Real-time intelligent measurement and calculation system and method for new energy project

Abstract

The invention provides a real-time intelligent measurement and calculation system and method for a new energy project, and relates to the technical field of power distribution network investment construction, and the system comprises a data collection module, a data preprocessing and storage module, a digital measurement and calculation model library, a man-machine interaction and data input module, a measurement and calculation engine module and a result output module. Real-time fine-granularity load data of a user power grid gateway electric meter is obtained through non-intrusive infrared acquisition equipment, and time-serialized power consumption and average power data are generated after preprocessing. The system is internally provided with a measurement and calculation model for digitalizing a traditional table model, a user can be guided to input project boundary parameters, real-time load data and manual parameter operation calculation are automatically fused, and finally an accurate measurement and calculation result containing key financial indexes is output. The method can solve the problems of extensive data and strong subjectivity in traditional investment calculation, and improves the scientificity and accuracy of new energy project investment decision.

Description

Technical Field

[0001] This invention relates to the field of power distribution network investment and construction technology, specifically to a real-time intelligent calculation system and method for new energy projects. Background Technology

[0002] As the development of national new energy projects enters a stage of rapid growth and fierce competition, the accuracy of project investment calculations directly affects the success or failure of the investment. Currently, traditional new energy project investment calculations rely primarily on manual methods. A typical process involves: first, obtaining electricity load information such as recent user electricity bills, which usually spans a large timeframe (e.g., monthly) and has coarse data granularity; then, analysts manually calculate the expected optimal installation capacity of new energy facilities and project economic indicators based on specific calculation models or empirical formulas.

[0003] This traditional method has significant drawbacks: on the one hand, the input data it relies on (electricity bills) is not timely and has coarse granularity, making it difficult to reflect the details of users' actual daily load fluctuations, leading to deviations in capacity configuration and power generation forecasts; on the other hand, the manual calculation process is highly dependent on the subjective experience of analysts, and there are many unquantifiable subjective factors in the selection of model parameters and the setting of boundary conditions, which may result in large differences in the calculation results of different personnel or at different times, lacking objectivity and consistency, and thus having limited guiding role in investment decisions.

[0004] Therefore, there is an urgent need for a new technology solution that can be based on real-time, detailed load data and can achieve objective and automated calculations to improve the accuracy and scientific nature of investment evaluation for new energy projects. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a real-time intelligent calculation system and method for new energy projects. The solution of the real-time intelligent calculation system and method for new energy projects provided by this invention is as follows:

[0006] Firstly, a real-time intelligent calculation system for new energy projects is provided, the system comprising:

[0007] The data acquisition module is configured to use a non-intrusive infrared acquisition method to read the cumulative electricity information from the user's power gateway meter at adjustable time intervals and generate raw real-time load data.

[0008] The data preprocessing and storage module is communicatively connected to the data acquisition module and is configured to clean the raw real-time load data, remove invalid data points, and calculate and generate user electricity consumption and average power data arranged in time series at specific time intervals based on the cleaned data.

[0009] The digital calculation model library stores at least one digital calculation model corresponding to a specific new energy project type. The calculation model is implemented by computer programming and encapsulates the formulas and calculation logic in the original calculation tables of the corresponding project type. The model defines a first type of input item and a second type of input item. The first type of input item is configured to receive the user's electricity consumption and / or average power data from the data preprocessing and storage module. The second type of input item is configured to receive parameters manually filled in by the user.

[0010] The human-computer interaction and data input module is configured to provide a user interface to guide users to select the calculation model corresponding to the project type, and guide users step by step to fill in the various manual input parameters required for the second type of input item;

[0011] The measurement engine module, connected to the data preprocessing and storage module, the digital measurement model library, and the human-computer interaction and data input module, is configured to perform the following operations: invoking the corresponding digital measurement model based on user selection; automatically importing the user's electricity consumption and / or average power data generated by the data preprocessing and storage module into the first type of input item of the model; receiving the manually entered parameters submitted by the human-computer interaction and data input module and filling them into the second type of input item of the model; running the digital measurement model for calculation; and...

[0012] The results output module is configured to display the calculation results output by the calculation engine module after it runs.

[0013] Preferably, the data acquisition module specifically includes: an infrared detector, a local data acquisition unit, and a wireless communication unit;

[0014] The infrared probe is aligned with the infrared interface of the power gateway meter in a non-contact manner.

