Investment aided decision-making method and device for digital energy fusion system

Abstract

The invention discloses an investment aid decision-making method and device for a digital energy fusion system, and belongs to the field of data analysis and simulation. The method comprises the following steps: acquiring first basic parameters of wind and light energy storage equipment and a data center in an automatic investment power scene, and second basic parameters of the wind and light energy storage equipment and the data center in a non-investment power scene; inputting the first basic parameters into a preset fusion investment model, respectively inputting the second basic parameters into a preset single-type investment model, and sequentially outputting cost data corresponding to an automatic investment power scene and a non-investment power scene; according to the basic parameters and the cost data of the automatic investment power scene and the non-investment power scene, generating a financial report of the corresponding scene, and determining a financial index for representing the feasibility of the data fusion project; and carrying out risk analysis according to the financial report and the financial index, and determining an investment decision scheme meeting a preset requirement. According to the method, the accuracy, efficiency and risk response capability of investment decision making of the logarithmic energy fusion system can be improved.

Description

Technical Field

[0001] This invention relates to the field of data analysis and simulation technology, and in particular to an investment assistance decision-making method and device for a data-energy fusion system. Background Technology

[0002] "Data-energy integration" is a crucial pathway to promote the green and low-carbon development of energy and power, an important means to unlock the value of energy and power data, and a vital platform for driving innovative development in the energy and power sector. Its core lies in comprehensively embedding digital technologies such as the Internet of Things and big data into the entire chain of energy production, transmission, storage, consumption, and management. Based on this, by building independent analysis and evaluation capabilities for data-energy integration investment projects, it allows for greater control over the analysis and evaluation of such projects, providing planning and decision-making tools and addressing significant technical shortcomings in investment decision-making analysis for data-energy integration projects.

[0003] In related technologies, the traditional data table modeling and analysis methods used for investment decision-making have significant limitations due to their reliance on manual operation. When faced with complex investment scenarios such as the integration of data centers and new energy projects, traditional methods cannot quantify the impact of uncertainties such as electricity price fluctuations and policy changes on investment returns, nor can they provide dynamic scenario simulation and risk warning functions. This results in low decision-making accuracy, efficiency, and risk response capabilities when facing data-energy integration scenarios.

[0004] Therefore, there is an urgent need for an investment support decision-making method and device for data-energy integration systems to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention provides a method and apparatus for investment support decision-making in data-energy integration systems, which can improve the accuracy, efficiency, and risk response capabilities of investment decisions for such systems. The technical solution is as follows: On the one hand, an investment support decision-making method for a data-energy integration system is provided, the method comprising: Obtain the first basic parameters of wind, solar and energy storage equipment and data centers under the scenario of self-invested computing power, and the second basic parameters of wind, solar and energy storage equipment and data centers under the scenario of no computing power investment; The first basic parameter is input into a preset integrated investment model, and the second basic parameter is input into a preset single-type investment model respectively. The cost data corresponding to the self-invested computing power scenario and the non-invested computing power scenario are output in sequence. Based on the basic parameters and cost data of the self-invested computing power scenario and the non-invested computing power scenario, generate corresponding financial statements for the scenario, and determine the financial indicators used to characterize the feasibility of the data fusion project. Based on the financial statements and financial indicators, a risk analysis is conducted to determine an investment decision plan that meets the preset requirements.

[0006] On the other hand, an investment assistance decision-making device for a data-energy integration system is provided, the device comprising: The acquisition module is used to acquire the first basic parameters of wind, solar and energy storage equipment and data centers under the scenario of self-invested computing power, and the second basic parameters of wind, solar and energy storage equipment and data centers under the scenario of no computing power investment. The calculation module is used to input the first basic parameter into a preset integrated investment model and input the second basic parameter into a preset single-type investment model respectively, and output the cost data corresponding to the self-invested computing power scenario and the non-invested computing power scenario in sequence. The generation module is used to generate financial statements for corresponding scenarios based on the basic parameters and cost data of self-invested computing power scenarios and non-invested computing power scenarios, and to determine financial indicators used to characterize the feasibility of data fusion projects. The analysis module is used to perform risk analysis based on the financial statements and financial indicators to determine investment decision schemes that meet preset requirements.

[0007] On the other hand, a computer device is provided, the computer device including a memory and a processor, the memory for storing computer programs, and the processor for executing the computer programs stored in the memory to implement the steps of the investment assistance decision-making method for the data-energy fusion system described above.

[0008] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, it implements the steps of the investment assistance decision-making method for the data-energy fusion system described above.

[0009] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the investment assistance decision-making method for a data-energy fusion system described above.

[0010] The technical solution provided by this invention can bring at least the following beneficial effects: By performing economic calculations for scenarios with and without self-invested computing power, a closed-loop analysis of "generation-use-storage" is achieved, accurately quantifying the power supply structure and revenue costs of integrated projects, thus improving the relevance and accuracy of the analysis; furthermore, based on basic parameters and cost data, financial statements and key indicators are automatically generated, significantly reducing labor costs and improving work efficiency; finally, through risk analysis, investment decisions can intuitively reflect risk-return characteristics, supporting dynamic scenario simulation and strategy exploration. From data input to risk output, this method achieves a comprehensive improvement in model applicability, risk quantification, and user interaction, providing enterprises with a scientific and efficient auxiliary decision-making tool for investment. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of an investment assistance decision-making method for a data-energy fusion system provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of a financial statement in a self-computing computing power scenario provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of a financial statement in a scenario where no computing power is invested, provided by an embodiment of the present invention; Figure 4 This is a sensitivity analysis - single-factor result graph provided by an embodiment of the present invention; Figure 5 This is a sensitivity analysis-two-factor result graph provided by an embodiment of the present invention; Figure 6 This is a sensitivity analysis three-factor result graph provided by an embodiment of the present invention; Figure 7 This is a sensitivity analysis case diagram provided by an embodiment of the present invention; Figure 8 This is a schematic diagram of break-even analysis provided in an embodiment of the present invention; Figure 9 This is a break-even example diagram provided in an embodiment of the present invention; Figure 10 This is an example diagram of scenario analysis provided in an embodiment of the present invention; Figure 11 This is a schematic diagram of a Monte Carlo simulation provided in an embodiment of the present invention; Figure 12 This is an example diagram of Monte Carlo simulation analysis provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] As mentioned earlier, the traditional data table modeling and analysis methods used in investment decision-making have significant limitations due to their reliance on manual operation, making it difficult to achieve automated and intelligent analysis in complex scenarios.

[0015] Based on this, the present invention is conceived to realize investment assistance decision-making for data-energy integration projects by constructing a composite evaluation method for different types of project combinations.

[0016] The following describes the specific implementation of the above concept.

[0017] Please refer to Figure 1 This invention provides an investment assistance decision-making method for a data-energy fusion system, the method comprising: Step 100: Obtain the first basic parameters of wind, solar and energy storage equipment and data center under the self-invested computing power scenario, and the second basic parameters of wind, solar and energy storage equipment and data center under the non-invested computing power scenario; Step 102: Input the first basic parameter into the preset integrated investment model, and input the second basic parameter into the preset single-type investment model respectively, and output the cost data corresponding to the self-invested computing power scenario and the non-invested computing power scenario in sequence. Step 104: Generate financial statements for the corresponding scenarios based on the basic parameters and cost data of the self-invested computing power scenario and the non-invested computing power scenario, and determine the financial indicators used to characterize the feasibility of the data fusion project. Step 106: Conduct risk analysis based on the financial statements and financial indicators to determine an investment decision plan that meets the preset requirements.

