Wind farm increased generation closed-loop control method and system based on three-dimensional reconstruction of wind turbine wake

Abstract

This invention provides a closed-loop control method and system for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake, relating to the technical field of intelligent wind farm control. The method includes: firstly, extracting wake characteristic parameters based on the three-dimensional reconstruction results of the wind turbine wake, and calculating the equivalent incoming wind speed in the rotor plane of the downstream turbine; inputting the equivalent incoming wind speed and the operating status of each wind turbine into a power generation prediction model to obtain the corresponding wind farm power generation prediction results; determining target control decision data with the goal of maximizing the wind farm power generation prediction results and constrained by turbine operating safety parameters, and generating corresponding wind turbine wake control commands. This solves the technical problems of low wind turbine wake sensing accuracy and poor power generation enhancement control stability, achieving an overall increase in wind farm power generation and ensuring the stability of power generation enhancement control.

Description

Technical Field

[0001] This invention relates to the technical field of intelligent control of wind farms, and more specifically, to a closed-loop control method and system for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake. Background Technology

[0002] The wake effect of wind farms can lead to problems such as decreased incoming wind speed, increased turbulence intensity, and reduced power output in downstream turbines. Currently, mainstream wake assessment methods mainly rely on empirical models or offline numerical simulations. These methods are difficult to accurately characterize the real wake evolution characteristics under complex terrain conditions and time-varying atmospheric stability. On the other hand, methods that rely solely on Supervisory Control and Data Acquisition (SCADA) data to inversely extrapolate the wake have inherent limitations in terms of insufficient observation dimensions and spatial resolution.

[0003] Existing research confirms that lidar technology can achieve real-time measurement and dynamic tracking of wind turbine wakes and support wind speed field reconstruction. Some studies have also demonstrated the feasibility of integrating this technology with control strategies. However, in practical engineering applications, many technical bottlenecks remain, including limited measurement resource allocation, significant fluctuations in increased power generation revenue, and insufficient stability of the control system. Current technology systems suffer from technical deficiencies such as insufficient accuracy in wind turbine wake sensing and poor stability in power generation control. Summary of the Invention

[0004] The purpose of this invention is to provide a closed-loop control method and system for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake, so as to solve the technical problems of low wind turbine wake sensing accuracy and poor power generation control stability in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a closed-loop control method for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake. The method includes: extracting wake characteristic parameters based on the three-dimensional reconstruction results of the wind turbine wake, and calculating the equivalent incoming wind speed in the rotor plane of the downstream unit; inputting the equivalent incoming wind speed and the operating status of each wind turbine into a power generation prediction model to obtain the corresponding wind farm power generation prediction results; determining target control decision data with the goal of maximizing the wind farm power generation prediction results and with unit operating safety parameters as constraints, and generating corresponding wind turbine wake control commands.

[0006] In some optional implementations, the methods for obtaining the three-dimensional reconstruction results of the wind turbine wake include: performing three-dimensional reconstruction of the wind turbine wake based on radial wind speed observation data obtained by wind-measuring lidar to obtain three-dimensional wind field data of the wind turbine wake region and corresponding reconstruction evaluation indicators; the three-dimensional wind field data includes the wind speed vectors at each spatial location in the wind turbine wake region.

[0007] In some optional implementations, wake characteristic parameters are extracted, including: analyzing and calculating the wind speed vectors at each spatial location in the above three-dimensional wind field data to obtain wake characteristic parameters used to characterize the wake spatial structure; wherein the wake characteristic parameters include: wake centerline position, wake velocity deficit distribution and / or wake deflection characteristics.

[0008] In some optional implementations, the equivalent incoming wind speed in the downstream unit rotor plane is calculated by: calculating the axial wind speed component within the swept area of ​​the downstream unit rotor based on the aforementioned wake characteristic parameters; and performing area averaging, weighted averaging, or power equivalence calculations on the aforementioned axial wind speed component to obtain the equivalent incoming wind speed.