[0015] The local collector is connected to the infrared probe via a wired connection to receive and temporarily store the accumulated power information.

[0016] The wireless communication unit is used to send the accumulated battery power information to the remote data preprocessing and storage module.

[0017] Preferably, the data preprocessing and storage module performs data cleaning including:

[0018] The original real-time load data is examined according to the time series, and data points with values ​​of zero or lower than the previous time point are identified as invalid data and removed.

[0019] Preferably, the specific time interval is five minutes, for example.

[0020] The data preprocessing and storage module calculates the difference between the cumulative power consumption readings at two consecutive time points to obtain the user's power consumption at a five-minute interval, and further calculates the average power within those five minutes.

[0021] Preferably, the second type of input items defined by the models in the digital calculation model library includes at least one of the following parameter categories: user basic information, power grid access parameters, cost parameters, electricity price parameters, equipment technical parameters, and financial and tax parameters.

[0022] Preferably, when the system is used for calculation of industrial and commercial energy storage projects, the second type of input items includes: user type, line capacity, access transformer capacity, energy storage system construction cost, operation and management costs, peak-valley-flat electricity price, rated capacity and power of energy storage equipment, tax rate, and financing cost.

[0023] Preferably, the calculation results output by the result output module include at least one of the following financial indicators: static total investment, dynamic total investment, internal rate of return (IRR), payback period, net present value (NPV), debt service coverage ratio, and net profit after tax.

[0024] Preferably, the human-computer interaction and data input module is further configured as follows:

[0025] After the calculation engine module runs for the first time and outputs the calculation results, a user interface is provided for the user to adjust some of the manually entered parameters, and the calculation engine module is triggered to rerun the calculation model based on the adjusted parameters for iterative calculation.

[0026] Secondly, a real-time intelligent calculation method for new energy projects is provided, the method comprising:

[0027] Step S1: Deploy and start the data acquisition module to collect the cumulative electricity consumption data of the user's power gateway inter-meter via infrared at set time intervals;

[0028] Step S2: Receive the collected data through the data preprocessing and storage module, clean and calculate it, and generate and store time-series user electricity consumption and average power data;

[0029] Step S3: Through the human-computer interaction and data input module, the user selects the target new energy project type, and the system calls the corresponding digital calculation model;

[0030] Step S4: The system guides the user to fill in all the manually entered parameters required by the digital calculation model through the human-computer interaction and data input module;

[0031] Step S5: The calculation engine module automatically obtains the corresponding user electricity consumption and / or average power data from the data preprocessing and storage module, and imports it into the digital calculation model together with the manually filled parameters received in step S4.

[0032] Step S6: The calculation engine module runs the digital calculation model, generates calculation results containing financial indicators, and displays them through the result output module.

[0033] Preferably, the method further includes:

[0034] Step S7: Determine whether the calculation result meets the preset investment return requirements;

[0035] Step S8: If not satisfied, adjust one or more of the manually entered parameters through the human-computer interaction and data input module, and return to step S5. Use the adjusted parameters and the original real-time data to rerun the calculation until a calculation result that meets the requirements is obtained or the iteration limit is reached.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. This invention can significantly improve the accuracy of calculations. By collecting minute-level real-time load data to replace traditional monthly electricity bills, it provides a more refined data foundation, making capacity configuration and revenue forecasts more realistic.

[0038] 2. This invention can effectively improve the objectivity of the measurement, solidify the manual measurement model into a digital model, and guide the input of standardized parameters, thereby minimizing the subjective bias caused by human experience and making the results more reliable and comparable.

[0039] 3. This invention can achieve efficient automated calculation and iterative optimization. The system automatically completes data processing and model calculation, and users can quickly adjust parameters to compare multiple schemes, which greatly improves analysis efficiency and scheme optimization speed.

[0040] 4. This invention can build dynamic and continuous optimization capabilities. The long-term data accumulation supported by the system can be used for continuous calibration and optimization of the calculation model, forming a data-driven, self-improving analysis loop.