[0018] In this embodiment of the invention, by performing economic calculations for scenarios involving self-invested computing power and those without, a closed-loop analysis of the "generation-use-storage" process is achieved. This accurately quantifies the power supply structure and revenue costs of integrated projects, improving the relevance and accuracy of the analysis. Furthermore, based on basic parameters and cost data, financial statements and key indicators are automatically generated, significantly reducing labor costs and improving work efficiency. Finally, risk analysis enables investment decisions to intuitively reflect risk-return characteristics, supporting dynamic scenario simulation and strategy exploration. From data input to risk output, this method achieves a comprehensive improvement in model applicability, risk quantification, and user interaction, providing enterprises with a scientific and efficient auxiliary decision-making tool for investment.

[0019] The following description Figure 1 The execution method of each step is shown.

[0020] First, for step 100, obtain the first basic parameters of wind, solar and energy storage equipment and data center under the self-invested computing power scenario, and the second basic parameters of wind, solar and energy storage equipment and data center under the non-invested computing power scenario.

[0021] In this embodiment of the invention, the first basic parameters include first basic information, first investment estimate, first basic cost and expense, first operating revenue, tax rate, first investment and financing related parameters, and depreciation parameters; Specifically, the core parameters of the first basic information category include those that need to be manually entered: project scale, project substation capacity, total rack power, whether there is renewable energy access (including the renewable energy type selected from the drop-down menu), renewable energy access power, renewable energy installed capacity ratio, grid access fee, whether the server is self-invested, selection of computing power equipment (number of servers such as training and push integrated servers), PUE, construction period, operation period, and project location (province, city / county). The key calculation item, annual electricity consumption, is automatically calculated according to the rule: "If annual electricity consumption is greater than the annual power generation of wind (solar) and energy storage, the grid-connected power of wind (solar) and energy storage is 0; if annual electricity consumption is less than the annual power generation of wind (solar) and energy storage, the grid-connected power = annual power generation of wind (solar) and energy storage - annual electricity consumption" (unit: MWh). All other parameters are set manually, with some parameters having value descriptions to help define the value logic, and the accuracy of the information is ensured by the consistency of units.

[0022] Among the core parameters of the first investment estimate, those requiring manual input include external power construction costs, civil engineering costs (including batteries), electromechanical costs, other preliminary costs, land acquisition costs, and the unit prices of various servers (integrated training and push computing power servers, super training servers, domestic computing power servers, etc.), management node servers, storage systems (SSD storage systems, mechanical hard disk storage systems), network systems, security systems, and operation platform system costs. Key automatically calculated items include static total investment (257.58 million yuan, excluding financing costs, full capital self-investment model), preliminary costs (4 million yuan), infrastructure costs (34 million yuan), equipment costs (219.58 million yuan, the total price of computing power equipment is calculated using the formula "number of integrated training and push servers × unit price of integrated training and push computing power servers + number of super training servers × unit price of super training servers + number of domestic computing power servers × unit price of domestic computing power servers"), construction period interest (8.24 million yuan), and dynamic total investment (268.12 million yuan). The remaining parameters are set by manual input, relying on a unified unit (ten thousand yuan) to ensure accurate calculation. Some parameters have value explanations (such as the capital model of static total investment).

[0023] Among the core parameters of the first basic cost category, those requiring manual input include electricity cost (including grid supply), broadband cost, other operating expenses (water and fuel costs), and maintenance cost. Key items for automatic calculation are total cost (43.19 million yuan) and power supply cost for the integrated project (32.55 million yuan, calculated as "valued from (wind, solar, and energy storage price + grid access fee (if applicable)) × wind, solar, and energy storage power generation"). Other parameters are set manually, relying on consistent units (in ten thousand yuan) to ensure calculation accuracy. Some parameters have explanations for their values ​​(e.g., maintenance cost "includes management costs and sales costs").

[0024] Among the core parameters for the first category of operating revenue, the following need to be manually entered: annual computing power subsidy revenue (including years 1-6) and annual other value-added revenue (including years 1-6). The key automatically calculated items are the total revenue from self-invested power leasing (625.4168 million yuan) and the revenue for each year (years 1-6). The revenue from integrated training and push server leasing, super training server leasing, and domestic computing power server leasing for each year are all automatically calculated using the formula "corresponding server utilization in the current year × corresponding server rental fee in the current year." The remaining parameters rely on automatic calculation or manual input settings, with the unit uniformly set to ten thousand yuan to ensure data logic and accuracy.

[0025] All core tax rate parameters are manually input items, covering VAT rate (6%, automatically generating prices including and excluding tax), surtax (12%), electricity tax rate (13%), corporate income tax (25%), urban maintenance and construction tax rate (7% for urban areas, 5% for county / township areas, and 1% for other areas, a menu option), and education surcharge (3%, with value descriptions including calculation methods for education surcharge and local education surcharge). All parameters rely on manual input settings, with units uniformly expressed as %, and some parameters include value descriptions to help define the tax rate rules.

[0026] Among the core parameters related to investment and financing, those requiring manual input include the capital ratio (including working capital), grace period, construction period interest rate, long-term loan interest rate, short-term loan interest rate, repayment period for the principal of long-term loans purchased for computing power, and working capital. The key automatically calculated item is capital (53.6245 million yuan, formula "Capital = Dynamic Total Investment × Capital Ratio"). Other parameters are set manually, relying on consistent units (e.g., %, year / month, ten thousand yuan) to ensure calculation accuracy. Some parameters have value instructions (e.g., the capital ratio is generally between 20% and 30%, the grace period is "interest only, no principal repayment during the construction period," etc.).

[0027] Among the core parameters related to depreciation, the replacement of electromechanical equipment and computing power equipment are manually entered for each year of the operating period (years 1-6) (mostly 0 for the first 5 years, and 1 million yuan for both electromechanical and computing power equipment replacement in year 6); the depreciation period and residual value rate related to maintaining operating investment (depreciation period and residual value rate of computing power and electromechanical equipment) are also manually entered; the VAT rate is selected from the drop-down menu (13%), and the VAT input tax is automatically calculated (230,100 yuan, the formula involves major repair and replacement investment and VAT rate); the depreciation period / residual value rate of computing power and electromechanical equipment, and the depreciation period / residual value rate of infrastructure are also manually entered. Parameters are set through manual input, drop-down selection, or automatic calculation, with units including 10,000 yuan, year, and percentage, ensuring the accuracy of depreciation and tax-related data logic.

[0028] Furthermore, the second basic parameters include: second basic information, production-related information, second investment estimates, second basic costs and expenses, second operating revenue, taxes, and second investment and financing-related parameters; Specifically, the core parameters of the second basic information section include the construction period, operation period, and construction schedule. For each project, the construction period (years / months) and operation period (years) must first be clearly defined; this is a prerequisite for ensuring the accuracy of subsequent model indicator calculations. Secondly, the construction schedule must cover all years of the construction period, the investment percentage for each year must be manually entered, and the sum of the percentages for each year must equal 1. All parameters in the basic information section must be set either manually or through drop-down menu selection.

