Display method and system for new energy power station transaction strategy

Abstract

The invention provides a new energy power station transaction strategy display method and system, and the system comprises a data interface module which is used for obtaining a load prediction data sequence, a quotation data set, and actual transaction data; the integrated display and interaction module is used for generating an interactive display interface and realizing linkage display and interaction of a load prediction data sequence, a quotation data set and actual transaction data in different functional areas; the compliance verification engine module is in communication connection with the integrated display and interaction module and is used for verifying the input quotation data in real time according to a preset rule and feeding back a result; and the local storage and calculation module is used for encrypting and storing all service data and supporting query, comparison and redisk analysis based on historical data in an offline environment. According to the invention, load prediction, quotation data and actual transaction results are presented in a centralized manner through the integrated linkage display interface, so that the convenience, safety and continuity of transaction decision making are improved.

Description

Technical Field

[0001] This application relates to the field of power market trading and data visualization technology, and more specifically, to a method and system for displaying trading strategies for new energy power plants. Background Technology

[0002] In the context of the deepening marketization of electricity trading, especially spot trading, new energy power plants need to make refined bids on trading platforms daily based on load forecasts. However, existing technical solutions face the following key issues that urgently need to be addressed in supporting trading decisions:

[0003] First, the decision-making process is inefficient and error-prone. Currently, traders mainly rely on general-purpose computer software. Key decision-making data, such as load forecast curves representing 96 time points in the next day and 11 price points reflecting the power plant's output capacity from 0% to 100%, are usually scattered across different files, charts, or software interfaces. This fragmented data makes it difficult for traders to quickly and intuitively conduct horizontal comparisons and comprehensive analyses, severely impacting decision-making efficiency.

[0004] Secondly, the decision-making process lacks real-time safety safeguards. Quotation operations rely heavily on human experience and self-discipline, and the system generally lacks the function of real-time, automatic compliance verification of quotation data. Traders are highly susceptible to triggering violations due to negligence when filling out quotation forms, such as submitting prices exceeding the market's maximum / minimum price limits. Such violations, once they occur, will directly lead to invalid transactions and even financial penalties.

[0005] Secondly, the continuity and reliability of the decision-making process are insufficient. Existing tools heavily rely on stable network connections to obtain data and access historical records. Once a network outage or fluctuation occurs at the power plant site, decision-making will immediately come to a standstill, traders will be unable to access historical data for reference and review, and the continuity of decision-making cannot be guaranteed.

[0006] Finally, the decision-making strategies lack systematic optimization support. Load forecasting, pricing schemes, and actual transaction results are isolated from each other, lacking effective comparative and in-depth analysis tools. Strategy optimization mainly relies on traders' personal memories and experience summaries, making it difficult to form a data-driven, iteratively improveable scientific decision-making loop.

[0007] In summary, existing technologies fail to provide a dedicated solution that integrates unified data display, real-time compliance and risk control, offline continuous analysis, and intelligent decision support. Summary of the Invention

[0008] The present invention aims to overcome the shortcomings of the prior art and provide a method and system for displaying trading strategies for new energy power plants, so as to improve the convenience, security and continuity of trading decisions.

[0009] In a first aspect, the present invention provides a method for displaying a trading strategy for a new energy power plant, characterized in that the method includes: Obtain the load forecast data sequence of new energy power plants in the future target period, as well as the set of quotation data corresponding to multiple preset output levels; In different functional areas of the same interactive display interface, the first curve formed by the load forecast data sequence, the second curve formed by the quotation data set, and the relevant actual transaction data are displayed in an integrated and linked manner. Upon receiving new bid data input, the bid data is verified for compliance in real time based on preset electricity market trading rules; The load forecast data sequence, the price quote data set, and the actual transaction data are synchronously stored locally, and the offline historical data comparison analysis and strategy review functions are provided based on the stored data. In response to user interaction with the content displayed in any functional area, the content displayed in other related functional areas will be updated accordingly.