[0041] Other beneficial effects of the present invention will be explained in detail through the introduction of specific technical features and technical solutions in specific embodiments. Those skilled in the art should be able to understand the beneficial technical effects brought about by these technical features and technical solutions through the introduction of these technical features and technical solutions. Attached Figure Description

[0042] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0043] Figure 1 This is a schematic diagram of the system operation process;

[0044] Figure 2 A schematic diagram of the input interface for calculating boundary conditions;

[0045] Figure 3 This is a schematic diagram showing the output of financial calculation results. Detailed Implementation

[0046] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0047] This invention provides a real-time intelligent calculation system for new energy projects. Through high-frequency, non-intrusive data acquisition, it obtains real, granular load data from users, using this data as key input. This data is then combined with digital and model-based calculation tools to replace the traditional, extensive calculation methods that rely on monthly electricity bills and manual estimations, thereby significantly improving the accuracy and objectivity of investment calculations. (Refer to...) Figure 1 As shown, the system specifically includes:

[0048] The data acquisition module is responsible for obtaining raw electricity data from the source. It is configured to use a non-intrusive infrared acquisition method to read the cumulative electricity information from the user's power gateway meter at adjustable time intervals and generate raw real-time load data.

[0049] This module is deployed at the user's site and connects to the infrared sensor via a 485 communication cable. It reads the electricity information of the State Grid meter in a non-contact manner and sends the data to the platform receiver via a 4G module. The main hardware includes an infrared sensor, a local data collector, and a 4G / 5G wireless communication module.

[0050] The infrared probe is aligned with the display section of the electricity meter at the power grid interface in a non-contact manner, without damaging the meter's lead seal, and complies with power grid metering regulations. An infrared reader head suitable for the infrared interface wavelength and rated baud rate (bps) of the standard power grid meter is selected and fixedly installed at an appropriate distance in front of the meter's display panel to ensure clear and stable reading of the scrolling cumulative electricity consumption data (unit: kWh) on the meter's LCD screen.

[0051] The local data acquisition unit connects to the infrared probe via a wired connection to receive and temporarily store the accumulated electricity information. It can be an embedded industrial computer or a dedicated data acquisition unit (RTU). It connects to the infrared probe via an RS-485 or TTL level serial cable, configuring the reading command and reading interval (typically set to send a reading command every 5 minutes; this interval can be remotely configured and adjusted). The acquisition protocol adopts the power industry standard DL / T 645-2007. The data acquisition unit receives and temporarily stores the accumulated electricity data returned by the infrared probe, along with a timestamp and data quality code (used as a criterion to determine whether the data acquisition unit has correctly read the meter data).

[0052] The wireless communication unit is used to send accumulated battery power information to a remote data preprocessing and storage module. It is integrated within the data collector or connected via an interface. The time-stamped accumulated battery power data is packaged and uploaded to the cloud data preprocessing and storage module periodically (e.g., every 15 minutes) or in real-time via the operator's 4G / 5G network using protocols such as MQTT and HTTP. Local SD card storage is also supported for offline export.

[0053] The data preprocessing and storage module is connected to the data acquisition module and is configured to clean the raw real-time load data, remove invalid data points, and calculate and generate user electricity consumption and average power data arranged in time series for a specific time interval based on the cleaned data.

[0054] This module is deployed on a cloud server and receives data from various data collection terminals. Data cleaning includes checking the uploaded time-series cumulative electricity data stream, identifying and removing invalid data points. Specifically, it checks the original real-time load data according to the time sequence, identifying and removing data points with values ​​of zero or lower than the previous time point as invalid data. This mainly includes: a) data with a quality code marked as "bad"; b) abnormal data where subsequent readings are lower than previous readings due to meter resets, data collection interference, etc. The cleaning algorithm ensures that the data sequence is strictly non-monotonic and decreasing. Data calculation and structuring: For the cleaned cumulative electricity data sequence, the electricity consumption within a fixed time window (e.g., 5 minutes) is calculated, along with the average power within that window. The data preprocessing and storage module calculates the difference between the cumulative electricity readings of two consecutive time points to obtain the user's electricity consumption at five-minute intervals, and further calculates the average power within those five minutes. The calculated "time point-electricity consumption-average power" triplet data is structured and stored according to user and time dimensions, forming a standardized historical load curve and unit-time electricity consumption curve database. This database supports efficient queries by time range and data granularity.

