New energy station simulation evaluation method and system based on multi-data fusion

Abstract

The invention discloses a new energy station simulation evaluation method and system based on multi-data fusion, and relates to the technical field of data processing. The method comprises the following steps: acquiring environmental parameters of an area where a target new energy station is located and layout parameters of a plurality of power generation units in the target new energy station; constructing a three-dimensional simulation interface of the target new energy station according to the layout parameters of the power generation units, and generating a first power generation simulation curve in the three-dimensional simulation interface according to the rated power and the environmental parameters of the power generation units; receiving power generation data of the target new energy station, and generating a second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface; and combining the first power generation simulation curve and the second power generation simulation curve to generate a production ratio simulation evaluation curve of the target new energy station, and displaying the production ratio simulation evaluation curve on a three-dimensional simulation interface. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of simulation evaluation of the new energy station is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and specifically to a new energy station simulation evaluation method and system based on multi-data fusion. Background Art

[0002] As the global demand for renewable energy grows, the planning, construction, and operation and maintenance of new energy sites, especially wind and solar power stations, have become increasingly important. In order to optimize the design of these sites and improve their operating efficiency, new energy site simulation and evaluation technology has emerged. This technology aims to predict and verify the commissioning of sites under different application conditions through high-precision simulation to guide actual construction and adjustment strategies.

[0003] At present, the existing simulation evaluation method of new energy stations usually collects historical power generation data of new energy stations in the past period to simulate and evaluate the commissioning of new energy stations, and presents it in the form of a report. However, in actual applications, since the commissioning of new energy stations is affected by many conditions, it is often difficult to reflect the actual commissioning of new energy stations by only considering the power generation data of a single dimension, resulting in deviations in the simulation evaluation results, thereby reducing the accuracy of the simulation evaluation of new energy stations. Summary of the invention

[0004] The present application provides a new energy station simulation evaluation method, system, electronic device and storage medium based on multi-data fusion, which can improve the accuracy of new energy station simulation evaluation.

[0005] In a first aspect, the present application provides a new energy station simulation evaluation method based on multi-data fusion, comprising: Acquire environmental parameters of the area where the target new energy site is located and layout parameters of multiple power generation units in the target new energy site; According to the layout parameters of each of the power generation units, a three-dimensional simulation interface of the target new energy site is constructed, and according to the rated power of each of the power generation units and the environmental parameters, a first power generation simulation curve is generated in the three-dimensional simulation interface; Receiving power generation data of the target new energy station, and generating a second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface; The first power generation simulation curve and the second power generation simulation curve are combined to generate a production ratio simulation evaluation curve of the target new energy site, and the production ratio simulation evaluation curve is displayed on the three-dimensional simulation interface.

[0006] In a second aspect of the present application, a new energy station simulation evaluation system based on multi-data fusion is provided, the system comprising: A parameter acquisition module, used to acquire environmental parameters of the area where the target new energy station is located and layout parameters of multiple power generation units in the target new energy station; A first power generation simulation curve determination module, used to construct a three-dimensional simulation interface of the target new energy site according to the layout parameters of each power generation unit, and generate a first power generation simulation curve in the three-dimensional simulation interface according to the rated power of each power generation unit and the environmental parameters; a second power generation simulation curve determination module, configured to receive power generation data of the target new energy station and generate a second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface; The production ratio simulation evaluation module is used to combine the first power generation simulation curve and the second power generation simulation curve to generate a production ratio simulation evaluation curve of the target new energy site, and display the production ratio simulation evaluation curve on the three-dimensional simulation interface.

[0007] In the third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a new energy station simulation evaluation method based on multi-data fusion when loaded and executed by the processor.

[0008] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a new energy station simulation evaluation method based on multi-data fusion.

[0009] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By adopting the above technical scheme, the environmental parameters of the area where the target new energy station is located and the layout parameters of multiple power generation units in the station are obtained, and various factors affecting the efficiency of new energy power generation are fully considered. First, the method constructs a three-dimensional simulation interface according to the layout parameters of the power generation unit, and truly reproduces the spatial layout of the station on site, ensuring the consistency of the simulation model with the actual site. Subsequently, the first power generation simulation curve is generated in combination with the rated power and environmental parameters of the power generation unit, which can accurately simulate the theoretical power generation under ideal conditions. At the same time, the actual power generation data is received and the second power generation simulation curve is generated, so that the simulation evaluation can reflect the power generation situation in actual operation. By combining the first power generation simulation curve with the second power generation simulation curve, a production ratio simulation evaluation curve is generated, which can not only intuitively show the difference between theoretical and actual power generation, but also quantitatively evaluate the actual operating efficiency of the station. Finally, the production ratio simulation evaluation curve is displayed on the three-dimensional simulation interface, providing intuitive and visual evaluation results, which is convenient for users to conduct comprehensive analysis and decision-making. Through the fusion of multi-dimensional data and three-dimensional simulation display, the limitations of the traditional single data dimension evaluation method are overcome, and a more comprehensive evaluation method is provided, thereby improving the accuracy of the simulation evaluation of new energy stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flow chart of a new energy station simulation evaluation method based on multi-data fusion provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a new energy station simulation evaluation system based on multi-data fusion provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0011] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0012] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0013] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0014] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0015] The present application embodiment provides a new energy station simulation evaluation method based on multi-data fusion. In one embodiment, please refer to Figure 1 , Figure 1 This is a flow chart of a new energy station simulation evaluation method based on multi-data fusion provided in an embodiment of the present application. The method can be implemented by a computer program, which can be integrated in an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run on a new energy station simulation evaluation system based on multi-data fusion based on a von Neumann system. Specifically, the method can include the following steps: Step 101: Obtain environmental parameters of the area where the target new energy station is located and layout parameters of multiple power generation units in the target new energy station.

[0016] Among them, the target new energy site refers to a centralized power generation site that uses renewable energy to generate electricity, which can be a solar photovoltaic power station, a wind power station or a hybrid power station of the two. The target new energy site includes multiple power generation units.

[0017] Environmental parameters refer to the quantitative indicators of external environmental factors that affect the power generation efficiency of the target new energy site, mainly including light intensity data, temperature data, wind speed data, etc.

[0018] The power generation unit refers to the basic functional unit in the target new energy site that can convert renewable energy into electrical energy. Depending on the type of new energy, it can include solar photovoltaic modules and wind turbines.

[0019] Layout parameters refer to the geometric parameters that describe the spatial distribution characteristics of each power generation unit in the target new energy site, mainly including parameters such as spatial coordinates, orientation angle, and spacing between power generation units.

[0020] Specifically, since the power generation efficiency of the new energy station is affected by a variety of environmental factors and layout factors, in order to achieve an accurate evaluation of the target new energy station, it is first necessary to obtain the environmental parameters of the area where the target new energy station is located and the layout parameters of multiple power generation units in the target new energy station. By arranging multiple environmental monitoring devices at different locations of the target new energy station, environmental parameters including light intensity data, temperature data and wind speed data are collected in real time. Among them, the environmental monitoring device includes a light sensor, a temperature sensor and a wind speed sensor, which continuously collect environmental data at a preset sampling period, and transmit the collected environmental data to the data processing unit through a data acquisition module. At the same time, the layout parameters of the power generation unit are extracted from the engineering design file of the target new energy station, including information such as the spatial coordinates, orientation angle and spacing of each power generation unit. These layout parameters reflect the specific installation position and relative position relationship of the power generation unit in the station, which directly affect the mutual shielding effect and overall power generation efficiency between the power generation units. By obtaining these environmental parameters and layout parameters, basic data support can be provided for the subsequent construction of a three-dimensional simulation interface and the generation of a power generation simulation curve, and a basis can also be provided for evaluating the actual power generation efficiency of the power generation unit and optimizing the layout plan. This method based on multi-dimensional data collection can comprehensively reflect the key factors affecting the power generation efficiency of new energy sites and effectively improve the accuracy of subsequent simulation evaluations.