[0029] Key production-related parameters include those requiring manual input: actual installed capacity, rated energy storage power, energy storage capacity, annual utilization hours in the first year, and the degradation rate, curtailment rate, on-grid electricity price for each year of operation, energy consumption through data integration, and energy integration mechanism price. The key calculation item, on-grid electricity, is automatically generated using the formula: "On-grid electricity for each year = annual utilization hours × actual installed capacity × annual degradation coefficient × (1 - curtailment rate)" (unit: MWh). All parameters are manually entered and set, and accuracy is ensured by maintaining consistent units.

[0030] The second investment estimate includes manually input items such as equipment purchase costs, construction and installation costs, other expenses, and energy storage investment (a VAT rate needs to be selected from a drop-down menu). The total amount including tax is automatically generated by the system (e.g., equipment purchase cost of 100,000,000 yuan, tax rate 13% / 9% / 6% / 0%). The core automatic calculation logic is as follows: the project's static total investment is derived from the sum of items I-7. The static total investment (self-invested) is generated through "Project Static Total Investment - Construction Period Interest," while working capital is calculated based on custom rules. All numerical units are uniformly in ten thousand yuan.

[0031] The second set of investment and financing related parameters includes: capital ratio, grace period, construction period / long-term / short-term loan interest rates (different interest rates need to be set separately), long-term loan principal repayment period, working capital to capital ratio, project company registered capital, etc. Short-term loans need to be manually entered for each year of the operating period. The core of the automated calculation is capital (formula: dynamic total investment × capital ratio) and the statutory surplus reserve limit (generated according to fixed rules). Long-term loan repayment methods can be selected via drop-down menus such as equal principal and interest payments / equal principal payments. All parameters must be strictly entered to ensure the accuracy of capital logic, interest calculation, and repayment plan.

[0032] The second set of basic costs and expenses are configured in segments based on a 25-year operating period. Core parameters are manually entered (values / rates / personnel, etc.), and the VAT rate is selected via a drop-down menu (13% / 9% / 6% / 0%). The system automatically calculates derivative items (such as the amount excluding tax, depreciation, and amortization). Capital expenditures such as major repairs and replacements (e.g., an investment of 200 million yuan in the 10th year) are listed as separate items, and input tax is automatically converted (e.g., 16.5138 million yuan).

[0033] The second operating revenue is centered on automatically calculated power generation sales revenue (formula: grid-connected electricity volume × (grid-connected electricity price - grid ancillary service allocation - two detailed assessment rules)), divided into two modules: grid-connected electricity sales revenue and revenue from electricity consumption through data integration. For grid-connected electricity sales revenue, the grid-connected electricity volume directly references production-related data; however, the grid-connected electricity price, grid ancillary service allocation, and the two detailed assessment rules parameters need to be manually entered year by year for the 25-year operating period. The VAT rate is selected via a drop-down menu (e.g., 13%) and the amount including / excluding tax is automatically generated. For revenue from electricity consumption through data integration, the electricity consumption volume and the data integration mechanism price directly reference production-related data; the VAT rate is selected via a drop-down menu (e.g., 13%) and the amount including / excluding tax is automatically generated. Non-operating income mainly consists of government subsidies, including two core parameters: a one-time government operating subsidy of RMB 2.5 million needs to be manually entered; deferred revenue recognition requires manual entry of the allocated amount for each year from year 1 to year 25 of the operating period, covering the entire project cycle. All numerical units are uniformly in RMB 10,000, and all operations are manual input. Other income configuration fields support custom income names. Core operations include manual input of quantity and unit price of income, and automatic separation of price and tax by selecting the VAT rate through the drop-down menu.

[0034] The tax information covers five core tax parameters: Value-added tax (VAT) preferential policies offer drop-down menu options (regular tax rates or immediate refund rates require manual input); corporate income tax preferential policies support multi-select menus; urban construction tax rates are fixed by region; education surcharge has a pre-set fixed tax rate; and urban land use tax requires manual input of the taxable land area and collection standard. All tax rate values ​​except for the immediate refund rate and land use tax can be locked via drop-down menus.

[0035] Then, for step 102, the first basic parameter is input into the preset integrated investment model, and the second basic parameter is input into the preset single-type investment model respectively, and the cost data corresponding to the self-invested computing power scenario and the non-invested computing power scenario are output in sequence.

[0036] In this embodiment of the invention, cost data is obtained as follows: based on the power supply structure and cost target of the data-energy fusion system under the self-invested computing power scenario, the first operating data is input into the preset single-type investment model and the fusion investment model respectively, and the fusion cost data including new energy power supply, cabinet and computing power leasing revenue under the self-invested computing power scenario is output; based on the energy consumption capacity under the non-invested computing power scenario, the second operating data is input into the single-type investment model and the fusion investment model respectively, and the benchmark cost data including the power consumption deficit and pure power sales revenue under the non-invested computing power scenario is output.

[0037] Specifically, the power supply from new energy sources is calculated using the following formula: In the formula, , , These represent the photovoltaic power generation in photovoltaic projects, the wind power generation in wind power projects, and the discharge volume in energy storage projects, respectively. The proportion of photovoltaic power consumed by the integration of data and energy in photovoltaic projects; The proportion of wind power consumption in wind power projects that integrates data and energy; The proportion of energy storage power consumed by data integration in energy storage projects; , , These represent the photovoltaic power, wind power, and energy storage power connected to the data and energy integration system, respectively. This indicates the power supply of the integrated project within the integrated project; The rental income is calculated using the following formula: In the formula, This represents the revenue from server rack rentals, including tax; N is the quantity of server racks of different types. Indicates the first The number of rack types; This represents the unit price of the i-th rack type; The corresponding calculated VAT rate; This indicates that the revenue from leasing includes tax. This is revenue from computing power leasing excluding tax; The corresponding VAT rate is used to calculate the revenue from computing power leasing.

[0038] For scenarios where no computing power is invested, the system focuses on calculating the shortfall in costs for new energy power consumption, revenue data from pure electricity sales, and providing benchmark cost data for comparative analysis of traditional energy projects.

[0039] For step 104, financial statements for the corresponding scenarios are generated based on the basic parameters of the self-invested computing power scenario and the non-invested computing power scenario and the cost data, and financial indicators used to characterize the feasibility of the data fusion project are determined.

[0040] In this embodiment, financial statements for the corresponding scenario are generated by establishing an associated computational relationship between the basic parameters and cost data obtained in the above steps.

[0041] like Figure 2 As shown, the financial statements for the self-invested computing power scenario include the first statement of capital raising and repayment of principal and interest, the first statement of depreciation and amortization, the first statement of income and taxes, the first statement of profit and loss - capital, the first statement of cash flow - capital, the first statement of profit and loss - total investment, the first statement of cash flow - total investment, the first statement of cash flow - financial plan, and the first balance sheet.

[0042] The first funding schedule details the funds required for the project during the construction and operation phases, including two main modules: dynamic total investment and funding sources. Funding sources are further divided into equity capital and debt financing. The main calculation indicators are the specific amounts of funds used for construction investment, construction period interest, and working capital. These are calculated using parameters such as the static total investment, equity ratio, and construction period interest loan rate entered into the parameter input table.