[0055] The digital calculation model library stores at least one digital calculation model corresponding to a specific new energy project type. These models are implemented through computer programming and encapsulate the formulas and calculation logic from the original calculation tables for the corresponding project type. Each model defines two types of input items. The first type is configured to receive user electricity consumption and / or average power data from the data preprocessing and storage module. The second type is configured to receive parameters manually entered by the user. The second type of input items includes at least one of the following parameter categories: user basic information, grid access parameters, cost parameters, electricity price parameters, equipment technical parameters, and financial and tax parameters. When the system is used for calculating industrial and commercial energy storage projects, the second type of input items includes: user type, line capacity, access transformer capacity, energy storage system construction cost, operation and management costs, peak-valley-flat electricity price, rated capacity and power of energy storage equipment, tax rate, and financing cost. The constructed models are stored in a cloud-based model library in the form of executable files, scripts, or services, and are accompanied by version management and metadata descriptions (such as applicable project type, creator, and update date) for easy access and updates.

[0056] The human-computer interaction and data input module is configured to provide a user interface to guide users to select the calculation model corresponding to the project type, and guide users step by step to fill in the various manual input parameters required for the second type of input items.

[0057] This module is provided as a web application or desktop client. After the calculation engine module runs for the first time and outputs the calculation results, a user interface is provided for users to adjust some manually entered parameters, and the calculation engine module is triggered to rerun the calculation model based on the adjusted parameters for iterative calculation.

[0058] The calculation engine module is a background service program that connects to the data preprocessing and storage module, the digital calculation model library, and the human-computer interaction and data input module. It is configured to perform the following operations: call the corresponding digital calculation model according to the user's selection; automatically import the user's electricity consumption and / or average power data generated by the data preprocessing and storage module into the first type of input item of the model; receive manually filled parameters submitted by the human-computer interaction and data input module and fill them into the second type of input item of the model; and run the digital calculation model to perform calculations.

[0059] This module includes task scheduling: the engine is triggered when the user completes all input on the human-computer interaction interface and clicks "Start Calculation".

[0060] Data assembly: The engine first sends a request to the data preprocessing and storage module based on the user-selected data source and time period to obtain the corresponding load curve data. Simultaneously, it receives all manually entered parameters from the human-computer interaction and data input modules.

[0061] Model execution: The engine calls the corresponding model in the digital calculation model library and injects the assembled load data and manual parameters according to the model interface requirements (parameter passing).

[0062] Calculation Execution: The model code is executed in a secure sandbox or computing environment. The model code will perform iterative calculations according to preset financial and engineering formulas, simulating cash flow, power generation / charging / discharging behavior, revenue, costs, etc. throughout the project's entire lifecycle.

[0063] The results output module is configured to display the calculation results output by the calculation engine module after it runs, and supports exporting the complete calculation report to PDF, Word, or Excel format. The calculation results output by the results output module include at least one of the following financial indicators: static total investment, dynamic total investment, internal rate of return (IRR), payback period, net present value (NPV), debt service coverage ratio, and net profit after tax.

[0064] This invention also provides a real-time intelligent calculation method for new energy projects, which specifically includes:

[0065] Step S1: Deploy and start the data acquisition module to collect the cumulative electricity consumption data of the user's power gateway inter-meter via infrared at set time intervals;

[0066] Step S2: Receive the collected data through the data preprocessing and storage module, clean and calculate it, and generate and store time-series user electricity consumption and average power data;

[0067] Step S3: Through the human-computer interaction and data input module, the user selects the target new energy project type, and the system calls the corresponding digital calculation model;

[0068] Step S4: The system guides the user to fill in all the manually entered parameters required by the digital calculation model through the human-computer interaction and data input module;

[0069] Step S5: The calculation engine module automatically obtains the corresponding user electricity consumption and / or average power data from the data preprocessing and storage module, and imports them into the digital calculation model together with the manually filled parameters received in step S4.

[0070] Step S6: The calculation engine module runs the digital calculation model, generates calculation results containing financial indicators, and displays them through the result output module;

[0071] Step S7: Determine whether the calculation results meet the preset investment return requirements;

[0072] Step S8: If not satisfied, adjust one or more manually entered parameters through the human-computer interaction and data input module, and return to step S5. Use the adjusted parameters and the original real-time data to rerun the calculation until a calculation result that meets the requirements is obtained or the iteration limit is reached.