[0021] Step 102: construct a three-dimensional simulation interface of the target new energy site according to the layout parameters of each power generation unit, and generate a first power generation simulation curve in the three-dimensional simulation interface according to the rated power and environmental parameters of each power generation unit.

[0022] Among them, the three-dimensional simulation interface refers to a virtual digital scene constructed based on the actual layout of the target new energy station. The interface uses a three-dimensional coordinate system to display the spatial layout characteristics and operating status of the station.

[0023] Rated power refers to the maximum electric power value that a power generation unit can stably output under standard test conditions, reflecting the basic power generation capacity of the power generation unit.

[0024] The first power generation simulation curve refers to a theoretical power output curve calculated based on the rated power and environmental parameters of the power generation unit. The curve reflects the ideal power generation performance of the target new energy site under specific environmental conditions.

[0025] Specifically, in order to intuitively display the spatial layout of the target new energy site and evaluate its theoretical power generation performance, it is necessary to construct a three-dimensional simulation interface and generate the first power generation simulation curve. First, the initial layout position is determined according to the spatial coordinates in the layout parameters of each power generation unit, and the shading influence coefficient of each power generation unit is calculated based on the orientation angle. In this process, the solar trajectory curve is generated by obtaining the solar azimuth and altitude angle at multiple standard time points, and the shadow length and projection direction of each power generation unit at different time points are calculated in combination with the orientation angle of the power generation unit. These shadow information are mapped to the plane coordinate system of the target new energy site to form a shadow overlap matrix, and then the cumulative shading time of each power generation unit is determined, and the corresponding shading influence coefficient is determined through a preset coefficient mapping table. Subsequently, the initial layout position of each power generation unit is optimized and adjusted in combination with the power generation unit spacing and the shading influence coefficient to obtain the target layout position, thereby constructing a three-dimensional simulation interface that accurately reflects the spatial distribution characteristics of the site. When generating the first power generation simulation curve, the system extracts light intensity data, temperature data, and wind speed data from the environmental parameters, determines the corresponding environmental impact factors through the preset environmental parameter mapping table, corrects the calibrated conversion efficiency of the power generation unit, and obtains the target power conversion efficiency. The rated power of each power generation unit is multiplied by the corresponding target power conversion efficiency to calculate the theoretical power output value, and these output values ​​are mapped to the coordinate system of the three-dimensional simulation interface to finally generate the first power generation simulation curve. This simulation method based on multi-parameter optimization can accurately reflect the power generation performance of the site under ideal conditions and provide a reference benchmark for subsequent comparative analysis with actual power generation data.

[0026] Based on the above embodiment, as an optional embodiment, in step 102: constructing a three-dimensional simulation interface of a target new energy station according to the layout parameters of each power generation unit, this step may also include the following steps: Step 201: Determine the spatial coordinates, orientation angles and spacing between power generation units in each layout parameter; determine the corresponding initial layout position according to the spatial coordinates of each power generation unit, and calculate the shading influence coefficient of each power generation unit based on the orientation angle of each power generation unit.

[0027] The spatial coordinates are expressed in a three-dimensional Cartesian coordinate system (X, Y, Z), where the X-axis and Y-axis define the position of the power generation unit in the station plane, and the Z-axis represents the installation height of the power generation unit. The spatial coordinates are used to determine the precise position of each power generation unit in the station and are the basic data for building a three-dimensional simulation interface.

[0028] The orientation angle describes the spatial orientation characteristics of the power generation unit, and the orientation angle directly affects the efficiency of the power generation unit in receiving renewable energy.

[0029] The spacing between power generation units refers to the minimum distance requirement between adjacent power generation units, including row spacing and column spacing.

[0030] Specifically, in order to accurately construct the three-dimensional simulation interface of the target new energy station and optimize the layout of the power generation unit, it is first necessary to clarify the specific spatial position and installation method of each power generation unit. The layout parameters of each power generation unit are extracted from the station design file, including the spatial coordinates (X, Y, Z) that define the position of the power generation unit in three-dimensional space, determine the orientation angle of the power generation unit to receive renewable energy, and ensure the minimum spacing requirements for the normal operation of the power generation unit. For solar photovoltaic modules, the orientation angle includes the azimuth (usually with the south direction as 0 degrees reference) and the tilt angle (the angle with the horizontal plane); for wind turbines, the windward direction of the wind rotor is mainly considered. Based on these spatial coordinate information, the system first marks the initial layout position of each power generation unit in the three-dimensional coordinate system. Subsequently, considering that the mutual shielding between power generation units will affect the power generation efficiency, it is necessary to calculate the shielding influence coefficient based on the orientation angle of each power generation unit. By combining parameters such as the solar azimuth and altitude angle to generate a solar trajectory curve, the shadow projection of the power generation unit at different time points is analyzed, and the shadow length and projection direction of each power generation unit at the standard time point are determined. The shadow information is mapped to the station plane coordinate system, and the shadow overlap matrix is ​​constructed to calculate the cumulative shading time of each power generation unit, and the corresponding shading influence coefficient is determined through the preset coefficient mapping table. This layout analysis method based on spatial geometric relationships and lighting laws can effectively evaluate the mutual influence between power generation units.

[0031] Based on the above embodiment, as an optional embodiment, in step 201: based on the orientation angle of each power generation unit, the shielding influence coefficient of each power generation unit is calculated. This step may also include the following steps: Step 211: Obtain the solar azimuth and altitude at multiple standard time points, and generate a solar trajectory curve based on the solar azimuth and altitude at each standard time point; determine the shadow length and projection direction of each power generation unit at each standard time point according to the orientation angle of each power generation unit and the solar trajectory curve.

[0032] The solar azimuth refers to the angle between the projection of sunlight on the horizontal plane and the due north direction. With the due north direction as the reference, the east is negative and the west is positive. At noon, when the sun is due south, the solar azimuth is 0 degrees. This angle reflects the relative direction of the sun on the horizontal plane and directly affects the shadow projection direction of the power generation unit.

[0033] The solar altitude angle refers to the angle between the sun's rays and the horizontal plane, ranging from 0 to 90 degrees. When the sun is on the horizon, the altitude angle is 0 degrees, and when it is at the zenith, the altitude angle is 90 degrees. The solar altitude angle changes with time and season, reaching its maximum value on the summer solstice and its minimum value on the winter solstice, which directly affects the shadow length of the power generation unit.

[0034] The sun path curve refers to a spatial curve that describes the apparent motion trajectory of the sun in the sky. This curve is formed by connecting the sun position data of multiple standard time points and reflects the movement pattern of the sun from sunrise to sunset.