[0010] In a second aspect, the present invention provides a display system for a new energy power plant trading strategy, used to implement the display method described in the first aspect, the system comprising: The data interface module is used to acquire load forecast data sequences, price quote data sets, and actual transaction data; An integrated display and interaction module is used to generate the interactive display interface, enabling the linked display and interaction of the load forecast data sequence, the quotation data set, and the actual transaction data in different functional areas; The compliance verification engine module is connected in communication with the integrated display and interaction module, and is used to verify the input quotation data in real time according to preset rules and provide feedback on the results. The local storage and computing module is used to encrypt and store all business data, and supports querying, comparing and reviewing based on historical data in an offline environment.

[0011] Thirdly, the present invention provides a readable medium including executable instructions, which, when executed by a processor of an electronic device, cause the electronic device to perform any of the methods described in the first aspect.

[0012] Fourthly, the present invention provides an electronic device including a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method as described in any of the first aspects.

[0013] This invention provides a method and system for displaying trading strategies for new energy power plants. Through an integrated, interconnected display interface, load forecasts, bidding data, and actual trading results are presented centrally, avoiding interface switching and enabling decision-makers to conduct rapid and intuitive comprehensive analysis, potentially improving decision-making efficiency by over 50%. Real-time compliance verification checks are completed instantly upon bid input, providing immediate alerts for violations such as exceeding price limits or non-monotonic curves, reducing compliance risks caused by human error to near zero. Local encrypted storage and offline analysis ensure that decision-makers can still access historical data and conduct review studies even in cases of network instability or interruption, guaranteeing uninterrupted trading decision-making. The intelligent correlation display of AI prediction models and convenient comparison and review functions of historical data provide traders with data-driven decision support, facilitating continuous optimization of bidding strategies. The interactive interface and interactive response mechanism adapted to touch operation lower the operational threshold of professional software, making it more suitable for power plant on-site office environments.

[0014] The further effects of the aforementioned non-conventional preferred method will be explained below in conjunction with specific embodiments. Attached Figure Description

[0015] To more clearly illustrate the embodiments of the present invention or the existing technical solutions, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram illustrating a method for displaying a new energy power plant trading strategy according to an embodiment of the present invention; Figure 2 A flowchart illustrating real-time compliance verification in a method for displaying a new energy power plant trading strategy, as provided in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating another method for displaying a new energy power plant trading strategy according to an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the composition of a display system for a new energy power plant trading strategy according to an embodiment of the present invention. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0018] In the context of the deepening marketization of electricity trading, especially spot trading, new energy power plants need to make refined bids on trading platforms daily based on load forecasts. However, existing technical solutions face the following key issues that urgently need to be addressed in supporting trading decisions:

[0019] First, the decision-making process is inefficient and error-prone. Currently, traders mainly rely on general-purpose computer software. Key decision-making data, such as load forecast curves representing 96 time points in the next day and 11 price points reflecting the power plant's output capacity from 0% to 100%, are usually scattered across different files, charts, or software interfaces. This fragmented data makes it difficult for traders to quickly and intuitively conduct horizontal comparisons and comprehensive analyses, severely impacting decision-making efficiency.

[0020] Secondly, the decision-making process lacks real-time safety safeguards. Quotation operations rely heavily on human experience and self-discipline, and the system generally lacks the function of real-time, automatic compliance verification of quotation data. Traders are highly susceptible to triggering violations due to negligence when filling out quotation forms, such as submitting prices exceeding the market's maximum / minimum price limits. Such violations, once they occur, will directly lead to invalid transactions and even financial penalties.

[0021] Secondly, the continuity and reliability of the decision-making process are insufficient. Existing tools heavily rely on stable network connections to obtain data and access historical records. Once a network outage or fluctuation occurs at the power plant site, decision-making will immediately come to a standstill, traders will be unable to access historical data for reference and review, and the continuity of decision-making cannot be guaranteed.

[0022] Finally, the decision-making strategies lack systematic optimization support. Load forecasting, pricing schemes, and actual transaction results are isolated from each other, lacking effective comparative and in-depth analysis tools. Strategy optimization mainly relies on traders' personal memories and experience summaries, making it difficult to form a data-driven, iteratively improveable scientific decision-making loop.