[0073] The present invention will now be described in more detail.

[0074] This invention provides a real-time intelligent measurement system for new energy projects. It uses infrared meter reading technology to directly collect electricity consumption data from user terminals and real-time load data from existing new energy terminals. The analysis system analyzes the collected data through a digital model and provides a relatively accurate measurement result.

[0075] Specifically, the infrared sensor is connected to the data collector via a 485 communication cable to read the electricity information of the State Grid electricity meter in a non-contact manner, and the data is sent to the platform receiving end via a 4G module.

[0076] After receiving the information, the platform preprocesses it, removing data with values ​​of 0 or decreasing numbers based on the time series. Based on the electricity consumption information, it generates electricity consumption data at specific intervals (e.g., electricity consumption over five minutes) and average power data (e.g., average power over five minutes). These two key indicators are crucial for selecting equipment capacity and power.

[0077] Based on the formulas in the existing calculation forms, a corresponding calculation model is constructed using a computer system. Based on the formulas and logic in the calculation forms for the corresponding project type, a calculation model is generated using computer programming or modeling. For the same project type, only one model needs to be created, and it can be reused subsequently. The model must simultaneously define and specify that the input data is divided into imported quantities and manually entered quantities.

[0078] The system automatically imports infrared data from the model and guides users to fill in manually entered data.

[0079] Once all input values ​​are complete, run the calculation model to obtain the calculation results.

[0080] Specifically, taking energy storage calculation as an example, the system operation process is roughly explained as follows:

[0081] (1) Select the calculation model, taking industrial and commercial energy storage as an example;

[0082] (2) User basic data entry: This step requires entering the user type (factory, hospital, school, etc.), user line capacity, and connected transformer capacity into the system;

[0083] (3) Install infrared meter readers and data collectors at the user's gateway, and store the data locally or upload it to the platform (user authorization required);

[0084] (4) Select real-time data (96 points / day) of typical user electricity load time periods as the calculation criterion;

[0085] (5) Input calculation boundary conditions: Based on the original calculation model table, select or input the calculation boundary conditions, such as construction costs, operation and management costs, electricity price information, business expenses, key parameters of the energy storage system, tax rate, financing costs, etc., referring to... Figure 2 As shown.

[0086] (6) The system runs and calculates the rate of return based on the original table model logic and calculation method, including financial data such as static total investment, dynamic total investment, IRR, payback period, NPV, average debt service coverage ratio, and average net profit after tax, to assist the company in making project investment decisions. Figure 3 As shown.

[0087] (7) If the calculation results do not meet the investment expectations, some boundary conditions can be reset and the calculation can be repeated;

[0088] (8) The energy storage capacity selection is based on the specifications and models provided by the current equipment manufacturers as boundary conditions. It can be recalculated by changing the key parameters of the equipment and the number of equipment to meet or approach the financial requirements of the investment.

[0089] This invention provides a real-time intelligent measurement system and method for new energy projects. It collects real-time user load data, increasing the granularity of input data and improving the accuracy of measurement results. It quantifies objective factors affecting project measurement, minimizing subjectivity and enhancing the objectivity of the results. Real-time data is integrated into the platform's measurement system, collecting user data over long periods and continuously updating the measurement results; the longer the collection period, the more accurate the results. Increasing the sample size improves the accuracy of the model's operation.

[0090] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0091] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A real-time intelligent calculation system for new energy projects, characterized in that, include: The data acquisition module is configured to use a non-intrusive infrared acquisition method to read the cumulative electricity information from the user's power gateway meter at adjustable time intervals and generate raw real-time load data. The data preprocessing and storage module is communicatively connected to the data acquisition module and is configured to clean the raw real-time load data, remove invalid data points, and calculate and generate user electricity consumption and average power data arranged in time series at specific time intervals based on the cleaned data. The digital calculation model library stores at least one digital calculation model corresponding to a specific type of new energy project; the model defines a first type of input item and a second type of input item, the first type of input item is configured to receive the user's electricity consumption and / or average power data from the data preprocessing and storage module, and the second type of input item is configured to receive parameters manually filled in by the user; The human-computer interaction and data input module is configured to provide a user interface to guide users to select the calculation model corresponding to the project type, and guide users step by step to fill in the various manual input parameters required for the second type of input item; The measurement engine module is connected to the data preprocessing and storage module, the digital measurement model library, and the human-computer interaction and data input module, and is configured to perform the following operations: call the corresponding digital measurement model according to the user's selection; automatically import the user's electricity consumption and / or average power data generated by the data preprocessing and storage module into the first type of input item of the model; receive the manually filled parameters submitted by the human-computer interaction and data input module and fill them into the second type of input item of the model; The digital measurement model is run to perform calculations; and, The results output module is configured to display the calculation results output by the calculation engine module after it runs.