[0035] Shadow length refers to the actual length of the shadow area formed by a power generation unit on the ground or other power generation unit surfaces under sunlight.

[0036] The projection direction refers to the spatial direction in which the shadow area extends, which is directly related to the solar azimuth and the orientation angle of the power generation unit. The projection direction is usually expressed as an angle with the true north direction, which changes dynamically with the change of the solar azimuth.

[0037] Specifically, in order to accurately evaluate the shading effect between power generation units, it is necessary to first simulate the movement trajectory of the sun in the sky and calculate the shadow projection of the power generation unit accordingly. Specifically, based on the geographic coordinates (longitude and latitude) of the target new energy site, the system selects typical dates of key solar terms such as the vernal equinox, summer solstice, autumnal equinox and winter solstice, and sets standard time points at fixed time intervals (such as 1 hour) on each typical date. For each standard time point, the azimuth (the angle between the sun's projection and the north direction) and altitude (the angle between the sun and the horizon) of the sun are calculated by astronomical algorithms. These angle data reflect the spatial position of the sun at a specific moment. The solar azimuth and altitude data of all standard time points are continuously mapped to the spherical coordinate system to form a solar trajectory curve that describes the sun's all-weather movement law. Subsequently, combined with the orientation angles (including azimuth and inclination angles) of each power generation unit, based on the principles of geometric optics, the incident angle of the sun's rays and the plane of the power generation unit is calculated, and the shadow length of the power generation unit at each standard time point is derived according to the physical size of the power generation unit. At the same time, the projection direction of the shadow is determined according to the relationship between the solar azimuth and the spatial position of the power generation unit. This shadow analysis method based on astronomical geometry can accurately simulate the shading characteristics of the power generation unit at different times of the year, provide basic data for the subsequent construction of the shadow overlap matrix, and ultimately achieve a quantitative assessment of the shading effect.

[0038] Step 221: Map the shadow length and projection direction of each power generation unit at each standard time point to the plane coordinate system of the target new energy site to obtain a shadow overlap matrix.

[0039] Among them, the shadow overlap matrix is ​​a two-dimensional data structure used to characterize the shading relationship between power generation units in the target new energy site.

[0040] Specifically, in order to quantitatively evaluate the occlusion relationship between power generation units, it is necessary to convert the shadow projection features in three-dimensional space into overlapping analysis on a two-dimensional plane. First, a plane coordinate system of the target new energy site is established, and the XOY plane projection grid is constructed based on the characteristic points of the site boundary. The grid density is determined according to the size characteristics of the power generation unit. For each standard time point, the system maps the shadow length and projection direction of each power generation unit to the plane coordinate system through geometric projection transformation. The transformation process takes into account the influence of terrain undulation on the shadow shape, and ensures the precise positioning of the shadow area through elevation correction. In the plane coordinate system, the shadow area of ​​each power generation unit is represented by a time-varying polygon, whose vertex coordinates are calculated by the shadow length and projection direction. The shadow polygons of all standard time points are superimposed on the same plane coordinate system to construct an M×N shadow overlap matrix, where M represents the number of power generation units and N represents the number of standard time points. Each element in the matrix records whether the shadow of a power generation unit at the corresponding time point overlaps with the position of other power generation units, as well as the area ratio of the overlapping area. This matrix analysis method based on plane projection can efficiently identify the mutual shading relationship between power generation units and provide data support for calculating the shading impact coefficient. By analyzing the time series characteristics of the shadow overlap matrix, the shading status of any power generation unit in the station at different times can be clearly understood, providing an accurate quantitative basis for subsequent layout optimization. In particular, the matrix can also be visualized in a three-dimensional simulation interface to help operation and maintenance personnel intuitively understand the spatial distribution characteristics of shading effects.

[0041] Step 231: Based on the shadow overlap matrix, determine the cumulative shading duration of each power generation unit, and determine the shading influence coefficient corresponding to each cumulative shading duration in a preset coefficient mapping table.

[0042] The cumulative shading duration refers to the total length of time that a power generation unit is affected by shading from other power generation units within the selected standard time point range. When the element value in the shadow overlap matrix exceeds the preset shading threshold, it means that there is effective shading at that time point. The time intervals of these time points are weighted and accumulated, and the sum of the time is the cumulative shading duration.

[0043] The preset coefficient mapping table is a data structure that establishes the corresponding relationship between the cumulative occlusion duration and the occlusion influence coefficient, and is used to convert the occlusion characteristics of the time dimension into dimensionless influence coefficients. The mapping table is based on historical operation data statistics, and uses a piecewise function or a lookup table to define the occlusion influence coefficients corresponding to the cumulative occlusion durations in different intervals.

[0044] Specifically, in order to convert the shadow overlap matrix into a quantitative indicator that can be used for layout optimization, it is necessary to calculate the cumulative shading duration of each power generation unit and establish a mapping relationship with the shading influence coefficient. First, the shadow overlap matrix is ​​analyzed in time series to count the time periods of each power generation unit affected by shading at all standard time points. When the value of the matrix element is greater than the preset shading threshold, for example, when the area overlap ratio exceeds 10%, it is considered that there is effective shading at this time point. The cumulative shading duration of each power generation unit is obtained by accumulating the time intervals of all effective shading time points. Considering the different degrees of shading influence in different time periods, the system performs weighted processing on the shading duration, with a higher weight for key time periods (such as time periods with greater sunshine intensity) and a lower weight for marginal time periods (such as early morning and evening). Subsequently, the weighted cumulative shading duration is input into a preset coefficient mapping table, which is established based on historical operation data and expert experience and defines the nonlinear correspondence between shading duration and influence coefficient. The shading influence coefficient of each power generation unit is obtained by table lookup or interpolation calculation. This method based on time series accumulation and mapping conversion can simplify the complex shading phenomenon into a single evaluation index, which is convenient for quantitative comparison and decision-making in the layout optimization process. In particular, the design of the coefficient mapping table takes into account the shading sensitivity of different types of power generation units, and adopts different mapping rules for solar photovoltaic modules and wind turbines to ensure that the calculated shading impact coefficient can accurately reflect the actual power generation performance loss.

[0045] Step 202: Based on the spacing between the power generation units and the shielding influence coefficients, the initial layout position of each power generation unit is adjusted to obtain the corresponding target layout position.

[0046] Among them, the shading influence coefficient refers to a dimensionless parameter that quantifies the degree of mutual shading between power generation units. Its value range is usually between 0 and 1, where 0 means no shading and 1 means complete shading. Specifically, the shading influence coefficient is a comprehensive indicator calculated by analyzing the shadow projection of power generation units at different time points and combining the cumulative shading duration.

[0047] The initial layout position refers to the installation position of each power generation unit in three-dimensional space, which is preliminarily determined based on the spatial coordinates in the site design document.