[0043] The first loan repayment schedule shows the details of current borrowings, interest accrued, and repayments during the project's construction and operation periods. The summary table adds up the long-term borrowings, working capital borrowings, and short-term borrowings. Long-term borrowings are calculated using two methods: equal principal and interest payments and equal principal payments. For equal principal and interest payments, an annuity formula is used to first calculate the sum of principal and interest each year (a fixed value), then the interest payable for each year is calculated based on the beginning and current borrowing amounts for each year, thus estimating the principal repaid each year. For equal principal payments, the principal repayment each year is a fixed value (total construction period borrowings / repayment period), and the interest payable for each year is calculated based on the beginning and current borrowing amounts for each year. The current borrowings are referenced from the corresponding year's debt financing data in the "Fundraising Schedule."

[0044] The first depreciation and amortization schedule primarily describes the net value and amortization of core assets during the construction and operation periods, including fixed assets, intangible assets, and the net value of assets acquired through major repairs and replacements at the beginning of the period, covering both equity capital and total investment. It mainly includes core indicators such as net fixed asset value, current fixed asset depreciation, and current amortization. The relevant calculations are obtained from the parameter input table and corresponding parameters in related tables. Furthermore, some indicators have a strong correlation between similar periods and require calculation on a period-by-period basis.

[0045] The first income and tax statement mainly consists of three sub-statements: the income statement, the tax statement, and the working capital estimation statement. The income statement includes indicators such as operating revenue, power generation sales revenue, non-operating income, and other income during the construction and operation periods. The tax statement includes core indicators such as the actual value-added tax paid in the current period, the output value-added tax, the input value-added tax, and the value-added tax surcharges. It mainly uses model boundary parameters such as the value-added tax rate, the first year's utilization hours, and the actual installed capacity from the parameter input table, as well as intermediate parameters such as site rental fees and insurance premiums from the cost and expense table.

[0046] The VAT refund upon collection is a parameter input table. The tax section offers optional parameters. If "normal" is selected, there is no VAT refund upon collection, and the refund rate is 0%. If "refund upon collection" is selected, the refund rate must be entered simultaneously. The VAT refund amount in year n = VAT actually paid in year n * refund rate. The working capital estimation table is used to calculate the difference between the revenue recognized in the profit and loss statement and the actual revenue received in the cash flow statement for a certain income item. This difference is recorded in accounts receivable. The associated revenue item in the parameter table where the payable revenue and actual revenue are not completely equal is "power generation subsidy" in this model.

[0047] The first profit statement displays detailed profit information for each year during the project's construction and operation periods. It primarily includes total profit, net profit, distributable profit, profit available for investor distribution, and undistributed profit, as well as key related indicators used in profit calculation. All revenue and cost figures in the profit statement are exclusive of tax. Yellow highlighted sections represent relatively fixed modules applicable to all projects, while blue highlighted sections represent intermediate calculation processes and may not be displayed. This table is calculated using relevant indicators such as government one-time subsidy income—deferred revenue recognition from the reference parameter input table, interest payments (summary—equal principal and interest) from the financing and principal and interest repayment table, current depreciation and amortization (equity) from the depreciation and amortization table, cost indicators from the cost and expense table, and revenue indicators from the revenue and tax table. Losses from previous years are carried forward on a 5-year rolling basis.

[0048] The first cash flow statement – ​​capital – mainly includes cash inflows (such as debt financing, electricity sales revenue, other income, etc.), cash outflows (construction investment, working capital support, debt expenditures, production and operating expenses, etc.), pre-tax net cash flow (1-2), cumulative pre-tax net cash flow, corporate income tax, and after-tax net cash flow. The calculation of each indicator uses indicators such as current borrowings (summary - equal principal and interest) and principal repayment (equal principal and interest) from the financing and repayment statement, intermediate indicators such as electricity sales revenue, electricity sales tax, other income, and other sales taxes from the income and tax statement, and indicators such as the first-year payment ratio from the parameter income statement. Specifically, the electricity subsidy revenue data for each year in the cash flow statement references the actual disbursement data entered in the parameter table (including tax and considering delayed payments, i.e., the actual payment ratio for the current year + the remaining payment ratio for the previous year). For government one-time operating subsidy revenue, the total amount of government one-time subsidies in the parameter table is referenced in the first year of the operating period.

[0049] The first cash flow statement – ​​total investment – ​​mainly includes core indicators such as cash inflows (power generation sales revenue, other income, and one-time government operating subsidies, etc.), cash outflows (construction investment, working capital, production and operation support, and non-operating expenses, etc.), pre-tax net cash flow, cumulative pre-tax net cash flow, adjusted income tax, after-tax net cash flow, and cumulative after-tax net cash flow. Its calculation primarily uses boundary parameters such as the first-year payment ratio (power generation sales revenue and other income), subsidy amount (government operating subsidies (one-time)) and working capital from the parameter input table, as well as intermediate calculation indicators such as the static total investment (self-investment) from the financing and principal and interest repayment table, the total production and operation cash outflows and non-operating cash outflows from the cost and expense table, and power generation sales revenue and power generation sales tax from the income table. Specifically, the power generation subsidy revenue data for each year in the cash flow statement references the actual disbursement data entered in the parameter table, and the government one-time operating subsidy revenue, in the first year of the operating period, references the total amount of the government one-time subsidy from the parameter table.

[0050] The First Cash Flow Statement - Financial Plan displays the annual net cash inflows and outflows during the project's construction and operation periods. It is divided into five sections: Net Cash Flow from Operating Activities, Net Cash Flow from Investing Activities, Net Cash Flow from Financing Activities, Net Cash Flow on the Project Company's Book Value, and Accumulated Surplus Funds. Each section contains corresponding inflow and outflow details. This statement is derived by referencing the parameter input table, the Funding and Principal / Interest Repayment Table, the Cost and Expense Table, the Revenue and Tax Table, the Profit and Loss Statement - Capital, and the Cash Flow Statement - Capital, and by performing simple summations of relevant indicators.

[0051] The core indicators of the first balance sheet mainly include total assets (current assets and non-current assets), total liabilities (current liabilities and non-current liabilities), owners' equity (or shareholders' equity), and total liabilities and owners' equity. The calculation primarily references the ending balance of borrowings in the statement of financing and repayment of principal and interest (the statement of principal and interest repayment of borrowings (short-term borrowings, working capital borrowings, long-term borrowings, and capital)); intermediate indicators in the statement of depreciation and amortization, such as newly added major repairs and replacement assets, current fixed asset depreciation, current depreciation and amortization of major repairs and replacement assets, and net fixed asset value; and intermediate indicators in the income statement - capital statement, such as government one-time operating subsidies - deferred income, accumulated statutory surplus reserve at the end of the period, and retained earnings. The calculation requires performing calculations for all indicators period by period to address the interdependencies between them.

[0052] like Figure 3 As shown, the financial statements for scenarios without computing power investment include the Second Statement of Funding and Principal and Interest Repayment, the Second Statement of Depreciation and Amortization, the Second Statement of Revenue and Taxes, the Second Statement of Profit and Loss - Capital, the Second Statement of Cash Flow - Capital, the Second Statement of Profit and Loss - Total Investment, the Second Statement of Cash Flow - Total Investment, the Second Statement of Cash Flow - Financial Plan, and the Second Balance Sheet.