[0023] In view of this, the present invention provides a method for displaying a trading strategy for new energy power plants. See also... Figure 1 The image shows a specific embodiment of a method for displaying a new energy power plant trading strategy provided by the present invention. In this embodiment, the method for displaying a new energy power plant trading strategy includes:

[0024] Step 101: Obtain the load forecast data sequence of new energy power plants in the future target period, as well as the set of quotation data corresponding to multiple preset output levels; Specifically, this step is the system's data initialization phase. Through automatic synchronization via the communication module or manual import, it acquires the load forecast data sequence for the new energy power plant during the target trading period (usually the next day) and the corresponding set of bid data for multiple preset output levels. The load forecast data sequence specifically refers to an ordered set of predicted hourly or 15-minute power generation output values ​​for a future trading cycle (usually one day). In the electricity spot market, the standard format is predicted values ​​at 96 time points per day (corresponding to 24 hours divided into 15-minute intervals). The bid data set specifically refers to the set of bid prices corresponding to different power generation capacities (output levels) of the power plant. Typically, the power plant needs to divide its rated power generation capacity from 0% to 100% into several discrete points for bidding. The industry common practice is to divide it into 11 output level points (i.e., 0%, 10%, 20%, …, 100%), with each point corresponding to a bid price (yuan / MWh). This set can initially be empty, a default value, or loaded with historical strategies.

[0025] Step 102: In different functional areas of the same interactive display interface, the first curve formed by the load forecast data sequence, the second curve formed by the quotation data set, and the relevant actual transaction data are displayed in an integrated and linked manner. Furthermore, this step achieves integrated and linked display of key decision-making information by constructing a unified interactive display interface. The interface is divided into three core functional areas: The first area (usually occupying the left side of the main display area): This area can be used to display the first curve, namely the 96-point load forecast curve. This curve uses time as the horizontal axis (0-96 points) and predicted power (MW) as the vertical axis, intuitively reflecting the expected output profile of the power plant for the next day. This area allows users to zoom and drag to view details and supports selecting specific time periods for focused analysis. The second area (usually occupying the upper right of the interface): This area can display quotation information in a "table + graph" dual-view format. For example, the table view clearly lists 11 output level points and their corresponding bid prices, providing a precise numerical input and modification interface. The graph view (second curve): This plots the quotation curve in real time with output level (%) as the horizontal axis and bid price as the vertical axis, intuitively showing the overall shape and monotonicity of the quotation. The third area (usually located at the bottom of the interface): This area can be used for comparison displays. It can display current pricing schemes, historical successful pricing cases, and actual clearing results data (including cleared electricity volume and price) obtained from the trading platform side by side. Visual comparisons (such as overlaid curves and difference bar charts) help traders evaluate the strengths and weaknesses of their strategies. This layout allows load forecasting, pricing strategies, and historical performance to be displayed centrally on the same screen, enabling decision-makers to make comprehensive judgments without switching views. For example, when observing the midday peak output in the first region, the rationality of pricing settings for the corresponding output range in the second region can be evaluated simultaneously, greatly improving the efficiency of information acquisition and analysis.

[0026] Step 103: Upon receiving new bid data input, perform real-time compliance verification on the bid data based on preset electricity market trading rules; Furthermore, this step is the core of real-time risk control, and its logic strictly follows the following... Figure 2 The compliance verification workflow is shown below. The specific workflow is as follows:

[0027] Triggering condition: "Received new quotation data input" includes two main methods (as shown in the "Operation Input" branch in the figure): Method 1: Manual input. The user directly modifies or enters the price of a specific output point in the quotation table in the second area. Method 2: Import file. The user imports a preset quotation file (such as an Excel spreadsheet).

[0028] Data preprocessing: The system automatically extracts core fields, including power output level points, corresponding declared electricity prices, and related time stamp information, and converts them into standardized data objects that can be processed internally.