[0048] Specifically, in order to reduce the mutual shielding effect between power generation units and improve the overall power generation efficiency of the station, it is necessary to optimize and adjust the initial layout position of the power generation units. First, based on the shielding influence coefficient calculated in the previous steps, identify the power generation unit combination with more serious shielding effect. For these power generation units, their spatial positions are adjusted by iterative optimization under the premise of meeting the minimum power generation unit spacing constraint. During the adjustment process, the system uses the power generation unit spacing as a hard constraint to ensure that the distance between adjacent power generation units is not less than the design requirements. At the same time, the shielding influence coefficient is used as the optimization target. By changing the spatial coordinates of the power generation unit, the layout scheme that can minimize the overall shielding effect is found. For solar photovoltaic modules, the main consideration is to adjust the row spacing and column spacing, especially in areas with large terrain undulations, and comprehensive optimization needs to be combined with terrain characteristics; for wind turbines, the focus is on the unit spacing in the dominant wind direction to avoid significant wake interference caused by the upwind unit to the downwind unit. Through multiple rounds of iterative optimization, the target layout position of each power generation unit is finally determined, so that the overall shielding loss of the station is minimized. This layout optimization method based on multiple constraints can not only improve the theoretical power generation efficiency of the station, but also provide a more reasonable layout plan for actual engineering construction, ensuring that the expected power generation performance can be achieved after the station is built.

[0049] Step 203: Based on the target layout positions of the power generation units, a three-dimensional simulation interface of the target new energy station is generated.

[0050] The target layout position refers to the optimal installation position of the power generation unit that is finally determined after optimizing and adjusting the initial layout position and comprehensively considering the spacing between power generation units and the shading influence coefficient.

[0051] Specifically, in order to display the spatial layout characteristics of the target new energy station and provide a visual operation monitoring platform, it is necessary to build a three-dimensional simulation interface based on the optimized target layout position. First, the terrain basic model of the station is established in the three-dimensional modeling environment, and the terrain elevation data and surface feature information of the site are integrated into the coordinate system. Subsequently, according to the target layout position of each power generation unit, the three-dimensional model of the power generation unit is constructed at the corresponding spatial coordinate point. For solar photovoltaic components, the support structure and component inclination need to be accurately restored; for wind turbines, the geometric characteristics of key components such as wind rotors, nacelles and towers need to be reflected. After completing the construction of the basic model, the system configures an independent data label for each power generation unit to display real-time operating parameters, including power output values, environmental parameters, equipment status and other information. At the same time, the interactive control function is integrated in the three-dimensional simulation interface to support operations such as scene roaming, perspective switching and target tracking, so that operation and maintenance personnel can observe the station layout from different angles. In particular, the system can also superimpose the power generation simulation curve and the environmental parameter distribution cloud map in the three-dimensional scene to achieve spatial visualization of data. This simulation interface based on 3D visualization technology can not only help operation and maintenance personnel intuitively understand the spatial layout of the station, but also serve as an important tool for operation monitoring and efficiency evaluation, providing strong support for the daily operation and maintenance management of the station. By updating operation data and status information in real time in the 3D scene, operation and maintenance personnel can quickly identify abnormal situations, diagnose and handle faults in a timely manner, and improve the operation and maintenance efficiency of the station.

[0052] Based on the above embodiment, as an optional embodiment, in step 102: generating a first power generation simulation curve in a three-dimensional simulation interface according to the rated power and environmental parameters of each power generation unit, this step may also include the following steps: Step 204: for each power generation unit, light intensity data, temperature data and wind speed data are extracted from the environmental parameters, and according to a preset environmental parameter mapping table, environmental impact factors corresponding to the light intensity data, temperature data and wind speed data are respectively determined.

[0053] The preset environmental parameter mapping table is a multi-dimensional data structure used to establish a quantitative relationship between environmental parameters such as light intensity, temperature and wind speed and their corresponding influencing factors.

[0054] Specifically, in order to accurately evaluate the impact of environmental factors on the performance of power generation units, it is necessary to extract and convert key environmental parameters. The system first extracts the light intensity data, temperature data and wind speed data of each power generation unit location from the site monitoring data. For solar photovoltaic components, the focus is on the changing characteristics of light intensity, including the components of direct radiation and scattered radiation, and the impact of component surface temperature on power generation efficiency; for wind turbines, the focus is on the wind speed data at the hub height and its temporal and spatial distribution characteristics. From the environmental parameter mapping table, the corresponding environmental impact factors are obtained by searching or interpolating the actual values ​​of different parameters. Among them, the light intensity data is mapped to the light impact factor through the radiation conversion model, which reflects the deviation between the actual light conditions and the standard test conditions; the temperature data is mapped to the temperature impact factor through the temperature characteristic curve, which reflects the degree of influence of temperature changes on power generation efficiency; the wind speed data is mapped to the wind speed impact factor through the power curve, which characterizes the change of power generation performance under actual wind conditions. This environmental impact assessment method based on multi-parameter mapping can convert complex environmental conditions into standardized impact factors, which is convenient for comprehensive calculation in power generation efficiency evaluation. In particular, the calculation process of environmental impact factors takes into account the coupling effect between parameters, such as the correlation between temperature and light intensity, and the correlation between wind speed and temperature, to ensure that the evaluation results are more in line with the actual operating characteristics. Through the quantitative processing of environmental parameters, it can provide a correction basis for the subsequent power generation efficiency evaluation and improve the accuracy of the site performance evaluation.

[0055] Step 205: Correct the calibrated conversion efficiency of the power generation unit based on each environmental impact factor to obtain a target power conversion efficiency; multiply the rated power of each power generation unit by the corresponding target power conversion efficiency to obtain a theoretical power output value of each power generation unit.

[0056] Among them, the calibrated conversion efficiency refers to the energy conversion ratio of the power generation unit converting primary energy into electrical energy under standard test conditions.

[0057] The target power conversion efficiency refers to the energy conversion efficiency of the power generation unit under actual operating conditions after correction of environmental factors. This efficiency value is obtained by comprehensively correcting the calibrated conversion efficiency with environmental factors such as light influence factor, temperature influence factor and wind speed influence factor, reflecting the comprehensive impact of actual environmental conditions on power generation performance.

[0058] The theoretical power output value refers to the expected output power calculated based on the rated power and target power conversion efficiency of the power generation unit under the current environmental conditions. This value is obtained by multiplying the rated power of the power generation unit with the target power conversion efficiency corrected by environmental factors, and reflects the ideal power generation capacity of the equipment under the actual operating environment.

[0059] Specifically, in order to accurately predict the power generation performance of the power generation unit under actual environmental conditions, it is necessary to correct the calibrated conversion efficiency for environmental factors. First, the calibrated conversion efficiency of each power generation unit is obtained. This efficiency value is the benchmark efficiency measured under standard test conditions. For solar photovoltaic modules, the calibrated conversion efficiency is usually measured under standard light intensity and standard temperature conditions; for wind turbines, it is measured under standard air density and rated wind speed conditions. Subsequently, the light influence factor, temperature influence factor and wind speed influence factor obtained in the above steps are comprehensively calculated, and the calibrated conversion efficiency is corrected in the form of a product to obtain the target power conversion efficiency reflecting the actual operating conditions. This correction process takes into account the coupling effect of environmental parameters and can more accurately reflect the actual performance status of the power generation unit. Finally, the rated power of each power generation unit is multiplied by the corresponding target power conversion efficiency to calculate the theoretical power output value, which reflects the expected power generation capacity of the power generation unit under the current environmental conditions. This power prediction method based on environmental correction provides a reliable theoretical basis for the evaluation of the power generation efficiency and operation optimization of the site by converting the performance parameters under standard conditions into output characteristics under actual operating conditions. In particular, by displaying the theoretical power output value in the three-dimensional simulation interface, operation and maintenance personnel can intuitively compare the difference between actual power generation and theoretical predicted value, timely discover performance anomalies and perform targeted maintenance.