[0053] The second funding schedule details the funds required for the project during the construction and operation phases, including two main modules: dynamic total investment and funding sources. Funding sources are further divided into equity capital and debt financing. The main calculation indicators are the specific amounts of funds used for construction investment, construction period interest, and working capital. These are calculated using parameters such as the static total investment, equity ratio, and construction period interest loan rate entered into the parameter input table.

[0054] The second loan repayment schedule shows the details of current borrowings, interest accrued, and repayments during the project's construction and operation periods. The summary table adds up the long-term borrowings, working capital borrowings, and short-term borrowings. Long-term borrowings are calculated using two methods: equal principal and interest payments and equal principal payments. For equal principal and interest payments, an annuity formula is used to first calculate the sum of principal and interest (a fixed value) each year, then the interest payable for each year is calculated based on the beginning and current loan amounts for each year, thus deducing the principal repaid each year. For equal principal payments, the principal repayment each year is a fixed value (total construction period borrowings / repayment period), and the interest payable for each year is calculated based on the beginning and current loan amounts for each year. The current borrowings are referenced from the corresponding year's debt financing data in the "Fundraising Schedule."

[0055] The second depreciation and amortization schedule primarily describes the net value and amortization of core assets during the construction and operation periods, including fixed assets, intangible assets, and the net value of assets acquired through major repairs and replacements at the beginning of the period, covering both equity capital and total investment. It mainly includes core indicators such as net fixed asset value, current fixed asset depreciation, and current amortization. The relevant calculations are obtained from the parameter input table and corresponding parameters in related tables. Furthermore, some indicators have a strong correlation between similar periods and require calculation on a period-by-period basis.

[0056] The second income and tax statement mainly consists of three sub-statements: the income statement, the tax statement, and the working capital estimation statement. The income statement includes indicators such as operating revenue, power generation sales revenue, non-operating income, and other income during the construction and operation periods. The tax statement includes core indicators such as the actual value-added tax (VAT) paid in the current period, VAT output tax, VAT input tax, and VAT surcharges. It primarily uses model boundary parameters such as the VAT rate, first-year utilization hours, and actual installed capacity from the parameter input table, as well as intermediate parameters such as site rental costs and insurance premiums from the cost and expense table. The VAT immediate refund is a parameter input table. The tax section offers optional conditional parameters. If "normal" is selected, there is no VAT immediate refund benefit, and the refund rate is 0%. If "immediate refund" is selected, the immediate refund rate is entered simultaneously. The VAT immediate refund amount in year n = actual VAT paid in year n * refund rate. The working capital estimation statement is used to calculate the difference between the revenue recognized in the profit statement and the actual revenue received in the cash flow statement for a certain income item. This difference is recorded in accounts receivable. The income item associated with the parameter table where the payable income and the actual income are not completely equal is "power generation subsidy" in this model.

[0057] The second profit statement – ​​capital – displays detailed profit information for each year during the project's construction and operation periods. This includes total profit, net profit, distributable profit, profit available for investor distribution, and undistributed profit, as well as key indicators used in profit calculation. All revenue and cost figures in the profit statement are exclusive of tax. Yellow highlighted sections represent relatively fixed modules applicable to all projects, while blue highlighted sections represent intermediate calculation processes and may not be displayed. This statement is calculated using relevant indicators such as government one-time subsidy income – deferred revenue recognition, interest payments (summary – equal principal and interest) in the financing and principal and interest repayment table, current depreciation and amortization (capital) in the depreciation and amortization table, cost indicators in the cost and expense table, and revenue indicators in the revenue and tax table. Losses from previous years are carried forward on a 5-year rolling basis.

[0058] The second cash flow statement – ​​capital – mainly includes cash inflows (such as debt financing, electricity sales revenue, other income, etc.), cash outflows (construction investment, working capital support, debt expenditures, production and operating expenses, etc.), pre-tax net cash flow (1-2), cumulative pre-tax net cash flow, corporate income tax, and after-tax net cash flow. The calculation of each indicator uses indicators such as current borrowings (summary - equal principal and interest) and principal repayment (equal principal and interest) from the financing and repayment statement, intermediate indicators such as electricity sales revenue, electricity sales tax, other income, and other sales taxes from the income and tax statement, and indicators such as the first-year payment ratio from the parameter income statement. Specifically, the electricity subsidy revenue data for each year in the cash flow statement references the actual disbursement data entered in the parameter table (including tax and considering delayed payments, i.e., the actual payment ratio for the current year + the remaining payment ratio for the previous year). For government one-time operating subsidy revenue, the total amount of government one-time subsidies in the parameter table is referenced in the first year of the operating period.

[0059] The second profit statement – ​​total investment – ​​displays detailed profit information for each year during the project's construction and operation periods. It primarily includes total profit, net profit, distributable profit, profit available for investor distribution, and undistributed profit, as well as key indicators used in profit calculation. All revenue and cost figures in the profit statement are exclusive of tax. Yellow highlighted sections represent relatively fixed modules applicable to all projects, while blue highlighted sections represent intermediate calculation processes and may not be displayed. This statement is calculated using relevant indicators such as government one-time subsidy income – deferred revenue recognition, interest payments (summary – equal principal and interest) in the financing and principal and interest repayment table, current depreciation and amortization (capital) in the depreciation and amortization table, cost indicators in the cost and expense table, and revenue indicators in the revenue and tax table. Losses from previous years are carried forward on a 5-year rolling basis.

[0060] The second cash flow statement – ​​Total Investment – ​​mainly includes core indicators such as cash inflows (power generation sales revenue, other income, and one-time government operating subsidies, etc.), cash outflows (construction investment, working capital, production and operation support, and non-operating expenses, etc.), pre-tax net cash flow, cumulative pre-tax net cash flow, adjusted income tax, after-tax net cash flow, and cumulative after-tax net cash flow. Its calculation primarily uses boundary parameters from the parameter input table, such as the first-year payment ratio (power generation sales revenue and other income), subsidy amount (government operating subsidies (one-time)), and working capital, as well as intermediate calculation indicators such as the static total investment (self-investment) in the financing and principal and interest repayment table, the total production and operation cash outflows and non-operating cash outflows in the cost and expense table, and power generation sales revenue and power generation sales tax in the income table. Specifically, the power generation subsidy revenue data for each year in the cash flow statement references the actual disbursement data entered in the parameter table, and the government one-time operating subsidy revenue, in the first year of the operating period, references the total amount of the government one-time subsidy in the parameter table.

[0061] The second cash flow statement – ​​Financial Plan – displays the annual net cash inflows and outflows during the project's construction and operation periods. It is divided into five sections: net cash flow from operating activities, net cash flow from investing activities, net cash flow from financing activities, net cash flow on the project company's books, and accumulated surplus funds. Each section contains corresponding inflow and outflow details. This statement is derived by referencing the parameter input table, the financing and principal and interest repayment table, the cost and expense table, the income and tax table, the profit statement – ​​capital, and the cash flow statement – ​​capital, and by performing simple summation calculations on the relevant indicators.