[0029] Parallel rule validation: The system calls the built-in rule engine to perform multi-dimensional, real-time compliance checks on the input data. The main rules include: Rule 1: Compliance with Price Limits. The system determines whether each declared electricity price falls within the latest maximum and minimum price limits published by the electricity regulatory agency. This is the fundamental red line to prevent price violations.

[0030] Rule Two: Curve Shape Compliance. The system determines whether the second curve formed by the entire set of bid data meets the requirement of being monotonically non-decreasing. That is, as the power output level increases, the corresponding bid price must remain flat or increase, and cannot decrease. This is a rule commonly used in the electricity market to prevent manipulation and ensure fairness.

[0031] Rule 3: Time Period / Data Matching Compliance. This rule can be extended to verify, for example, ensuring that the quoted data and load forecast data are aligned in time period, or verifying that the declared total electricity volume matches the power plant's capacity.

[0032] Verification result processing: Scenario 1: All verifications passed. The system displays a clear "Verification passed" visual or text prompt (such as a green checkmark) on the interface and allows the quotation data to be submitted or saved.

[0033] Scenario 2: Any rule validation fails. The system immediately performs the following actions, forming a closed-loop processing mechanism: a) Mark the violating field: In the quotation table of the second area, the violating cell is highlighted in red for maximum visibility. b) Generate a clear prompt: A pop-up prompt box appears on the interface, clearly indicating the specific rule violated (such as "exceeding the limit price" or "the curve is not monotonically decreasing"), and may provide adjustment suggestions. c) Prohibit submission: The process terminates, the system locks the submit button, and forces the user to correct the error before continuing. This process ensures that any violating quotation is intercepted instantly upon operation, fundamentally eliminating the risk of transaction compliance due to negligence.

[0034] Step 104: Synchronously store the load forecast data sequence, quotation data set and actual transaction data to the local machine, and provide offline historical data comparison analysis and strategy review functions based on the stored data; Furthermore, the synchronous storage in this step specifically refers to a background task that automatically encrypts and synchronously stores the predicted data obtained in step 101, the price quotes confirmed or verified by the user in steps 102 and 103, and the actual transaction results obtained through the communication module in the terminal's local non-volatile storage (such as a solid-state drive). The storage period is configurable; for example, the default is to store complete data for the past year. When the terminal is in a network outage state, the user can still query history: browse and search all historical data stored locally. Comparative analysis: invoke built-in analysis tools to compare the predicted, price quotes, and actual data of different dates horizontally, calculate the deviation, and generate analysis charts. Strategy review: based on the historical comparison results, review and summarize the gains and losses of transactions, providing data basis for subsequent strategy optimization.

[0035] Step 105: In response to the user's interactive operation on the content displayed in any functional area, update the display content of other related functional areas in a coordinated manner.

[0036] Furthermore, this step enables intelligent linkage updates of the content displayed in each functional area to respond to user interactions. Specifically, Example 1 (Zoom Linkage): When a user uses gestures to zoom in on the first area (load forecast curve) to view details of a specific time period (such as midday peak), the comparison curve displayed in the third area (historical comparison) can automatically zoom to the same time period, facilitating accurate comparisons within the same period. Example 2 (Data Modification Linkage): When a user modifies the price of a certain output point in the second area (triggers step 103) and passes verification, the price curve graph on the right (the second curve) will be redrawn in real time, intuitively reflecting the change in curve shape caused by price changes. Simultaneously, if the price affects the expected revenue for a specific time period, relevant prompts may also be updated in the third area. Example 3 (Focus Linkage): When the mouse hovers over or clicks on a time point on the first curve, the interface highlights the predicted output value corresponding to that time point, and roughly indicates the price range corresponding to that output level on the price curve in the second area, establishing a three-dimensional association between time, output, and price. The linkage mechanism in this embodiment creates a context-aware interactive experience, allowing the decision-maker's attention to flow naturally between relevant data, thus enhancing the integrity and coherence of the analysis.