[0060] Step 206: Map the theoretical power output value of each power generation unit to the coordinate system of the three-dimensional simulation interface to generate a first power generation simulation curve.

[0061] Specifically, in order to demonstrate the theoretical power generation performance of the power generation unit, it is necessary to visualize the calculated theoretical power output value. The system first constructs a standardized coordinate system in the three-dimensional simulation interface. The horizontal axis represents the time dimension, and different time scales such as hours, days, and months can be selected; the vertical axis represents the power output value. For each power generation unit, its theoretical power output value at different time points is converted into discrete data points on the curve through coordinate mapping, and these data points are connected by smoothing methods such as spline interpolation or Bezier curves to form a continuous first power generation simulation curve. The time series characteristics of the data are taken into account in the generation process of the curve to ensure that the curve trend can accurately reflect the dynamic change law of power output. For solar photovoltaic modules, the first power generation simulation curve usually shows obvious daily variation characteristics, and the peak appears at noon; for wind turbines, the curve shape is mainly affected by changes in wind conditions and may show greater volatility. In the three-dimensional simulation interface, the first power generation simulation curves of different power generation units can be distinguished by different colors or line types to facilitate comparative analysis. Through this visual expression, operation and maintenance personnel can quickly grasp the theoretical power generation performance of each power generation unit in the station, identify the time distribution characteristics of power output, and provide intuitive data support for operation and management decisions.

[0062] Step 103: receiving power generation data of the target new energy station, and generating a second power generation simulation curve corresponding to the power generation data in a three-dimensional simulation interface.

[0063] Among them, power generation data refers to the set of electrical parameters collected by the site monitoring system that reflects the actual operating status of the power generation unit.

[0064] The second power generation simulation curve refers to the power output change curve generated based on the measured power generation data of the target new energy station. This curve is formed by mapping the actual active power data of the power generation unit into the coordinate system of the three-dimensional simulation interface in time series, and truly reflects the changes in the power generation performance of the equipment during operation.

[0065] Specifically, in order to achieve comparative analysis between theoretical power generation performance and actual power generation, it is necessary to visualize the measured power generation data of the station. The system receives the actual power output data of each power generation unit in the target new energy station in real time through data acquisition equipment. The data content includes electrical parameters such as active power, reactive power, voltage, and current. The received power generation data is first preprocessed, including outlier filtering, data padding, and timestamp alignment to ensure the continuity and reliability of the data. Subsequently, the processed power generation data is mapped according to the same coordinate system rules as the first power generation simulation curve to generate a second power generation simulation curve that represents the actual power generation performance. For solar photovoltaic modules, the second power generation simulation curve reflects the various losses and efficiency changes in the actual photoelectric conversion process; for wind turbines, it reflects the power output characteristics during actual operation. In the three-dimensional simulation interface, the second power generation simulation curve and the first power generation simulation curve use different display styles, which is convenient for intuitively comparing the difference between theoretical values ​​and measured values. Through this hyperbolic comparison visualization method, operation and maintenance personnel can quickly discover power generation performance anomalies, evaluate power generation efficiency losses, and formulate targeted optimization measures in a timely manner.

[0066] Based on the above embodiment, as an optional embodiment, in step 103: generating a second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface, this step may also include the following steps: Step 301: segment the power generation data according to preset time intervals to obtain data samples of multiple time windows; based on the data samples of each time window, determine the cumulative value of the power generation in each time window.

[0067] Specifically, in order to achieve a refined evaluation of power generation performance, it is necessary to divide and statistically analyze the continuous power generation data in time scales. The system first sets a suitable time interval according to the performance evaluation requirements, such as 15 minutes, 1 hour or 24 hours, and processes the continuously collected power generation data in segments according to this time interval to form multiple time windows with fixed durations. In each time window, the active power data of the power generation unit is time-integrated, and the power value is converted into an electrical value to obtain the cumulative value of power generation in the period. For solar photovoltaic components, this segmented statistical method can reflect the distribution characteristics of power generation in different periods, which is convenient for analyzing the impact of sunshine conditions on power generation efficiency; for wind turbines, it helps to evaluate the power generation performance under different wind conditions. During the data processing process, the system will perform integrity checks on the data samples in each time window, and use interpolation or marking processing for periods with missing data to ensure the accuracy of the statistical results. Through this cumulative statistical method based on time windows, the continuous power generation process can be converted into discrete evaluation indicators, providing more detailed data support for subsequent performance evaluation and optimization.

[0068] Step 302: Calculate the actual power output value of each time window based on the increment of the accumulated value between each time window and an adjacent time window and a preset time interval.

[0069] Specifically, in order to accurately evaluate the actual power generation performance of the power generation unit in different time windows, it is necessary to convert the cumulative power generation into power output characteristics. The system first calculates the difference between the cumulative values ​​of power generation between adjacent time windows, that is, the power generation increment, which reflects the actual power generation of the power generation unit in the current time window. Subsequently, this power generation increment is divided by the preset time interval (the time unit is uniformly converted to hours) to obtain the average actual power output value of the time window. For solar photovoltaic components, this incremental-based power calculation method can eliminate the influence of the cumulative effect and more accurately reflect the actual power generation capacity of each time period; for wind turbines, it can reflect the average power generation level in different time periods, which is convenient for comparison with the theoretical power output value. During the calculation process, the system will verify the validity of the data, eliminate abnormal incremental values ​​caused by factors such as equipment start-up and shutdown, and failure, and ensure the reliability of the calculation results. Through this incremental analysis method, discrete power generation statistics can be converted into continuous power change characteristics, providing a more intuitive measurement indicator for power generation efficiency evaluation. By analyzing the changing trend of the actual power output values ​​in different time windows, it is also possible to identify equipment performance degradation or signs of failure, providing a basis for predictive maintenance.

[0070] Step 303: Map each actual power output value to the coordinate system of the three-dimensional simulation interface to generate a second power generation simulation curve.

[0071] Specifically, in order to realize the visualization expression of actual power generation performance, it is necessary to convert the calculated actual power output value into an intuitive graphical display. The system first maps these discrete data points into the coordinate system in the three-dimensional simulation interface, taking the central moment of the time window as the horizontal coordinate value and the corresponding actual power output value as the vertical coordinate value. Subsequently, the cubic spline interpolation algorithm is used to smoothly connect these discrete data points to form a continuous second power generation simulation curve, which truly reflects the power output change characteristics of the power generation unit during actual operation. For solar photovoltaic modules, the second power generation simulation curve usually shows obvious daily cycle fluctuation characteristics, and the fluctuation amplitude is significantly affected by weather conditions; for wind turbines, the curve shape is mainly determined by wind conditions and unit control strategies. In the three-dimensional simulation interface, the second power generation simulation curve is distinguished from the first power generation simulation curve by different colors and line types, which is convenient for intuitive comparison of the difference between theoretical expectations and actual operation. Through this curve visualization method, operation and maintenance personnel can quickly identify power generation performance anomalies and evaluate actual power generation efficiency.

[0072] Step 104: Combine the first power generation simulation curve and the second power generation simulation curve to generate a production ratio simulation evaluation curve of the target new energy station, and display the production ratio simulation evaluation curve on a three-dimensional simulation interface.

