New energy power generation equipment online operation analysis method and platform based on data fusion

Abstract

The invention belongs to the technical field of online operation analysis, and discloses a new energy power generation equipment online operation analysis method and platform based on data fusion, and the method cleans and corrects data, reflects real-time business activities through employing fixed cost and variable cost, collects wind field projects, and obtains a new energy power generation equipment online operation analysis result. The design parameters are compared with the parameters after actual construction and operation, and the actual operation efficiency of the wind field project is evaluated; the platform comprises a data management module, a real-time profit viewing module and a historical profit viewing module. According to the method, the hysteresis of financial statement data is made up, production and operation management is converted into whole-process management and control of beforehand planning, in-process monitoring and after-event analysis from after-event summarization, marginal contribution is improved, and the purpose of'controlling month with day 'is achieved.

Description

Technical Field

[0001] The present application belongs to the technical field of online business analysis, and in particular relates to an online business analysis method and platform for new energy power generation equipment based on data fusion. Background Art

[0002] To optimize new assets from the perspective of the full life cycle management of new energy power plants, it is necessary to evaluate the comprehensive benefits and asset performance of the new energy power plant and each independent power generation unit, identify problems, and achieve improved operational efficiency. However, the existing economic activity analysis of new energy power plants has the following problems:

[0003] 1) The analysis is conducted on a project-by-project, monthly basis (due to the lag in financial reporting). The results cannot guide the operation and maintenance of each independent power generation unit, nor can they link the operation and maintenance activities of wind turbines and photovoltaic equipment with economic benefits.

[0004] 2) There are a lot of uncertainties in the operation of new energy power stations. The existing economic activity analysis cannot reflect the composition of the operating costs of a single wind turbine or a single photovoltaic array, the characteristics of the operating costs, and the impact of operating cost uncertainty factors on operating income. It is impossible to conduct sensitivity analysis or benchmarking analysis. It is also impossible to provide operating data support for technical transformation activities to improve the quality and efficiency of wind turbines / photovoltaic equipment, nor can it provide data support for the optimization of new assets. Summary of the Invention

[0005] The purpose of this application is to provide an online business analysis method and platform for new energy power generation equipment based on data fusion, which solves the problem of real-time economic evaluation of wind turbine operation, provides a systematic and complete theoretical basis for comprehensively understanding the situation of new energy operation costs, and then analyzes the sensitivity of new energy operation income to uncertain cost changes, improves the management level of new energy operation income, and also provides support for improving the quality and efficiency of power stations.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] In a first aspect, the present application provides an online business analysis method for new energy power generation equipment based on data fusion, comprising:

[0008] Clean the data through the unified data cleaning model built to achieve comprehensive data processing;

[0009] By constructing a real-time revenue model for a single wind turbine and correcting the data, the traditional economic activity analysis of wind farms, which is conducted on a monthly or weekly basis, can be changed to a real-time economic evaluation of wind turbine operations.

[0010] By building a real-time operating cost model for a single wind turbine, we use fixed costs + variable costs to reflect real-time business activities, integrate business and financial data, and connect information links. This allows us to transform monthly economic activity analysis into real-time production and operation data, tracking and analyzing the daily production and operation status of new energy power stations.

[0011] The established single wind turbine operational performance evaluation index system provides data support for wind farm spot trading;

[0012] By establishing a single wind turbine project evaluation index system, we collected wind farm projects, compared the design parameters with the actual construction and operation parameters, and evaluated the actual operation efficiency of the wind farm projects.

[0013] In some embodiments, the steps of constructing a unified data cleaning model include:

[0014] Unified timeline: The timelines of unit operation data, fault data, and wind / solar power forecasts are unified;

[0015] Physical rule elimination: pre-process all data based on physical rules;

[0016] Data interpolation: missing data are interpolated according to the difference method;

[0017] Data screening and classification: Screen and classify data;

[0018] Detect and eliminate data anomalies: Use statistical methods to detect numerical attributes and calculate the mean and standard deviation of field values;

[0019] Detect and eliminate duplicate records: Clean duplicate records, determine whether two records are nearly duplicates, and eliminate duplicate data based on the time priority principle;

[0020] Utilize the relationship between the fan model and fan parameters to identify unreasonable data.

[0021] In some embodiments, data is filtered and classified according to normal operation, power restriction, fault shutdown, and planned shutdown of the unit.

[0022] In some embodiments, isolated data, erroneous data, and missing data are cleaned by constructing a numerical model of a wind farm.