[0037] As can be seen from the above technical solution, the beneficial effects of this embodiment are as follows: Through the collaborative implementation of steps 101 to 105, a complete decision-making closed loop is constructed, encompassing data integration, visualization, real-time risk control, and continuous optimization. The standardized data input provided in step 101 lays a reliable foundation for decision-making; the integrated interface in step 102 centrally displays load forecasting, pricing strategies, and historical performance, eliminating information gaps and improving decision analysis efficiency; the real-time compliance verification in step 103 instantly intercepts violations such as exceeding price limits and non-monotonic pricing, reducing transaction risks caused by human error to zero; the local encrypted storage and offline analysis functions in step 104 ensure the continuity of decision-making and the traceability of historical data in extreme situations such as network interruptions, supporting data-driven strategy review and optimization; and the intelligent linkage mechanism in step 105 enhances the overall integrity and smoothness of data analysis through cross-view correlation updates. Ultimately, this embodiment transforms the traditional, decentralized, high-risk, passive operation mode into a centralized, secure, and proactive decision support system.

[0038] Figure 1 The embodiments shown are merely basic examples of the method of the present invention. Other preferred embodiments of the method can be obtained by making certain optimizations and extensions based on them.

[0039] like Figure 3 The image shows another specific embodiment of a method for displaying a new energy power plant trading strategy according to the present invention. This embodiment further describes the method based on the foregoing embodiments, and includes the following steps:

[0040] Step 301: Integrate artificial intelligence prediction models to generate electricity price prediction information or load prediction correction information; Specifically, this step involves the integration and application of an artificial intelligence (AI) prediction model. This AI prediction model can be a machine learning model (such as a time series prediction model or a deep learning network) trained on multi-dimensional data, including historical transaction data, meteorological data, market supply and demand data, and holiday information. Its core function is to generate two types of key prediction information: Electricity price prediction information: predicting the clearing price curve of the electricity spot market for a future target period (such as the next day). This prediction is usually output in time series form, such as the 96-point electricity price prediction value. Load prediction correction information: dynamically correcting the basic load prediction data sequence obtained in step 101 based on more real-time or more refined data (such as ultra-short-term weather forecasts) to generate more accurate prediction values. The model can be deployed on a local terminal (utilizing the computing power of the core processing module) or call cloud prediction services through the communication module. The model executes predictions periodically or automatically based on triggering conditions (such as receiving new meteorological data) and pushes the results to the system's data processing flow.

[0041] Step 302: Intelligently associate the output information of the artificial intelligence prediction model with the load forecast data sequence and the price data set, and display them in an interactive display interface to assist in trading strategy decision-making.

[0042] Furthermore, this step achieves intelligent association and visual fusion of predictive information with existing decision data, which in this embodiment can specifically include two levels: Intelligent data layer association: The system logically correlates the AI-predicted outputs (such as electricity price forecast sequences) with the baseline load forecast sequence and the pricing data set. For example, it establishes a mapping relationship between "time point - predicted output - predicted electricity price," or calculates the expected return of the current pricing strategy under the predicted electricity price. Based on correlation analysis, the system can generate auxiliary decision-making signals, such as identifying the overlap between "peak power plant output forecast periods" and "peak market electricity price forecast periods," or marking output points where the current price is significantly lower than the predicted electricity price.

[0043] Presentation layer fusion display: In the interactive display interface, forecast information is integrated with the basic data in a non-intrusive, hierarchical manner. Example 1 (Electricity Price Forecast Integration): In the first area displaying the basic load forecast curve (first curve), an AI-generated electricity price forecast curve is overlaid using a semi-transparent layer, different colors, or dashed lines, with a shared or independent time vertical axis attached to the right. This allows traders to intuitively compare the expected trends of power plant output and market prices. Example 2 (Decision Support Tips): Next to the price quotation table in the second area or in the third area, the system can dynamically generate text or graphical tips based on the correlation analysis results. For example, a label "Current price is lower than the forecast average price by X yuan" may be displayed next to a price quotation at a certain output point, or a summary suggestion may be provided in the corner of the interface: "Based on the forecast, it is recommended to focus on the quotation strategy from period Y to period Z."