[0062] The core indicators of the second balance sheet mainly include total assets (current assets and non-current assets), total liabilities (current liabilities and non-current liabilities), owners' equity (or shareholders' equity), and total liabilities and owners' equity. The calculation primarily references the ending balance of borrowings in the statement of financing and repayment of principal and interest (the statement of principal and interest repayment of borrowings (short-term borrowings, working capital borrowings, long-term borrowings, and capital)); intermediate indicators in the statement of depreciation and amortization, such as newly added major repairs and replacement assets, current fixed asset depreciation, current major repairs and replacement asset depreciation and amortization, and net fixed asset value; and intermediate indicators in the income statement - capital statement, such as government one-time operating subsidies - deferred income, accumulated statutory surplus reserve at the end of the period, and retained earnings. The calculation requires performing calculations for all indicators period by period to address the interdependencies between them.

[0063] Furthermore, in order to quantitatively assess the economic viability and feasibility of the project based on the above reports, it is necessary to calculate the following two indicators: net present value and internal rate of return.

[0064] Net Present Value (NPV) is calculated using the following formula: If NPV is positive, it indicates that the project's return exceeds the benchmark rate of return, making it an worthwhile investment. Essentially, it discounts future funds at different points in time to the present using a certain discount rate, reflecting the time value of money.

[0065] The internal rate of return (IRR) is calculated using the following formula: In the formula, CF t For the first t Net cash flow for the period r The discount rate is... n For the project cycle.

[0066] If the IRR is higher than the industry benchmark rate of return, the project is feasible.

[0067] For step 106, risk analysis is performed based on the financial statements and financial indicators to determine an investment decision plan that meets the preset requirements.

[0068] In this embodiment of the invention, an investment decision scheme that meets preset requirements is determined through the following steps: Sensitivity curves for single-factor sensitivity, two-factor sensitivity, and three-factor sensitivity are calculated sequentially based on the aforementioned fiscal indicators and key parameters determined from the financial statements. The sensitive factors that have the greatest impact on the revenue of the data-energy fusion system are then identified based on the sensitivity curves.

[0069] like Figures 4-6 As shown, sensitivity analysis is first performed on a single factor. Sensitivity analysis in the financial and economic evaluation model of investment projects is an important uncertainty analysis method. It aims to identify sensitive factors that significantly impact the economic benefit indicators of investment projects from numerous uncertainties, and to analyze and calculate the degree of their influence and sensitivity on these indicators, thereby assessing the project's risk tolerance. The following are the implementation steps of sensitivity analysis: 1) Determine sensitivity analysis indicators: Select key indicators that can reflect the economic benefits of the investment project as the objects of sensitivity analysis, such as net present value (NPV), internal rate of return (IRR), and investment payback period.

[0070] 2) Calculate the target value of the technical solution: Based on the current investment scale, operating costs, expected returns and other parameters, calculate the target value of the financial and economic evaluation model of the investment project, that is, the optimal economic benefit index under the current conditions.

[0071] 3) Selecting Uncertain Factors: From the numerous uncertainties of the investment project, select factors that may have a significant impact on economic benefits for sensitivity analysis. These factors may include market demand, product prices, raw material prices, production costs, construction period, financing conditions, etc.

[0072] 4) Calculate the impact of changes in uncertain factors on the analytical indicators: Change the values ​​of the selected uncertain factors one by one, and calculate the impact of each factor on the economic benefit indicators under different changes. This is usually achieved by calculating the sensitivity coefficient or plotting the sensitivity curve.

[0073] 5) Identify sensitive factors, analyze them, and take countermeasures: Based on the calculation results, identify the sensitive factors that have the greatest impact on economic benefit indicators. Conduct in-depth analysis of these sensitive factors to understand their specific impact on economic benefits in terms of mode and extent. Simultaneously, based on the analysis results, formulate corresponding risk response measures to improve the risk resistance of the investment project.

[0074] For two-factor sensitivity analysis, it is assumed that the uncertain factors are independent of each other. Two factors are examined at the same time each time, while other factors remain unchanged, in order to analyze the degree of influence and sensitivity of the two variable factors on the economic benefit indicators.

[0075] For three-factor sensitivity analysis, it is assumed that the uncertainties are independent of each other. All three factors are examined simultaneously each time, while other factors remain constant, to analyze the degree of influence and sensitivity of these three variable factors on economic benefit indicators. Taking a two-factor sensitivity analysis of a photovoltaic model as an example... Figure 7 The selected sensitivity factors are the annual on-grid electricity of the photovoltaic system and the average annual electricity price during the operating period of the photovoltaic system. The impact of the simultaneous change of the two factors on the selected financial evaluation indicators is considered. According to the results, it can be seen that as the annual on-grid electricity of the photovoltaic system and the average annual electricity price during the operating period of the photovoltaic system change simultaneously, the value of the internal rate of return of the total investment will increase accordingly.

[0076] Furthermore, the break-even point of the data-energy integration system is determined based on the balance between costs and benefits in the financial statements, serving as a static risk benchmark to collaboratively assess the risk resistance capability of the data-energy integration system with sensitive factors.

[0077] like Figure 8 As shown, break-even analysis in project investment analysis is an important financial analysis method, also known as loss balance analysis or break-even point analysis. It is a method for studying the balance between costs and benefits in an investment project. By analyzing the interrelationships between output, costs, and revenue, it identifies the critical point between project profitability and loss, i.e., the break-even point.

[0078] The break-even analysis steps for project investment typically include the following aspects: (1) Determine key parameters: total cost and expenses, total sales tax and surcharges and total sales revenue (excluding value-added tax).

[0079] (2) Calculate the break-even point: Based on the calculation formula of total cost / (total sales revenue (excluding VAT) - total sales tax surcharge), the break-even point (production capacity utilization rate) is obtained.

[0080] (3) Analyze the break-even point: assess whether the project’s break-even point is reasonable, whether the profit margin is sufficient, and whether the project has sufficient risk resistance.

[0081] Taking the photovoltaic model as an example, such as Figure 9As shown, the break-even point for this project corresponds to a capacity utilization rate of 77.31%. This means that when the capacity utilization rate reaches 77.31%, the sales revenue exactly offsets the fixed costs of 282.034 million yuan; if the utilization rate is lower than 77.31%, the project will fall into a loss-making range; if the utilization rate is higher than 77.31%, the marginal contribution generated by the excess will be fully converted into net profit.

[0082] Furthermore, based on the aforementioned financial statements, scenario models are constructed to simulate project performance under specific conditions in order to conduct a comprehensive, assumption-based risk assessment of the data-energy fusion system.

[0083] Scenario analysis in investment project evaluation is a specialized research method that delves into the macroeconomic environment in which an investment project exists and the various potential future scenarios. Its key feature is its ability to comprehensively consider multiple factors, including policy, market, technology, and environment, thereby providing a more holistic assessment of the risks and returns of new energy projects. The application steps are as follows: (1) Clarify the analysis objectives: First, it is necessary to clarify the specific objectives and requirements of the investment project in order to determine the focus and direction of the scenario analysis.

[0084] (2) Collect relevant information: Collect information on policies, markets, technologies, environment and other aspects related to the investment project to provide data support for scenario analysis.

[0085] (3) Constructing scenarios: Based on the collected information, construct multiple possible future scenarios. These scenarios should cover various situations that the project may face, including favorable scenarios, unfavorable scenarios, and neutral scenarios.

[0086] (4) Analyze the impact of scenarios: For each constructed scenario, analyze its impact on the investment project. This includes the assessment of project costs, benefits, risks, etc., as well as the project's adaptability and development capacity under different scenarios.