[0023] In some embodiments, the relationship between various operation data of the wind turbine and the relationship between the operation data of the wind turbine and the state of the wind turbine are used to perform state estimation on redundant data and filter out erroneous data.

[0024] In some embodiments, correcting the wind turbine power data within a time interval corresponding to the wind farm daily grid power data includes:

[0025] The power of the fan after correction = the power before correction * correction coefficient;

[0026] Correction coefficient = daily grid-connected electricity of the wind farm / total electricity of all wind turbines in the wind farm before correction;

[0027] The electricity used to calculate the power generation income in real time is the integrated value of the electricity within the calculation time interval, and it needs to be recalculated and corrected with the on-grid electricity consumption of the gateway meter at 24 hours a day;

[0028] The on-grid electricity price is the average electricity price of the monthly budget, and when the budget is adjusted within the month, it will be recalculated and corrected.

[0029] In some embodiments, variable cost calculation includes: allocating fixed costs by day, hour, and minute based on the time and spare parts information in the operation and maintenance work order to obtain real-time cost details for each wind turbine.

[0030] In some embodiments, the fixed cost allocation calculation includes allocating the fixed cost by day, hour, and minute based on the monthly budget.

[0031] In some embodiments, an operational performance evaluation index system for a single wind turbine is constructed based on a real-time revenue model of a single wind turbine and a real-time operating cost model of a single wind turbine, including a real-time profit index, profitability index, operational capability index, and development capability index of a single wind turbine.

[0032] In a second aspect, the present application provides an online business analysis platform for new energy power generation equipment based on data fusion, comprising:

[0033] Data management module, used for wind farm management, wind turbine management, cost management, operating budget management, comprehensive electricity price management, asset data management, and project design parameter management;

[0034] Real-time profit viewing module, used to provide real-time profit trend analysis chart;

[0035] The historical profit viewing module is used to provide operation analysis and evaluation functions, including operation overview, operation indicator analysis, profit analysis, revenue analysis, cost analysis, cost details viewing, operation performance evaluation, and project evaluation.

[0036] Compared with the existing technology, the online business analysis method and platform for new energy power generation equipment based on data fusion provided by this application has the following beneficial effects:

[0037] This application is applied to the operation of each independent power generation unit in a new energy power station, making up for the lag of financial statement data, transforming production and operation management from post-summary to full-process control of pre-planning, in-process monitoring, and post-analysis, thereby improving marginal contribution and achieving the goal of "controlling the month with the day".

[0038] Compared with existing methods, this application uses fixed costs + variable costs to reflect real-time business activities. By strengthening the association with financial data, it promotes the integration and linkage of business and finance, connects the information chain, and transforms the previous monthly granularity economic activity analysis into real-time calculation of production and operation data such as electricity cost, profit, marginal contribution, etc., to track and analyze the daily production and operation of new energy power stations; at the same time, this application accurately records the labor and spare parts costs of each operation and maintenance activity by associating with the actual operation and maintenance work order activities, integrates financial cost and construction cost allocation data, and refines the granularity of business activity analysis to each power generation unit and each power generation unit's subsystem, providing a basis for optimizing resource allocation decisions for site control, power marketing, and business quantitative analysis.

[0039] This application makes up for the lag in financial statement data, transforms production and operation management from post-summary to full-process control of pre-planning, in-process monitoring, and post-analysis, improves marginal contribution, and achieves the goal of "controlling the month by the day". With the help of a digital platform, fixed costs + variable costs are used to reflect real-time business activities. By strengthening the connection with financial data, the integration and linkage of business and finance are promoted, the information chain is connected, and production and operation data such as cost per kilowatt-hour, profit, marginal contribution, etc. are calculated in real time. The daily production and operation status of new energy power stations is tracked and analyzed, providing a basis for optimizing resource allocation decisions for site control, power marketing, and quantitative operation analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solution of this application, the following is a brief introduction to the drawings required for the technical description.

[0041] Figure 1 A flowchart of the online business analysis method for new energy power generation equipment based on data fusion provided in this application;

[0042] Figure 2 Data graphs provided for this application;

[0043] Figure 3 Schematic diagram of the structure of the online business analysis platform for new energy power generation equipment based on data fusion provided in this application. DETAILED DESCRIPTION

[0044] The following is further explained in detail through specific implementation methods.

[0045] like Figure 1 and Figure 2 As shown, the present application provides an online business analysis method for new energy power generation equipment based on data fusion, comprising:

[0046] Clean the data through the unified data cleaning model built to achieve comprehensive data processing;

[0047] By building a real-time revenue model for a single wind turbine, data can be corrected, changing the traditional monthly or weekly economic activity analysis model of wind farms to achieve real-time economic evaluation of wind turbine operations.