[0073] Among them, the production ratio simulation evaluation curve refers to a performance evaluation curve drawn in time series by calculating the ratio of actual power output value to theoretical power output value. The vertical axis value of the curve represents the production ratio value of the power generation unit at each time point, reflecting the degree of conformity between the actual power generation efficiency and the theoretical expectation.

[0074] Specifically, the system first pairs the power values ​​of the first power generation simulation curve and the second power generation simulation curve at the same time point, calculates the ratio of the actual power output value to the theoretical power output value, and obtains the production ratio data reflecting the power generation efficiency. During the calculation process, the system verifies the validity of the power value, eliminates abnormal data caused by equipment failure or external interference, and ensures the accuracy of the production ratio calculation results. Subsequently, the calculated production ratio data is mapped to the coordinate system of the three-dimensional simulation interface in time series, and a continuous production ratio simulation evaluation curve is generated using a smooth interpolation method. For solar photovoltaic components, the curve reflects the deviation between the actual photoelectric conversion efficiency and the theoretical efficiency, which can be used to evaluate the performance attenuation and maintenance effect of the components; for wind turbines, it reflects the impact of the actual operation strategy on the power generation efficiency. The system sets a standard reference interval for the production ratio in the three-dimensional simulation interface, and uses different color markings when the curve falls into different intervals, so that the operation and maintenance personnel can quickly judge the operating status of the equipment. Through this visual analysis method of the production ratio, the power generation efficiency loss can be accurately quantified, the performance optimization space can be identified, and data support can be provided for operation and maintenance decisions.

[0075] Based on the above embodiment, as an optional embodiment, in step 104: combining the first power generation simulation curve and the second power generation simulation curve to generate a target new energy station production ratio simulation evaluation curve, this step may also include the following steps: Step 401: extracting theoretical power peak points of the first power generation simulation curve in multiple sampling periods and actual power peak points of the second power generation simulation curve in each sampling period.

[0076] Specifically, in order to evaluate the performance of the power generation unit under the optimal working conditions, it is necessary to extract and analyze the peak characteristics of the power output. The system first sets a reasonable sampling period according to the power generation characteristics. For example, for solar photovoltaic components, the period with the strongest sunshine is usually selected, and for wind turbines, the period with the best wind conditions is selected. In each sampling period, the system uses a peak detection algorithm to scan the first power generation simulation curve and the second power generation simulation curve respectively to identify the theoretical power peak point and the actual power peak point. To ensure the accuracy of peak detection, the system sets dynamic thresholds and time window constraints to avoid misjudging short-term fluctuations as peak points. For solar photovoltaic components, the peak point usually occurs around noon, reflecting the maximum photoelectric conversion efficiency; for wind turbines, the peak point corresponds to the period when the wind speed reaches the rated value. During the peak extraction process, the system preprocesses the data, including noise filtering and outlier removal, to ensure the accuracy of the extraction results. Through this peak-based analysis method, we can focus on the maximum power generation capacity of the power generation unit and evaluate the performance differences of the equipment under the optimal working conditions.

[0077] Step 402: Calculate the time offset and amplitude offset between the theoretical power peak point and the actual power peak point in each sampling period.

[0078] Specifically, in order to accurately quantify the actual performance deviation of the power generation unit, it is necessary to perform a multi-dimensional analysis of the difference between the theoretical and actual power peak points. The system first calculates the time offset of the actual power peak point relative to the theoretical power peak point in each sampling period, and the offset reflects the timing difference of the power generation response. For solar photovoltaic components, the time offset may be caused by factors such as component orientation deviation and shadow shielding; for wind turbines, it may be caused by the influence of the unit startup characteristics and control strategy. At the same time, the system calculates the power amplitude offset between the two peak points, which directly reflects the degree of loss of power generation capacity. During the calculation process, the system uses a standardized processing method to convert the amplitude offset into a percentage relative to the theoretical peak value, which is convenient for comparison between devices of different capacities. To improve the calculation accuracy, the system interpolates and fits the data around the peak point to ensure the continuity and reliability of the offset. Through this two-dimensional offset analysis, the performance anomaly of the power generation unit can be comprehensively evaluated and the specific manifestations of efficiency loss can be identified.

[0079] Step 403: Determine the production ratio coefficient of each sampling period based on the time offset and amplitude offset in each sampling period; perform time series superposition on the production ratio coefficient of each sampling period to obtain a production ratio simulation evaluation curve.

[0080] Specifically, the production ratio coefficient refers to a performance evaluation index calculated based on the time offset and amplitude offset. The coefficient is obtained by weighted fusion of the time response difference and power output difference between the theoretical power peak point and the actual power peak point, reflecting the comprehensive performance level of the power generation unit in a specific sampling period.

[0081] Specifically, in order to comprehensively transform the multi-dimensional performance deviation into a unified evaluation index, it is necessary to establish a production ratio calculation model based on offset. Specifically, the system first sets the weight coefficients of the time offset and the amplitude offset according to the power generation characteristics, and obtains the production ratio coefficient of each sampling period through weighted fusion calculation. For solar photovoltaic modules, when the time offset is large, it indicates that there is a misalignment in the power generation timing, which will reduce the production ratio coefficient; when the amplitude offset is significant, it directly reflects the loss of power generation efficiency. For wind turbines, the system will dynamically adjust the weight configuration according to the characteristics of the wind conditions to more accurately evaluate the performance deviation. After the production ratio coefficient is calculated, the system combines the coefficients of each sampling period in chronological order, uses the cubic spline interpolation algorithm to smooth the discrete production ratio coefficient, and generates a continuous production ratio simulation evaluation curve. To ensure the reliability of the evaluation results, the system sets the effective value range of the production ratio coefficient and corrects or marks the abnormal values. Through this comprehensive evaluation method based on multi-dimensional offset, a continuous evaluation curve reflecting the overall performance level of the power generation unit can be obtained.

[0082] Based on the above embodiment, as an optional embodiment, in step 403: determining the production ratio coefficient of each sampling period based on the time offset and amplitude offset in each sampling period, this step may also include the following steps: Step 413: Divide the time offset in each sampling period by the standard time interval to obtain a corresponding time offset coefficient; divide the amplitude offset in each sampling period by the theoretical power peak value to obtain a corresponding amplitude offset coefficient.

[0083] Specifically, in order to eliminate the influence of different time scales and power levels on the evaluation results, it is necessary to standardize the time offset and amplitude offset. The system first sets a standard time interval based on the power generation characteristics. For solar photovoltaic components, the duration of the peak power generation period in a day is usually selected, and for wind turbines, the characteristic period of wind condition changes is considered. The time offset in each sampling period is divided by this standard time interval to obtain a dimensionless time offset coefficient, which reflects the degree of power generation response delay relative to the reference time. At the same time, the system divides the amplitude offset in each sampling period by the theoretical power peak value of the corresponding period to obtain the amplitude offset coefficient, which characterizes the ratio of power loss to theoretical power generation capacity. Through this standardized processing, the offset characteristics of different dimensions are converted into comparable relative quantities, which is convenient for subsequent comprehensive evaluation. For solar photovoltaic components, the standardized offset coefficient can better reflect the change law of component performance over time; for wind turbines, it helps to evaluate the power generation efficiency under different wind conditions. In particular, this standardized processing method can also be used for performance comparison between different types of power generation units, providing a unified evaluation benchmark for the overall optimization of the site.