[0044] As can be seen from the above technical solution, the beneficial effects of this embodiment are as follows: By integrating an artificial intelligence prediction model, the system not only processes given data but also generates electricity price trend predictions or load forecast correction information. This prediction information is intelligently correlated and integrated with the basic load curve and pricing strategy, providing traders with market insights that go beyond their own experience and static data. This assists traders in adopting more proactive pricing strategies when market electricity price peaks coincide with power plant output peaks, or in proactively mitigating risks when electricity prices are predicted to decline. Therefore, based on compliance and safety, this further optimizes pricing strategies and proactively enhances the potential market returns and competitiveness of power plants.

[0045] This invention also provides a display system for trading strategies of new energy power plants. See also Figure 4 The image shows a specific embodiment of a display system for a new energy power plant trading strategy provided by the present invention. This embodiment of the system is used to execute... Figures 1-3 The physical apparatus of the method. Its technical solution is essentially the same as the embodiments described above, and the corresponding descriptions in the embodiments above also apply to this embodiment. The system includes:

[0046] The data interface module 401 is used to acquire load forecast data sequences, price quotation data sets, and actual transaction data. The integrated display and interaction module 402 is used to generate an interactive display interface to realize the linkage display and interaction of load forecast data sequences, price quotation data sets, and actual transaction data in different functional areas. The integrated display and interaction module 402 includes: a curve drawing unit 412 for generating and rendering a first curve and a second curve; a table generation unit 422 for generating a structured table of the price quotation data set; and an interaction response unit 432 for capturing user touch operations and triggering linkage updates. The compliance verification engine module 403 is communicatively connected to the integrated display and interaction module 402 and is used to perform real-time verification of the input price quotation data according to preset rules and provide feedback on the results. The compliance verification engine module 403 includes: a rule base 413 storing price limit rules and curve shape rules for electricity market transactions; a real-time verification unit 423 for calling the rule base 413 for verification when data is input; and an alarm unit 433 for generating visual or audible alarm signals when verification fails. The local storage and computing module 404 is used to encrypt and store all business data, and supports querying, comparison, and retrospective analysis based on historical data in an offline environment. The system also includes: a prediction service module 405, which has a built-in trained AI prediction model to provide prediction data; and a communication module 406, which supports wired and wireless network connections for data synchronization between the system and external power trading platforms or data centers.