[0048] By building a real-time operating cost model for a single wind turbine, we use fixed costs + variable costs to reflect real-time business activities, integrate business and financial data, and connect information links. This allows us to transform monthly economic activity analysis into real-time production and operation data, tracking and analyzing the daily production and operation status of new energy power stations.

[0049] The established single wind turbine operational performance evaluation index system provides data support for wind farm spot trading;

[0050] By establishing a single wind turbine project evaluation index system, we collected wind farm projects, compared the design parameters with the actual construction and operation parameters, and evaluated the actual operation efficiency of the wind farm projects.

[0051] (1) Establish a unified data cleaning model

[0052] According to the following method, a data cleaning model is established for equipment ledger data, operation data, financial data, maintenance records, and other data sources. The data cleaning model covers multiple data sources such as equipment ledger data, operation data, financial data, maintenance records, etc. Through a series of cleaning steps, comprehensive data processing is achieved. The specific steps include:

[0053] 1) Timeline Unification: Unify the timelines of unit operating data, fault data, and wind / solar power forecasts. During the data cleaning process, the timeline must first be unified to ensure consistency between unit operating data, fault data, and wind / solar power forecasts. This facilitates subsequent data analysis and decision-making, and avoids misunderstandings or errors caused by time inconsistencies.

[0054] 2) Physical rule elimination: Based on physical rules, all data are preprocessed to eliminate data that does not conform to physical laws, so as to reduce noise and interference in the data and improve the accuracy and reliability of the data.

[0055] 3) Data interpolation: Missing data is interpolated using the differencing method. The differencing method can infer missing data points based on existing data points, thereby maintaining the continuity and integrity of the data.

[0056] 4) Data screening and classification: Data is screened and classified according to normal unit operation, power restrictions, fault shutdown, planned shutdown, etc., to better understand the operating status and performance of the equipment and provide strong support for subsequent business decisions.

[0057] 5) Detect and eliminate data anomalies: Statistical methods are used to detect numeric attributes, calculate the mean and standard deviation of field values, and consider the confidence interval of each field to identify anomalous fields and records. Data mining methods are introduced into data cleaning, such as clustering methods for detecting anomalous records, model methods for identifying anomalous records that do not conform to existing patterns, and association rule methods for identifying anomalous data in the dataset that does not conform to rules with high confidence and support.

[0058] 6) Detect and eliminate duplicate records: Clean duplicate records, determine whether two records are nearly duplicates, and remove duplicate data according to the time priority principle to reduce data redundancy and improve data processing efficiency and quality.

[0059] 7) Identify irrational data using the relationship between wind turbine models and parameters: A numerical model of the wind farm is used to clean isolated, erroneous, and missing data. By leveraging the relationships between wind turbine operating data and between wind turbine operating data and wind turbine status, redundant data is estimated and erroneous data is filtered out, further improving data accuracy and reliability and providing more accurate information support for operational decisions regarding renewable energy power generation equipment.

[0060] (2) Establish a real-time revenue model for a single wind turbine

[0061] Revenue is equal to the product of grid-connected electricity volume and grid-connected electricity price.

[0062] The cumulative power generation data used to calculate wind turbine power is affected by many factors and has poor fault tolerance. After obtaining the wind farm's daily grid-connected power data, this data should be corrected. The specific correction method for the wind turbine power data within the time interval corresponding to the wind farm's daily grid-connected power data is as follows:

[0063] The power of the fan after correction = the power before correction * correction coefficient;

[0064] Correction coefficient = daily grid-connected electricity of the wind farm / total electricity of all wind turbines in the wind farm before correction.

[0065] The electricity quantity used to calculate the power generation income in real time is the integrated value of the electricity quantity within the calculation time interval, and it needs to be recalculated and corrected using the electricity quantity collected by the gateway meter at 24 hours every day.

[0066] The on-grid electricity price is the average electricity price of the monthly budget, and when the budget is adjusted within the month, it will be recalculated and corrected.

[0067] For areas where spot trading has been carried out, medium- and long-term positions, day-ahead cleared electricity volume and electricity prices, and real-time cleared electricity volume and electricity prices will be used for future recalculation and correction.

[0068] (3) Establish a real-time operating cost model for a single wind turbine

[0069] Variable cost calculation method: Based on the time and spare parts information in the operation and maintenance work order, the fixed cost is amortized by day, hour and minute to obtain real-time cost details for each wind turbine.