2. The real-time intelligent calculation system for new energy projects according to claim 1, characterized in that, The data acquisition module specifically includes: an infrared detector, a local data acquisition unit, and a wireless communication unit; The infrared probe is aligned with the infrared interface of the power gateway meter in a non-contact manner. The local collector is connected to the infrared probe via a wired connection to receive and temporarily store the accumulated power information. The wireless communication unit is used to send the accumulated battery power information to the remote data preprocessing and storage module.

3. The real-time intelligent calculation system for new energy projects according to claim 1, characterized in that, The data preprocessing and storage module performs data cleaning including: The original real-time load data is examined according to the time series, and data points with values ​​of zero or lower than the previous time point are identified as invalid data and removed.

4. The real-time intelligent calculation system for new energy projects according to claim 1 or 3, characterized in that, The specific time interval is five minutes; The data preprocessing and storage module calculates the difference between the cumulative power consumption readings at two consecutive time points to obtain the user's power consumption at a five-minute interval, and further calculates the average power within those five minutes.

5. The real-time intelligent calculation system for new energy projects according to claim 1, characterized in that, The second type of input items defined by the models in the digital calculation model library include at least one of the following parameter categories: user basic information, power grid access parameters, cost parameters, electricity price parameters, equipment technical parameters, and financial and tax parameters.

6. The real-time intelligent calculation system for new energy projects according to claim 5, characterized in that, When the system is used for calculation of industrial and commercial energy storage projects, the second type of input items includes: user type, line capacity, access transformer capacity, energy storage system construction cost, operation and management costs, peak-valley-flat electricity price, rated capacity and power of energy storage equipment, tax rate, and financing cost.

7. The real-time intelligent calculation system for new energy projects according to claim 1, characterized in that, The calculation results output by the result output module include at least one of the following financial indicators: static total investment, dynamic total investment, internal rate of return (IRR), payback period, net present value (NPV), debt service coverage ratio, and net profit after tax.

8. The real-time intelligent calculation system for new energy projects according to claim 1, characterized in that, The human-computer interaction and data input module is also configured as follows: After the calculation engine module runs for the first time and outputs the calculation results, a user interface is provided for the user to adjust some of the manually entered parameters, and the calculation engine module is triggered to rerun the calculation model based on the adjusted parameters for iterative calculation.

9. A real-time intelligent calculation method for new energy projects, based on the real-time intelligent calculation system for new energy projects according to any one of claims 1-8, characterized in that, include: Step S1: Deploy and start the data acquisition module to collect the cumulative electricity consumption data of the user's power gateway inter-meter via infrared at set time intervals; Step S2: Receive the collected data through the data preprocessing and storage module, clean and calculate it, and generate and store time-series user electricity consumption and average power data; Step S3: Through the human-computer interaction and data input module, the user selects the target new energy project type, and the system calls the corresponding digital calculation model; Step S4: The system guides the user to fill in all the manually entered parameters required by the digital calculation model through the human-computer interaction and data input module; Step S5: The calculation engine module automatically obtains the corresponding user electricity consumption and / or average power data from the data preprocessing and storage module, and imports it into the digital calculation model together with the manually filled parameters received in step S4. Step S6: The calculation engine module runs the digital calculation model, generates calculation results containing financial indicators, and displays them through the result output module.

10. The real-time intelligent calculation method for new energy projects according to claim 9, characterized in that, The method further includes: Step S7: Determine whether the calculation result meets the preset investment return requirements; Step S8: If not satisfied, adjust one or more of the manually entered parameters through the human-computer interaction and data input module, and return to step S5. Use the adjusted parameters and the original real-time data to rerun the calculation until a calculation result that meets the requirements is obtained or the iteration limit is reached.

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