[0087] (5) Develop response strategies: Based on the results of the scenario analysis, develop corresponding response strategies. These strategies should aim to reduce project risks, increase returns, and enhance the project's adaptability.

[0088] Taking the photovoltaic model as an example, such as Figure 10 As shown, two scenarios are set up: Scenario 1 has an average annual electricity price (including VAT) of 0.3 billion yuan during the photovoltaic system's operating period, and Scenario 2 has an average annual electricity price (including VAT) of 0.4 billion yuan during the photovoltaic system's operating period. The results of this scenario analysis are presented in two tables: investment return indicators and annual financial indicators. The scenario values ​​are compared with the original values ​​in the tables. Green indicates an increase, meaning the scenario value is greater than the original value; red indicates a decrease, meaning it is less than the original value; no label indicates equality. It can be seen that the project's return decreases in Scenario 1, while the project's return increases in Scenario 2.

[0089] Finally, based on the aforementioned sensitive factors and financial statements, various random scenarios are simulated, and the static risk benchmark and comprehensive risk assessment results are integrated based on the simulation results to determine the risk-adjusted decision support scheme.

[0090] like Figure 11 As shown, Monte Carlo simulation analysis is a highly effective tool in project investment economic evaluation. Based on probability and statistics principles, it predicts possible project outcomes by simulating a large number of random samples. Monte Carlo simulation analysis is mainly applied in the following aspects of project investment economic evaluation: (1) Project cost assessment: By randomly sampling, the probability distribution of various costs is determined, the uncertainty and risk of project costs are assessed, and investment decision-makers are helped to formulate reasonable cost control strategies.

[0091] (2) Investment return assessment: Establish the probability distribution of investment return rate, assess the uncertainty and risk of investment return, help decision-makers judge the feasibility of investment, and formulate corresponding risk control strategies.

[0092] (3) Sensitivity analysis and risk analysis: Monte Carlo simulation can identify sensitive factors that have a significant impact on the economic benefits of a project and quantify the degree of impact of these factors on the project, thereby helping investors better understand the risks and potential benefits of the project.

[0093] The specific steps for Monte Carlo simulation analysis are as follows: (1) Identify risk factors: Through sensitivity analysis, identify the key risk factors that affect the economic efficiency of project investment. These factors may include market demand, product prices, raw material prices, production costs, etc.

[0094] (2) Determine the probability distribution of the risk random variables: Based on historical data or expert judgment, determine a suitable probability distribution for each risk factor. This usually involves statistical analysis of the data to determine the statistical characteristics of each risk factor, such as the mean and variance.

[0095] (3) Determine the number of simulations: Determine the number of Monte Carlo simulations based on the complexity of the project and the required accuracy. The more simulations, the closer the results will be to the real situation, but the computational load will also be greater.

[0096] (4) Generate random numbers: Use a random number generator (such as the RAND function in Excel or the random number generation function in programming software such as Python) to generate a large number of random numbers for each risk factor. These random numbers represent the possible values ​​that the risk factor may take in the future.

[0097] (5) Calculate economic evaluation indicators: Based on the basic data and generated random numbers, calculate the economic evaluation indicators for each simulation, such as net present value (NPV) and internal rate of return (IRR).

[0098] (6) Organize and analyze simulation results: Perform statistical analysis on the simulation results, calculate the expected value, variance, standard deviation and other statistics of economic evaluation indicators, and draw probability distribution charts and cumulative probability charts. These charts can help investors intuitively understand the economic performance of the project under different conditions.

[0099] (7) Risk assessment and decision-making: Based on the simulation results, assess the risks and potential returns of the project, and make reasonable decisions in combination with the investor's risk tolerance and investment objectives.

[0100] Taking the photovoltaic model as an example, such as Figure 12 As shown, parameter simulations were performed using a uniform distribution of the annual on-grid electricity of the photovoltaic system with a maximum value of 40,000 MWh and a minimum value of 20,000 MWh. Similarly, parameter simulations were performed using a uniform distribution of the average annual electricity price (including VAT) during the photovoltaic system's operating period with a maximum value of 0.4 billion yuan and a minimum value of 0.3 billion yuan. The internal rate of return (IRR) for total investment, internal rate of return (IRR) for equity, total investment payback period, and equity payback period corresponding to each simulated electricity volume and price parameter were obtained. Analysis of the distribution of the simulation results for these four evaluation indicators, and comparison with the set benchmark values, revealed that the probability of meeting the benchmark values ​​was 100%. This means that if the project's electricity volume and price are within the simulated range of variation, the project has a high probability of generating revenue, and the project's economic risk is relatively low.

[0101] This invention provides an investment assistance decision-making device for a data-energy fusion system, the device comprising: The acquisition module is used to acquire the first basic parameters of wind, solar and energy storage equipment and data centers under the scenario of self-invested computing power, and the second basic parameters of wind, solar and energy storage equipment and data centers under the scenario of no computing power investment. The calculation module is used to input the first basic parameter into a preset integrated investment model and input the second basic parameter into a preset single-type investment model respectively, and output the cost data corresponding to the self-invested computing power scenario and the non-invested computing power scenario in sequence. The generation module is used to generate financial statements for corresponding scenarios based on the basic parameters and cost data of self-invested computing power scenarios and non-invested computing power scenarios, and to determine financial indicators used to characterize the feasibility of data fusion projects. The analysis module is used to perform risk analysis based on the financial statements and financial indicators to determine investment decision schemes that meet preset requirements.

[0102] In this embodiment of the invention, when the calculation module executes the process of inputting the first basic parameters into a preset integrated investment model and inputting the second basic parameters into preset single-type investment models respectively, and sequentially outputs cost data corresponding to the self-invested computing power scenario and the non-invested computing power scenario, the process includes: inputting the first operating data into the preset single-type investment model and the integrated investment model respectively according to the power supply structure and cost target of the data-energy integration system under the self-invested computing power scenario, and outputting integrated cost data including new energy power supply, cabinet and computing power leasing revenue under the self-invested computing power scenario; and inputting the second operating data into the single-type investment model and the integrated investment model respectively according to the energy consumption capacity under the non-invested computing power scenario, and outputting benchmark cost data including the shortfall cost of power consumption and the pure power sales revenue under the non-invested computing power scenario.

[0103] In this embodiment of the invention, the power supply from the new energy source is calculated using the following formula: In the formula, , , These represent the photovoltaic power generation in photovoltaic projects, the wind power generation in wind power projects, and the discharge volume in energy storage projects, respectively. The proportion of photovoltaic power consumed by the integration of data and energy in photovoltaic projects; The proportion of wind power consumption in wind power projects that integrates data and energy; The proportion of energy storage power consumed by data integration in energy storage projects; , , These represent the photovoltaic power, wind power, and energy storage power connected to the data and energy integration system, respectively. This indicates the power supply of the integrated project within the integrated project; The rental income is calculated using the following formula: In the formula, This represents the revenue from server rack rentals, including tax; N is the quantity of server racks of different types. Indicates the first The number of rack types; This represents the unit price of the i-th rack type; The corresponding calculated VAT rate; This indicates that the revenue from leasing includes tax. This is revenue from computing power leasing excluding tax; The corresponding VAT rate is used to calculate the revenue from computing power leasing.