[0070] Fixed cost allocation calculation method: Based on the monthly budget, fixed costs are allocated by day, hour, and minute. Then, within the real-time full cost calculation interval, fixed costs are allocated per kilowatt-hour based on the amount of electricity accumulated. The fixed cost per kilowatt-hour is calculated as the quotient of the fixed cost and the amount of electricity accumulated. At 24:00 on the last day of the month, the calculated figure for the current month is revised based on the actual fixed cost data from the financial system.

[0071] This application uses fixed cost + variable cost to reflect real-time business activities, integrates business and financial data, and connects information links. It transforms the previous monthly economic activity analysis into real-time calculation of production and operation data such as electricity cost, profit, marginal contribution, etc., and tracks and analyzes the daily production and operation status of new energy power stations.

[0072] (4) Establish a single fan operation performance evaluation index system

[0073] Based on the revenue model and cost model, the following operational performance evaluation index system is constructed, and calculations are performed at the beginning and end of the period based on different time scales such as minutes, hours, days, months, and years. The details are as follows:

[0074] 1) Real-time profit indicator of a single wind turbine: Calculate the current profit of a single wind turbine in real time.

[0075] Real-time profit = real-time income - real-time cost;

[0076] Real-time income = real-time power generation × real-time electricity price;

[0077] Real-time cost = real-time operation and maintenance cost + real-time depreciation cost + real-time fuel cost.

[0078] The time scale is, for example, minutes or hours.

[0079] 2) Profitability indicators: including the net present value of a single wind turbine, dynamic investment payback period, benefit-cost ratio, and return on total assets.

[0080] Net present value (NPV) is the sum of the net cash flows of each year during the project life, discounted to the present value at the time of project implementation, taking into account the time value of money. NPV = Σ[(CI - CO)t / (1 + r)^t], where CI is the cash inflow, CO is the cash outflow, r is the discount rate, and t is the time.

[0081] The dynamic payback period is the time required to recover the full investment, taking into account the time value of money. It is determined by accumulating net cash flows until the accumulated net cash flows reach or exceed the initial investment.

[0082] The benefit-cost ratio is the ratio of the total present value of benefits to the total present value of costs over the entire life of the project. BCR = ΣBIt / ΣCIt, where BI is the present value of benefits and CI is the present value of costs.

[0083] Return on total assets is the ratio of net profit to average total assets.

[0084] ROA = net profit / average total assets × 100%;

[0085] Average total assets = (total assets at the beginning of the period + total assets at the end of the period) / 2.

[0086] 3) Operational capability indicators: mainly asset turnover rate.

[0087] Asset turnover rate is the ratio of a company's net sales revenue to its average total assets over a certain period of time.

[0088] Asset turnover = net sales revenue / average total assets. For wind turbines, this can be adjusted to the ratio of power generation or revenue to average assets (such as the original value of the wind turbine).

[0089] 4) Development capability indicators: mainly include net profit growth rate and sales growth rate.

[0090] The net profit growth rate is: the ratio of the increase in net profit in the current period to the net profit in the previous period.

[0091] Net profit growth rate = (net profit of this period - net profit of previous period) / net profit of previous period × 100%.

[0092] The sales increase rate is: the ratio of the increase in sales revenue (or power generation) in the current period to the sales revenue (or power generation) in the previous period.

[0093] Sales growth rate = (current period sales revenue - previous period sales revenue) / previous period sales revenue × 100%;

[0094] Power generation growth rate = (current period power generation - previous period power generation) / previous period power generation × 100%.

[0095] (5) Establish a project evaluation index system for single fans

[0096] Collect design parameters for wind farm projects at various stages, including feasibility studies, investment committees, preliminary designs, management budgets, and final accounts. Compare these design parameters with actual construction and operational parameters to evaluate the actual operational performance of the wind farm project.

[0097] Project evaluation indicators include: static total investment, dynamic total investment, static investment per kilowatt, dynamic investment per kilowatt, annual on-grid power, annual utilization hours, electricity price excluding tax, operating income, total cost and expenses, and total investment rate of return.

[0098] (6) Platform construction

[0099] like Figure 3 As shown, this application provides an online business analysis platform for new energy power generation equipment based on data fusion based on the above-mentioned online business analysis method for new energy power generation equipment based on data fusion, including a data management module, a real-time profit viewing module and a historical profit viewing module.