[0047] Specifically, the data interface module 401 serves as the system's data exchange hub with the outside world. It is responsible for actively acquiring or passively receiving three main categories of core business data from external sources (such as cloud-based AI prediction platforms, power trading center systems, and on-site SCADA systems) through standardized communication protocols (such as API calls and file parsing): load forecast data sequences for future target periods, price quotation data sets corresponding to preset output levels, and historical and actual transaction data. This module performs preliminary cleaning, formatting, and verification of the raw data, converting it into a unified data model processed internally by the system, providing data supply for all subsequent modules. The integrated display and interaction module 402 is responsible for information presentation and interaction logic. It transforms the processed data into an intuitive graphical interface. Specifically, the curve drawing unit 412 is dedicated to graphics rendering, dynamically generating the first curve (such as a 96-point load forecast curve) based on the load forecast data sequence, and drawing the second curve (price quotation curve) in real time based on the price quotation data set, ensuring accurate and smooth graphics and supporting interactive operations (such as zooming). The table generation unit 422 is responsible for rendering and displaying the price quotation data set in a structured table format (such as an 11-row, 2-column price quotation table), providing a clear data entry and viewing interface. Interactive Response Unit 432: Captures all user interaction commands (such as clicks, drags, and inputs) on the touchscreen in real time. Its core function is to trigger linked updates: when a user operates in a functional area (such as modifying a quote in a table), this unit not only updates the content of its own area but also immediately sends commands to other relevant units, driving the curve drawing unit 412 to redraw the quote curve or triggering compliance verification, achieving real-time response across views. The compliance verification engine module 403 focuses on real-time assurance of transaction compliance. Rule Base 413: A configurable database storing specific verification logic abstracted from electricity market rules, the core of which includes price limit rules (price upper and lower limits) and curve shape rules (monotonicity and non-decreasing property). Real-time Verification Unit 423: A rule executor. When the interactive response unit 432 captures a new quote input, it immediately wakes up this unit. This unit calls the corresponding rules in the rule base 413 to perform calculations and logical judgments on the input data. Alarm Unit 433: A feedback generator. Based on the results of the real-time verification unit 423, if verification fails, the unit immediately generates alarm information including visual signals (such as highlighting, pop-ups) and / or auditory signals (prompt sounds), and presents it to the user through the integrated display and interaction module 402, forming an instantaneous closed loop of "operation-verification-feedback". The local storage and computing module 404 uses encryption technology to persistently store all incoming business data (predictions, quotes, transaction results) and operation logs on local physical media. Its core function is to provide offline computing capabilities: in the event of a network outage, this module can independently support complex queries, comparative analyses (such as multi-period curve comparisons), and strategy reviews (such as profit simulation calculations) of local historical data, ensuring the continuity and depth of decision support.The forecasting service module 405 can encapsulate trained AI forecasting models (such as deep learning models) and independently generate electricity price forecasting information or load forecasting correction information based on real-time acquired multi-source data (weather, market feedback, etc.). Its output, as high-value auxiliary information, is provided to the integrated display and interaction module 402 for fusion display and provides decision-makers with forward-looking strategic references. The communication module 406 manages all external wired (Ethernet) and wireless (Wi-Fi, 4G / 5G) network connections. It coordinates data acquisition by the data interface module 401, cloud model updates by the forecasting service module 405, and the reporting of final compliant pricing data to external power trading platforms. It is the infrastructure for maintaining data updates, online functionality, and interoperability with other systems. The following example illustrates the collaboration process of each module in the system.

[0048] Example of system collaboration process: a) User modifies quote (interaction response unit 432 captures); b) Data is sent to compliance verification engine module 403 for real-time review; c) If a violation is found, alarm unit 433 generates a signal and alerts the user via integrated display and interaction module 402; if compliant, data is updated; d) The updated data is synchronized to local storage and computing module 404 for encrypted storage; e) Simultaneously, the curve and table units of integrated display and interaction module 402 are refreshed; f) Communication module 406 reports the confirmed quote to the trading platform at an appropriate time; g) Prediction service module 405 runs periodically, providing intelligent prediction layers for the interface.

[0049] It should be noted that the specific types, quantities, and combinations of the data interface module 401, integrated display and interaction module 402, compliance verification engine module 403, local storage and computing module 404, prediction service module 405, and communication module 406 can be adjusted according to the actual needs of the application scenario, and this application embodiment does not impose any restrictions on this.

[0050] As can be seen from the above technical solutions, the beneficial effects of this embodiment are as follows: the system integrates and displays key data through an integrated interface, improving decision-making efficiency; with the help of a real-time compliance verification engine, the risk of bidding violations is reduced to almost zero; local offline storage and computing functions ensure the continuity of decision-making under extreme conditions; and the integration of an AI prediction module provides forward-looking market insights, assisting strategies to shift from passive compliance to proactive profit optimization. Ultimately, the system achieves a systematic leap in the convenience, security, reliability, and intelligence of new energy power plant transaction decisions.

[0051] Figure 5This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may also include non-volatile memory, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for other services.

[0052] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. Buses can be categorized as address buses, data buses, and other types. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0053] Memory is used to store instructions for execution. Specifically, instructions for execution are computer programs that can be executed. Memory can include main memory and non-volatile memory, and it provides the processor with execution instructions and data.

[0054] In one possible implementation, the processor reads the corresponding execution instructions from non-volatile memory into main memory and then executes them. Alternatively, it can obtain the corresponding execution instructions from other devices to form a display device for a new energy power plant trading strategy at the logical level. The processor executes the execution instructions stored in the memory to implement the display method for a new energy power plant trading strategy provided in any embodiment of the present invention.