[0104] In this embodiment of the invention, the financial statements for the self-invested computing power scenario include a fundraising and principal and interest repayment statement, a depreciation and amortization statement, a cost and expense statement, a revenue and tax statement, a profit statement, and a cash flow statement, calculated sequentially; the financial statements for the non-invested computing power scenario include a fundraising and principal and interest repayment statement, a depreciation and amortization statement, a revenue and tax statement, a profit statement, a cash flow statement, a financial plan statement, and a balance sheet, calculated sequentially.

[0105] In this embodiment of the invention, the fiscal indicators include the net present value and internal rate of return of the data-energy fusion system, wherein: The net present value (NPV) is calculated using the following formula: The internal rate of return (IRR) is calculated using the following formula: In the formula, CF t For the first t Net cash flow for the period r The discount rate is... n For the project cycle.

[0106] In this embodiment of the invention, risk analysis is performed based on the financial statements and the financial indicators to determine an investment decision scheme that meets preset requirements, including: Sensitivity curves for single-factor sensitivity, two-factor sensitivity, and three-factor sensitivity are calculated sequentially based on the aforementioned fiscal indicators and key parameters determined from the financial statements. The sensitive factors that have the greatest impact on the revenue of the data-energy fusion system are then identified based on the sensitivity curves. The break-even point of the data-energy integration system is determined based on the cost-benefit balance in the financial statements, and is used as a static risk benchmark to assess the risk resistance of the data-energy integration system in conjunction with sensitive factors. Based on the financial statements, a scenario model is constructed to simulate the project's performance under specific conditions in order to conduct a comprehensive, assumption-based risk assessment of the data-energy fusion system. Based on the aforementioned sensitive factors and financial statements, various random scenarios are simulated, and the static risk benchmark and comprehensive risk assessment results are integrated based on the simulation results to determine a risk-adjusted decision support scheme.

[0107] It should be noted that the investment assistance decision-making device for a data-energy integration system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The investment assistance decision-making device for a data-energy integration system provided in the above embodiments and the investment assistance decision-making method embodiments for a data-energy integration system belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0108] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for investment support decision-making in a data-energy fusion system, characterized in that, The method includes: Obtain the first basic parameters of wind, solar and energy storage equipment and data centers under the scenario of self-invested computing power, and the second basic parameters of wind, solar and energy storage equipment and data centers under the scenario of no computing power investment; The first basic parameter is input into a preset integrated investment model, and the second basic parameter is input into a preset single-type investment model respectively. The cost data corresponding to the self-invested computing power scenario and the non-invested computing power scenario are output in sequence. Based on the basic parameters and cost data of the self-invested computing power scenario and the non-invested computing power scenario, generate corresponding financial statements for the scenario, and determine the financial indicators used to characterize the feasibility of the data fusion project. Based on the financial statements and financial indicators, a risk analysis is conducted to determine an investment decision plan that meets the preset requirements.

2. The method as described in claim 1, characterized in that, The step of inputting the first basic parameter into a preset integrated investment model and the second basic parameter into preset single-type investment models respectively, and sequentially outputting cost data corresponding to self-invested computing power scenarios and non-invested computing power scenarios, includes: Based on the power supply structure and cost target of the data-energy fusion system under the self-invested computing power scenario, the first operating data is input into the preset single-type investment model and fusion investment model respectively, and the fusion cost data including new energy power supply, cabinet and computing power leasing revenue under the self-invested computing power scenario is output. Based on the energy absorption capacity under the scenario without computing power investment, the second operating data is input into the single-type investment model and the integrated investment model respectively, and the benchmark cost data including the shortfall cost of power absorption and the pure power sales revenue under the scenario without computing power investment is output.

3. The method as described in claim 2, characterized in that, The power supply from the new energy source is calculated using the following formula: In the formula, , , These represent the photovoltaic power generation in a photovoltaic project, the wind power generation in a wind power project, and the discharge amount in an energy storage project, respectively. The proportion of photovoltaic power consumed by the integration of data and energy in photovoltaic projects; The proportion of wind power consumption in wind power projects that integrates data and energy; The proportion of energy storage power consumed by data integration in energy storage projects; , , These represent the photovoltaic power, wind power, and energy storage power connected to the data and energy integration system, respectively. This indicates the power supply of the integrated project within the integrated project; The rental income is calculated using the following formula: In the formula, This represents the revenue from server rack rentals, including tax; N is the quantity of server racks of each type. Indicates the first The number of rack types; This represents the unit price of the i-th rack type; The corresponding calculated VAT rate; This indicates that the revenue from leasing includes tax. This is revenue from computing power leasing excluding tax; The corresponding VAT rate is used to calculate the revenue from computing power leasing.

4. The method as described in claim 1, characterized in that, The financial statements for the self-invested computing power scenario include, in sequence, a statement of fundraising and principal and interest repayment, a statement of depreciation and amortization, a statement of costs and expenses, a statement of income and taxes, a statement of profit and loss, and a statement of cash flow. The financial statements for the scenario without computing power investment include, in sequence, a statement of fundraising and principal and interest repayment, a statement of depreciation and amortization, a statement of income and taxes, a statement of profit and loss, a statement of cash flow, a statement of financial planning, and a statement of balance sheet.

5. The method as described in claim 1, characterized in that, The financial indicators include the net present value and internal rate of return of the data-energy integration system, wherein: The net present value (NPV) is calculated using the following formula: The internal rate of return (IRR) is calculated using the following formula: In the formula, CF t For the first t Net cash flow for the period r The discount rate is... n For the project cycle.

6. The method as described in claim 1, characterized in that, Based on the aforementioned financial statements and financial indicators, a risk analysis is conducted to determine an investment decision plan that meets preset requirements, including: Sensitivity curves for single-factor sensitivity, two-factor sensitivity, and three-factor sensitivity are calculated sequentially based on the aforementioned fiscal indicators and key parameters determined from the financial statements. The sensitive factors that have the greatest impact on the revenue of the data-energy fusion system are then identified based on the sensitivity curves. The break-even point of the data-energy integration system is determined based on the cost-benefit balance in the financial statements, and is used as a static risk benchmark to assess the risk resistance of the data-energy integration system in conjunction with sensitive factors. Based on the aforementioned financial statements, a scenario model is constructed to simulate the project's performance under specific conditions in order to conduct a comprehensive, hypothesis-based risk assessment of the data-energy fusion system. Based on the aforementioned sensitive factors and financial statements, various random scenarios are simulated, and the static risk benchmark and comprehensive risk assessment results are integrated based on the simulation results to determine a risk-adjusted decision support scheme.

7. An investment assistance decision-making device for a data-energy fusion system, characterized in that, The device includes: The acquisition module is used to acquire the first basic parameters of wind, solar and energy storage equipment and data centers under the scenario of self-invested computing power, and the second basic parameters of wind, solar and energy storage equipment and data centers under the scenario of no computing power investment. The calculation module is used to input the first basic parameter into a preset integrated investment model and input the second basic parameter into a preset single-type investment model respectively, and output the cost data corresponding to the self-invested computing power scenario and the non-invested computing power scenario in sequence. The generation module is used to generate financial statements for corresponding scenarios based on the basic parameters and cost data of self-invested computing power scenarios and non-invested computing power scenarios, and to determine financial indicators used to characterize the feasibility of data fusion projects. The analysis module is used to perform risk analysis based on the financial statements and financial indicators to determine investment decision schemes that meet preset requirements.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

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