[0100] The data management module is used for wind farm management, wind turbine management, cost management, operating budget management, comprehensive electricity price management, asset data management, and project design parameter management. It collects and organizes various data such as wind farms, wind turbines, costs, operating budgets, comprehensive electricity prices, asset data, and project design parameters.

[0101] Wind farm management is mainly responsible for recording and managing the basic information of the wind farm. Wind turbine management is mainly responsible for recording in detail the key information of each wind turbine, such as the model, manufacturer, installation location, and operating status. Cost management is mainly responsible for tracking and recording the various costs of wind power projects, including equipment purchase costs, installation costs, operation and maintenance costs, etc. Operating budget management is mainly responsible for formulating and managing the operating budget of wind power projects based on historical data and future forecasts. Comprehensive electricity price management is mainly responsible for recording and analyzing the electricity prices of wind power projects. Asset data management is mainly responsible for digital management of all assets of wind power projects, including equipment depreciation, maintenance records, etc. Project design parameter management is mainly responsible for storing and managing the design parameters of the project in the feasibility study, investment committee, preliminary design, management budget and other stages to facilitate comparison and analysis with actual operating data.

[0102] Real-time profit viewing module: Displays real-time profit, cost, and revenue for each wind turbine on a GIS map, and counts the number of profitable and loss-making turbines. The real-time profit viewing module provides real-time profit trend analysis charts.

[0103] Through the real-time profit viewing module, the location of each wind turbine is marked on the map, and its profit, cost and revenue data are displayed in real time.

[0104] The real-time profit viewing module counts the number of wind turbines currently making profits and making losses based on real-time data, helping managers quickly understand the overall operation of the wind farm.

[0105] Through the real-time profit viewing module, you can obtain real-time profit trend analysis charts to help managers understand the changing trends of profits and make timely adjustment decisions.

[0106] The historical profit review module includes: operational overview, operational indicator analysis, profit analysis, revenue analysis, cost analysis, cost detail review, operational performance evaluation, and project evaluation. The historical profit review module provides operational analysis and evaluation capabilities, helping managers gain a deeper understanding of operational performance.

[0107] The Operation Overview provides an overview of the wind farm's overall operations, including key indicators such as power generation, revenue, and costs. Operational Indicator Analysis provides in-depth analysis of operational indicators such as power generation, equipment utilization, and O&M costs to identify potential problems and areas for improvement. Profit Analysis calculates and analyzes the wind farm's profitability. Revenue Analysis provides a detailed analysis of the wind farm's revenue sources and composition, helping managers understand the impact of different electricity pricing policies and time periods on revenue. Cost Analysis provides a detailed analysis of each wind farm's costs to identify key areas and potential for cost control. Cost Details View provides detailed data and records for each cost, helping managers gain a deeper understanding of cost structure and changing trends. Operational Performance Evaluation evaluates and scores the wind farm's operational performance based on operational data, helping managers understand the wind farm's operational level. Project Evaluation evaluates and analyzes the overall performance of wind power projects.

[0108] For new energy power stations, this application establishes a unified model and standardized expression of equipment ledger data and operation data, financial data, maintenance records and other data to achieve data integration and break through information silos; for the important influencing factors of new energy operation income (mainly the uncertainty of new energy costs and wind / light power forecasts), the cost structure of new energy operation is analyzed from three aspects: investment and construction, operation and maintenance, and finance, using fixed cost + variable cost, and through the sensitivity analysis results of new energy operation income to the main uncertainty factors, a framework for new energy uncertainty income management methods is established to achieve benchmarking analysis of new energy operation costs, income and profits in different dimensions.

[0109] This application establishes a model for operating revenue management and asset performance benchmarking for new energy power plants. Benchmarking analysis will be conducted on the costs, revenue, and profits of each independent power generation unit, from feasibility studies, investment committee decisions, initial design, final settlement, and actual operation, as well as asset performance assessment and forecasting. By studying the sensitivity of new energy power plant revenue to key uncertain cost variables and wind / solar power forecasts, an assessment and benchmarking management of the impact of performance, failures, operation and maintenance, and technical upgrades on wind turbine / photovoltaic asset performance will be conducted.

[0110] This application accurately records the labor and spare parts costs of each operation and maintenance activity by associating it with the work order activities of actual operation and maintenance, integrating the financial cost and construction cost allocation data, and refining the granularity of business activity analysis to each power generation unit and its subsystems.

[0111] This application analyzes the sensitivity of new energy operating income to major uncertainties, establishes a framework for new energy uncertainty income management methods, and realizes benchmarking analysis of new energy operating costs, income, and profits in different dimensions.

