule information to obtain the market analysis and prediction results, including: According to the supply and demand information, power policy information, economic situation information, transaction information and meteorological climate information, a pre-trained comprehensive price prediction model is used to make a prediction to obtain the power price prediction result within a preset time period in the future, wherein the comprehensive price prediction model uses at least one of a time series analysis algorithm, a machine learning algorithm and a deep learning algorithm; When the market analysis and forecasting results include ancillary service supply and demand forecasting results, the power market analysis and forecasting is performed according to the power industry policy information, historical power market information and market transaction rule information to obtain the market analysis and forecasting results, including: According to the ancillary service information, power grid information, power industry policy information and power supply information, a pre-trained ancillary service supply and demand forecasting model is used to make forecasts to obtain ancillary service supply and demand forecasting results; When the market analysis and forecasting results include ancillary service cost budget results, the power market analysis and forecasting is performed according to the power industry policy information, historical power market information and market transaction rule information to obtain the market analysis and forecasting results, including: According to the auxiliary service cost information, supply and demand information and market transaction rule information in the transaction information, the cost allocation and benefit evaluation method is used to predict the auxiliary service cost budget; When the market analysis and prediction results include ancillary service price prediction results, the power market analysis and prediction is performed according to the power industry policy information, historical power market information and market transaction rule information to obtain the market analysis and prediction results, including: Based on supply and demand information, electricity policy information, economic situation information, transaction information and meteorological climate information, a pre-trained ancillary service price prediction model is used to perform predictions to obtain ancillary service price prediction results for a preset time period in the future, wherein the ancillary service price prediction model uses at least one of a time series analysis algorithm, a machine learning algorithm and a deep learning algorithm.

5. The method according to claim 1, characterized in that The formulating of management strategies and corresponding risk assessments based on the historical power market information to obtain management strategy solutions and risk assessment results include: According to the historical power market information and the market analysis and prediction results, multiple management strategy solutions are obtained through different target mathematical models; Conduct transaction simulations and evaluate various management strategy options to determine the optimal management strategy option; A risk assessment is performed on the optimal management strategy scheme to obtain a risk assessment result.

6. The method according to claim 1, characterized in that When the management strategy formulation includes power generation quotation strategy formulation, the management strategy formulation and corresponding risk assessment are performed based on the historical power market information to obtain a management strategy solution and risk assessment results, including: According to the historical electricity market information, a pre-trained power generation capacity prediction model is used to predict the power generation capacity of the basin power station in a future preset time period to obtain a power generation capacity prediction result; According to the market transaction rule information, the market analysis forecast results and the power generation capacity forecast results, at least one first target mathematical model is established to obtain multiple electricity quantity and price combination declaration plans; Conducting transaction simulation according to the market transaction rule information, and evaluating each electricity quantity and price combination declaration scheme according to different first objective functions; According to the evaluation results, select the best electricity quantity and price combination declaration plan; Get updated information and data from other companies; Based on the updated information and data information of other enterprises, a risk assessment is performed on the optimal electricity quantity and electricity price combination application plan to determine a final electricity quantity and electricity price combination application plan and a first risk assessment result; When the management strategy formulation includes auxiliary service quotation strategy formulation, the management strategy formulation and corresponding risk assessment are performed according to the historical power market information to obtain a management strategy solution and risk assessment results, including: According to the historical power market information, a pre-trained auxiliary service capacity prediction model is used to predict the auxiliary service capacity of the power station in the basin in a preset time period in the future to obtain an auxiliary service capacity prediction result; Establishing at least one second objective mathematical model according to the market transaction rule information, the market analysis prediction results, the auxiliary service price prediction results and the auxiliary service capacity prediction results, so as to obtain multiple auxiliary service quantity and auxiliary service price combination declaration plans; Conducting transaction simulation according to the market transaction rule information, and evaluating various auxiliary service quantity and auxiliary service price combination declaration plans according to different second objective functions; According to the evaluation results, select the best ancillary service quantity and ancillary service price combination declaration plan; Get updated information and data from other companies; Conducting a risk assessment on the optimal auxiliary service quantity and auxiliary service price combination declaration plan based on the updated information and data information of other enterprises to determine a final auxiliary service quantity and auxiliary service price combination declaration plan and a second risk assessment result; When the management strategy formulation includes power generation auxiliary service coordination strategy formulation, the management strategy formulation and corresponding risk assessment are performed according to the historical power market information to obtain a management strategy solution and risk assessment results, including: According to the market analysis prediction results, the power generation capacity prediction results and the auxiliary service capacity prediction results, different target coordination models are established to obtain power generation and auxiliary service coordination strategies; A risk assessment is performed on the power generation and ancillary service coordination strategy to obtain a corresponding third risk assessment result.

7. The method according to claim 1, characterized in that The method further comprises: Obtain the power market information of other enterprises, analyze it and obtain the analysis results; Based on the analysis results, the target marketing management strategy information is optimized.

8. A marketing management device based on a river basin power station, characterized in that: include: An acquisition module, used to acquire power industry policy information, historical power market information and market transaction rule information corresponding to the power station in the basin; A prediction module, used to perform power market analysis and prediction based on the power industry policy information, historical power market information and market transaction rule information, and obtain market analysis and prediction results, wherein the market analysis and prediction results include at least one of the following: power supply and demand prediction results, power generation cost budget results, power price prediction results, auxiliary service supply and demand prediction results, auxiliary service cost budget results and auxiliary service price prediction results; An evaluation module, used to formulate management strategies and corresponding risk assessments based on the historical power market information to obtain management strategy solutions and risk assessment results, wherein the management strategy formulation includes at least one of the following: power generation quotation strategy formulation, auxiliary service quotation strategy formulation, and power generation auxiliary service coordination strategy formulation; The output module is used to output target marketing management strategy information based on the market analysis and prediction results, the management strategy plan and the risk assessment results.

9. A computer device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, wherein the instructions are configured to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Computer executable instructions are stored, and the computer executable instructions are used to execute the method according to any one of claims 1 to 7.

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