[0084] Step 423: Perform weighted summation on the time offset coefficient and the amplitude offset coefficient of each sampling period to obtain the corresponding production ratio coefficient.

[0085] Specifically, in order to reasonably integrate the performance deviations of the two dimensions of time and amplitude into a single evaluation index, a comprehensive calculation model based on weights needs to be established. The system first sets the weight factors of the time offset coefficient and the amplitude offset coefficient according to the characteristics and operation requirements of the power generation unit. For solar photovoltaic modules, the weight of the time offset coefficient is usually higher because the power generation efficiency is closely related to the sunshine timing; for wind turbines, considering the direct reflection of power output on wind energy utilization efficiency, the weight of the amplitude offset coefficient may be greater. In each sampling period, the system multiplies the time offset coefficient by its corresponding weight, multiplies the amplitude offset coefficient by its corresponding weight, and then adds the two weighted results to obtain the commissioning ratio coefficient of the period. To ensure the rationality of the calculation results, the system will dynamically optimize the weight configuration and adjust the weight ratio in time according to environmental conditions and operating status. Through this weighted fusion method, a comprehensive evaluation index that fully reflects the power generation performance can be obtained. In particular, the calculation process of the commissioning ratio coefficient also takes into account the nonlinear characteristics of the influence of different types of deviations on the power generation efficiency, and uses correction coefficients to calibrate the calculation results to improve the accuracy of the evaluation.

[0086] It should be noted that among the new energy stations, wind farms are important power generation stations. The production efficiency of wind farms is not only affected by factors such as the layout of generator sets and environmental parameters, but also by micro-site selection and machine position positioning. Reasonable micro-site selection helps to utilize high-quality wind sources, while appropriate machine position positioning can reduce the array wind and light attenuation effect, thereby maximizing the power generation potential of wind farms.

[0087] The evaluation method of wind farms includes: first, obtaining environmental parameters such as wind speed data in the area where the target wind farm is located, and collecting layout parameters such as the spatial coordinates and orientation angles of the wind turbines. Secondly, when constructing the three-dimensional simulation interface of the wind farm based on the layout parameters of the wind turbines, it is necessary to determine the spatial position coordinates, orientation angles and unit spacing of each wind turbine, and calculate the shielding influence coefficient of each unit based on these parameters, so as to adjust the final layout position of the unit.

[0088] In the specific steps of calculating the shading influence coefficient, it is necessary to obtain wind direction data at multiple standard time points and generate a wind trajectory curve based on the wind direction; then, according to the orientation angle and wind trajectory of each unit, determine the shadow length and projection direction at each time point, map it to the station plane coordinate system, and obtain the shadow overlap matrix; finally, based on the shadow overlap matrix, determine the cumulative shading time of each unit, and find the corresponding shading influence coefficient in the preset mapping table. Through this series of calculations, the layout of wind turbines can be reasonably optimized to avoid excessive array shadow shading.

[0089] On the other hand, in addition to using layout parameters to generate the theoretical power curve of the wind farm, this method will also deploy a certain number of wind towers and other sensors to collect actual wind speed data. Using these measured data, combined with the power curve, the actual power output of the wind farm can be accurately predicted, and the actual power generation simulation curve of the wind farm can be generated.

[0090] Finally, by comparing the theoretical and actual power generation curves of the wind farm, calculating the time and power offset, obtaining evaluation parameters such as the production ratio, and visualizing them in a three-dimensional simulation scene, it provides an intuitive decision-making basis for the operation and maintenance optimization of the wind farm.

[0091] This method integrates multiple factors such as wind farm micro-site selection, machine layout optimization, measured wind speed data, power prediction, etc., and can more accurately and comprehensively evaluate the actual commissioning performance of wind farms. Not only does it improve the accuracy and credibility of the evaluation, but the intuitive three-dimensional visualization interface is also conducive to program analysis and optimization decision-making, and can provide strong technical support for the efficient planning, construction and refined operation and maintenance of wind farms.

[0092] Reference Figure 2, a new energy station simulation evaluation system based on multi-data fusion is provided in an embodiment of the present application, the system comprises: a parameter acquisition module, a first power generation simulation curve determination module, a second power generation simulation curve determination module, and a production ratio simulation evaluation module, wherein: A parameter acquisition module, used to acquire environmental parameters of the area where the target new energy station is located and layout parameters of multiple power generation units in the target new energy station; A first power generation simulation curve determination module is used to construct a three-dimensional simulation interface of a target new energy station according to the layout parameters of each power generation unit, and generate a first power generation simulation curve in the three-dimensional simulation interface according to the rated power and environmental parameters of each power generation unit; A second power generation simulation curve determination module, used to receive power generation data of a target new energy station and generate a second power generation simulation curve corresponding to the power generation data in a three-dimensional simulation interface; The production ratio simulation evaluation module is used to combine the first power generation simulation curve and the second power generation simulation curve to generate a production ratio simulation evaluation curve of the target new energy site, and display the production ratio simulation evaluation curve on a three-dimensional simulation interface.

[0093] On the basis of the above-mentioned embodiment, the first power generation simulation curve determination module is also used to determine the spatial coordinates, orientation angles and spacing between power generation units in each layout parameter; according to the spatial coordinates of each power generation unit, the corresponding initial layout position is determined, and based on the orientation angle of each power generation unit, the shielding influence coefficient of each power generation unit is calculated; in combination with the spacing between each power generation unit and each shielding influence coefficient, the initial layout position of each power generation unit is adjusted to obtain the corresponding target layout position; based on the target layout position of each power generation unit, a three-dimensional simulation interface of the target new energy site is generated.

[0094] On the basis of the above embodiments, the first power generation simulation curve determination module is also used to obtain the solar azimuth and altitude angles at multiple standard time points, and generate a solar trajectory curve based on the solar azimuth and altitude angles at each standard time point; determine the shadow length and projection direction of each power generation unit at each standard time point according to the orientation angle of each power generation unit and the solar trajectory curve; map the shadow length and projection direction of each power generation unit at each standard time point to the plane coordinate system of the target new energy site to obtain a shadow overlap matrix; based on the shadow overlap matrix, determine the cumulative shading time of each power generation unit, and determine the shading influence coefficient corresponding to each cumulative shading time in a preset coefficient mapping table.

[0095] On the basis of the above embodiments, the first power generation simulation curve determination module is also used to extract light intensity data, temperature data and wind speed data from the environmental parameters for each power generation unit, and determine the environmental impact factors corresponding to the light intensity data, temperature data and wind speed data according to a preset environmental parameter mapping table; correct the calibrated conversion efficiency of the power generation unit based on each environmental impact factor to obtain the target power conversion efficiency; multiply the rated power of each power generation unit by the corresponding target power conversion efficiency to obtain the theoretical power output value of each power generation unit; map the theoretical power output value of each power generation unit to the coordinate system of the three-dimensional simulation interface to generate a first power generation simulation curve.

[0096] On the basis of the above-mentioned embodiment, the second power generation simulation curve determination module is also used to segment the power generation data according to preset time intervals to obtain data samples of multiple time windows; based on the data samples of each time window, determine the cumulative value of the power generation in each time window; based on the increment of the cumulative value between each time window and the adjacent time window and the preset time interval, calculate the actual power output value of each time window; map each actual power output value to the coordinate system of the three-dimensional simulation interface to generate a second power generation simulation curve.