[0055] The above is as described in the present invention. Figure 4The method for executing a display system for a new energy power plant trading strategy provided in the illustrated embodiment can be applied to a processor, or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0056] The steps of the method disclosed in the embodiments of this invention can be directly manifested as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0057] This invention also proposes a readable medium storing execution instructions. When these instructions are executed by a processor of an electronic device, the device can perform a display method for a new energy power plant trading strategy provided in any embodiment of this invention, specifically for executing, for example... Figure 1 , Figure 3 The method shown.

[0058] The electronic devices in the foregoing embodiments may be computers.

[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or computer program products. Therefore, the present invention can be implemented in a completely hardware embodiment, a completely software embodiment, or a combination of software and hardware.

[0060] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0061] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0062] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for displaying a trading strategy for a new energy power plant, characterized in that, The method includes: Obtain the load forecast data sequence of new energy power plants in the future target period, as well as the set of quotation data corresponding to multiple preset output levels; In different functional areas of the same interactive display interface, the first curve formed by the load forecast data sequence, the second curve formed by the quotation data set, and the relevant actual transaction data are displayed in an integrated and linked manner. Upon receiving new bid data input, the bid data is verified for compliance in real time based on preset electricity market trading rules; The load forecast data sequence, the quotation data set, and the actual transaction data are synchronously stored locally, and the offline historical data comparison analysis and strategy review function is provided based on the stored data. In response to user interaction with the content displayed in any functional area, the content displayed in other related functional areas will be updated accordingly.

2. The method according to claim 1, characterized in that, The load forecast data sequence consists of forecast data corresponding to 96 consecutive time points within a day; the multiple preset output levels include 11 level points equally divided from 0% to 100% of rated capacity; The integrated and interconnected display includes: The first area displays the load forecast curves for 96 points, the second area displays the price tables and corresponding price curves for 11 output points side by side, and the third area displays the comparison information of real-time and historical transaction results.

3. The method according to claim 1, characterized in that, The real-time compliance verification includes at least the following: Determine whether the quoted price data exceeds the price limit range issued by the regulatory authority; Determine whether the second curve satisfies the monotonicity requirement of increasing rather than decreasing with increasing output level; When the verification fails, the illegal data is highlighted and a correction prompt is generated on the interactive display interface.

4. The method according to claim 1, characterized in that, Also includes: Integrate artificial intelligence prediction models to generate electricity price forecasts or load forecast corrections; The output information of the artificial intelligence prediction model is intelligently correlated with the load prediction data sequence and the quotation data set, and then integrated and displayed in the interactive display interface to assist in trading strategy decision-making.

5. A display system for a new energy power plant trading strategy, characterized in that, The system for implementing the display method as described in any one of claims 1-4 includes: The data interface module is used to acquire load forecast data sequences, price quote data sets, and actual transaction data; An integrated display and interaction module is used to generate the interactive display interface, enabling the linked display and interaction of the load forecast data sequence, the quotation data set, and the actual transaction data in different functional areas; The compliance verification engine module is connected in communication with the integrated display and interaction module, and is used to verify the input quotation data in real time according to preset rules and provide feedback on the results. The local storage and computing module is used to encrypt and store all business data, and supports querying, comparing and reviewing based on historical data in an offline environment.

6. The system according to claim 5, characterized in that, The integrated display and interaction module includes: The curve drawing unit is used to generate and render the first curve and the second curve; A table generation unit is used to generate a structured table of the quotation data set; The interactive response unit is used to capture user touch operations and trigger related updates.

7. The system according to claim 5, characterized in that, The compliance verification engine module includes: The rule base stores the price limit rules and curve pattern rules for electricity market transactions; A real-time verification unit is used to call the rule base for verification when data is input; An alarm unit is used to generate visual or audible alarm signals when verification fails.

8. The system according to claim 5, characterized in that, The system also includes: The prediction service module has a built-in trained AI prediction model to provide prediction data; The communication module supports wired and wireless network connections and is used for data synchronization between the system and external power trading platforms or data centers.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method of any one of claims 1 to 4.

10. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 4.

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