[0112] Integrating the above analysis results, we conduct a benchmark analysis of the costs, benefits and profits of each independent power generation unit from the feasibility study, investment committee decision, preliminary design, completion settlement, to actual operation, and conduct an assessment and forecast of asset performance.

[0113] The above description is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed in this application should be covered by the scope of protection of the present application.

Claims

1. A new energy power generation equipment-level online business analysis method based on data fusion, characterized in that: include: Clean the data through the unified data cleaning model built to achieve comprehensive data processing; The data is corrected by constructing a real-time revenue model for a single wind turbine; By building a real-time operating cost model for a single wind turbine, using fixed and variable costs to reflect real-time business activities, integrating business and financial data, and connecting information links, we can transform monthly economic activity analysis into real-time production and operation data, and track and analyze the daily production and operation status of new energy power stations. The established single wind turbine operational performance evaluation index system provides data support for wind farm spot trading; By establishing a single wind turbine project evaluation index system, we collected wind farm projects, compared the design parameters with the actual construction and operation parameters, and evaluated the actual operation efficiency of the wind farm projects.

2. The online business analysis method for new energy power generation equipment based on data fusion according to claim 1 is characterized in that: The steps to build a unified data cleaning model include: Unified timeline: The timelines of unit operation data, fault data, and wind / solar power forecasts are unified; Physical rule elimination: pre-process all data based on physical rules; Data interpolation: missing data are interpolated according to the difference method; Data screening and classification: Screen and classify data; Detect and eliminate data anomalies: Use statistical methods to detect numerical attributes and calculate the mean and standard deviation of field values; Detect and eliminate duplicate records: Clean duplicate records, determine whether two records are nearly duplicates, and eliminate duplicate data based on the time priority principle; Utilize the relationship between the fan model and fan parameters to identify unreasonable data.

3. The online business analysis method for new energy power generation equipment based on data fusion according to claim 2 is characterized in that: The data is filtered and classified according to normal operation, power restriction, fault shutdown and planned shutdown of the unit.

4. The online business analysis method for new energy power generation equipment based on data fusion according to claim 2 is characterized in that: Isolated data, erroneous data and missing data are cleaned through the constructed wind farm numerical model.

5. The method for online business analysis of new energy power generation equipment based on data fusion according to claim 4 is characterized in that: By utilizing the relationship between the various operating data of the fan and the relationship between the fan operating data and the fan status, the redundant data is estimated and the erroneous data is filtered out.

6. The method for online business analysis of new energy power generation equipment based on data fusion according to claim 1 is characterized in that: Correct the wind turbine power data within the time interval corresponding to the wind farm daily grid power data, including: The power of the fan after correction = the power before correction * correction coefficient; Correction coefficient = daily grid-connected electricity of the wind farm / total electricity of all wind turbines in the wind farm before correction; The electricity used to calculate the power generation income in real time is the integrated value of the electricity within the calculation time interval, and it needs to be recalculated and corrected with the on-grid electricity consumption of the gateway meter at 24 hours a day; The on-grid electricity price is the average electricity price of the monthly budget, and when the budget is adjusted within the month, it will be recalculated and corrected.

7. The method for online business analysis of new energy power generation equipment based on data fusion according to claim 1 is characterized in that: Variable cost calculation includes: allocating fixed costs by day, hour, and minute based on the time and spare parts information in the operation and maintenance work order to obtain real-time cost details for each wind turbine.

8. The method for online business analysis of new energy power generation equipment based on data fusion according to claim 1 is characterized in that: Fixed cost allocation calculation includes: allocating fixed costs by day, hour, and minute based on the monthly budget.

9. The method for online business analysis of new energy power generation equipment based on data fusion according to claim 1 is characterized in that: Based on the real-time revenue model of a single wind turbine and the real-time operating cost model of a single wind turbine, an operational performance evaluation index system for a single wind turbine is constructed, including the real-time profit index, profitability index, operational capability index and development capability index of a single wind turbine.

10. A new energy power generation equipment-level online business analysis platform based on data fusion, characterized by: include: Data management module, used for wind farm management, wind turbine management, cost management, operating budget management, comprehensive electricity price management, asset data management, and project design parameter management; Real-time profit viewing module, used to provide real-time profit trend analysis chart; The historical profit viewing module is used to provide operation analysis and evaluation functions, including operation overview, operation indicator analysis, profit analysis, revenue analysis, cost analysis, cost details viewing, operation performance evaluation, and project evaluation.

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