[0097] On the basis of the above-mentioned embodiment, the production ratio simulation evaluation module is also used to extract the theoretical power peak points of the first power generation simulation curve in multiple sampling time periods and the actual power peak points of the second power generation simulation curve in each sampling time period; calculate the time offset and amplitude offset between the theoretical power peak point and the actual power peak point in each sampling time period; determine the production ratio coefficient of each sampling time period based on the time offset and amplitude offset in each sampling time period; perform time series superposition on the production ratio coefficient of each sampling time period to obtain the production ratio simulation evaluation curve.

[0098] Based on the above embodiments, the production ratio simulation evaluation module is also used to divide the time offset in each sampling period by the standard time interval to obtain the corresponding time offset coefficient; divide the amplitude offset in each sampling period by the theoretical power peak to obtain the corresponding amplitude offset coefficient; and perform weighted summation of the time offset coefficient and amplitude offset coefficient of each sampling period to obtain the corresponding production ratio coefficient.

[0099] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0100] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0101] The communication bus 302 is used to realize the connection and communication between these components.

[0102] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0103] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0104] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0105] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a new energy station simulation evaluation method based on multi-data fusion.

[0106] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the memory 305 to store an application program for a new energy station simulation evaluation method based on multi-data fusion. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0107] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0108] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0109] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0111] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0112] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0113] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A simulation evaluation method for new energy stations based on multi-data fusion, characterized in that: include: Acquire environmental parameters of the area where the target new energy site is located and layout parameters of multiple power generation units in the target new energy site; According to the layout parameters of each of the power generation units, a three-dimensional simulation interface of the target new energy site is constructed, and according to the rated power of each of the power generation units and the environmental parameters, a first power generation simulation curve is generated in the three-dimensional simulation interface; Receiving power generation data of the target new energy station, and generating a second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface; The first power generation simulation curve and the second power generation simulation curve are combined to generate a production ratio simulation evaluation curve of the target new energy site, and the production ratio simulation evaluation curve is displayed on the three-dimensional simulation interface.

2. The new energy station simulation evaluation method based on multi-data fusion according to claim 1 is characterized in that: The step of constructing a three-dimensional simulation interface of the target new energy station according to the layout parameters of each of the power generation units includes: Determining the spatial coordinates, orientation angles and spacing between power generation units in each of the layout parameters; Determining the corresponding initial layout position according to the spatial coordinates of each power generation unit, and calculating the shielding influence coefficient of each power generation unit based on the orientation angle of each power generation unit; In combination with the spacing between the power generation units and the shielding influence coefficients, the initial layout position of each power generation unit is adjusted to obtain a corresponding target layout position; Based on the target layout position of each of the power generation units, a three-dimensional simulation interface of the target new energy station is generated.

3. The new energy station simulation evaluation method based on multi-data fusion according to claim 2 is characterized in that: The calculating the shielding influence coefficient of each power generation unit based on the orientation angle of each power generation unit includes: Acquire the solar azimuth and altitude angles at a plurality of standard time points, and generate a solar trajectory curve based on the solar azimuth and altitude angles at each of the standard time points; Determine the shadow length and projection direction of each power generation unit at each standard time point according to the orientation angle of each power generation unit and the sun trajectory curve; Mapping the shadow length and projection direction of each power generation unit at each standard time point to the plane coordinate system of the target new energy site to obtain a shadow overlap matrix; Based on the shadow overlap matrix, the accumulated shading duration of each power generation unit is determined, and the shading influence coefficient corresponding to each accumulated shading duration is determined in a preset coefficient mapping table.

4. The new energy station simulation evaluation method based on multi-data fusion according to claim 1 is characterized in that: Generating a first power generation simulation curve in the three-dimensional simulation interface according to the rated power of each power generation unit and the environmental parameters includes: For each of the power generation units, light intensity data, temperature data and wind speed data are extracted from the environmental parameters, and environmental impact factors corresponding to the light intensity data, the temperature data and the wind speed data are respectively determined according to a preset environmental parameter mapping table; Based on each of the environmental impact factors, the calibrated conversion efficiency of the power generation unit is corrected to obtain a target power conversion efficiency; Multiplying the rated power of each power generation unit by the corresponding target power conversion efficiency to obtain a theoretical power output value of each power generation unit; The theoretical power output value of each of the power generation units is mapped to the coordinate system of the three-dimensional simulation interface to generate a first power generation simulation curve.

5. The new energy station simulation evaluation method based on multi-data fusion according to claim 1 is characterized in that: The generating a second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface includes: Segmenting the power generation data according to preset time intervals to obtain data samples of multiple time windows; Determining a cumulative value of power generation within each of the time windows based on the data samples in each of the time windows; Calculating the actual power output value of each time window based on the increment of the accumulated value between each time window and an adjacent time window and the preset time interval; Each of the actual power output values ​​is mapped to the coordinate system of the three-dimensional simulation interface to generate a second power generation simulation curve.

6. The new energy station simulation evaluation method based on multi-data fusion according to claim 1 is characterized in that: The combining the first power generation simulation curve and the second power generation simulation curve to generate a production ratio simulation evaluation curve of the target new energy station includes: Extracting theoretical power peak points of the first power generation simulation curve in multiple sampling periods and actual power peak points of the second power generation simulation curve in each of the sampling periods; Calculating the time offset and amplitude offset between the theoretical power peak point and the actual power peak point in each of the sampling time periods; Determining the production ratio coefficient of each sampling period based on the time offset and the amplitude offset in each sampling period; The production ratio coefficients of each sampling period are superimposed in time series to obtain a production ratio simulation evaluation curve.

7. The new energy station simulation evaluation method based on multi-data fusion according to claim 6 is characterized in that: Determining the production ratio coefficient of each sampling period based on the time offset and the amplitude offset in each sampling period includes: Dividing the time offset in each sampling period by the standard time interval to obtain a corresponding time offset coefficient; Dividing the amplitude offset in each sampling period by the theoretical power peak value to obtain a corresponding amplitude offset coefficient; The time offset coefficient and the amplitude offset coefficient of each sampling period are weighted and summed to obtain the corresponding production ratio coefficient.

8. A new energy station simulation evaluation system based on multi-data fusion, characterized in that: The system comprises: A parameter acquisition module, used to acquire environmental parameters of the area where the target new energy station is located and layout parameters of multiple power generation units in the target new energy station; A first power generation simulation curve determination module, used to construct a three-dimensional simulation interface of the target new energy site according to the layout parameters of each power generation unit, and generate a first power generation simulation curve in the three-dimensional simulation interface according to the rated power of each power generation unit and the environmental parameters; a second power generation simulation curve determination module, configured to receive power generation data of the target new energy station and generate a second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface; The production ratio simulation evaluation module is used to combine the first power generation simulation curve and the second power generation simulation curve to generate a production ratio simulation evaluation curve of the target new energy site, and display the production ratio simulation evaluation curve on the three-dimensional simulation interface.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the new energy station simulation evaluation method based on multi-data fusion as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the new energy station simulation evaluation method based on multi-data fusion as described in any one of claims 1 to 7 is executed.

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