[0009] In some optional implementations, the above-mentioned target control decision data is generated in the following ways: based on the above-mentioned wind farm power generation prediction results, and with the unit operation safety boundary as a constraint, the target control parameters corresponding to maximizing the overall power generation of the wind farm are determined; based on the above-mentioned target control parameters, the cooperative yaw correction amount of the upstream units is determined; based on the above-mentioned cooperative yaw correction amount, the target control decision data is determined to adjust the operating status of the upstream wind turbines; the above-mentioned target control decision data includes at least two of the above-mentioned cooperative yaw correction amounts.

[0010] In some optional implementations, the above method further includes: dynamically adjusting the target control decision data according to the above reconstruction evaluation index; if the above reconstruction evaluation index does not meet the preset threshold, reducing the weight coefficient of the equivalent incoming wind speed when generating the above target control decision data / in the above power generation prediction model, or outputting control command data based on historical statistical data.

[0011] In some optional implementations, the above-mentioned reconstruction evaluation metrics include reconstruction confidence and / or reconstruction uncertainty; the above-mentioned reconstruction confidence is used to evaluate the wake three-dimensional wind field reconstruction model; the above-mentioned wake three-dimensional wind field reconstruction model is constructed based on the radial wind speed observation data acquired by the above-mentioned wind-measuring lidar, and is used to realize the three-dimensional reconstruction of the wind turbine wake; the above-mentioned reconstruction uncertainty is determined based on the signal-to-noise ratio and / or effective sampling rate of the above-mentioned radial wind speed observation data, and is used to evaluate the quality of the above-mentioned radial wind speed observation data.

[0012] Secondly, embodiments of the present invention provide a closed-loop control system for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake, comprising: a wake feature extraction module, used to extract wake feature parameters based on the three-dimensional reconstruction results of the wind turbine wake, and calculate the equivalent incoming wind speed in the rotor plane of the downstream unit; a power generation prediction module, used to input the above equivalent incoming wind speed and the operating status of each wind turbine into a power generation prediction model to obtain the corresponding wind farm power generation prediction results; and a control decision generation module, used to determine target control decision data with the goal of maximizing the above wind farm power generation prediction results and with unit operating safety parameters as constraints, and generate corresponding wind turbine wake control commands.

[0013] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the method described in any of the first aspects above.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method described in any of the first aspects above.

[0015] This invention provides a closed-loop control method and system for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake. The method includes: firstly, extracting wake characteristic parameters based on the three-dimensional reconstruction results of the wind turbine wake, and calculating the equivalent incoming wind speed in the rotor plane of the downstream unit; inputting the equivalent incoming wind speed and the operating status of each wind turbine into the power generation prediction model to obtain the corresponding wind farm power generation prediction results; determining the target control decision data with the goal of maximizing the wind farm power generation prediction results and the unit operating safety parameters as constraints, and generating the corresponding wind turbine wake control commands. This solves the technical problems of low wind turbine wake sensing accuracy and poor power generation enhancement control stability, and realizes high-reliability sensing-driven closed-loop power generation enhancement control. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a closed-loop control method for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake provided in an embodiment of the present invention; Figure 2A flowchart illustrating another closed-loop control method for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake provided in an embodiment of the present invention; Figure 3 A schematic diagram of a closed-loop control system for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake is provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The operational efficiency of wind farms is significantly affected by the dynamic evolution of wind fields in the atmospheric boundary layer. Among these, the downstream turbine flow distortion caused by the wake of a single turbine is one of the key physical mechanisms resulting in overall power loss in wind farms. In recent years, wind turbine condition monitoring and wind farm group collaborative control technologies have continued to develop, and wind measurement methods have gradually evolved from traditional cup anemometers to remote sensing and spatialization. LiDAR, as a non-contact wind farm detection device, has been applied in engineering projects such as wind resource assessment, turbine location optimization, and power prediction. Some studies have attempted to use it for wake observation, obtaining radial velocity information in a limited number of directions through line scanning or conical scanning, and combining simplified assumptions to invert local wind speed change trends. However, such applications are mostly in the experimental verification or offline analysis stage, and a technical path that can be embedded into the real-time control system of wind farms has not yet been formed. Limited by equipment performance, data processing capabilities, and system integration depth, the current role of lidar in wind power scenarios is still mainly positioned as an auxiliary sensing tool, rather than a reliable decision-making basis in the closed-loop control chain.

[0020] At the control level, existing wind farm collaborative control strategies mostly rely on numerical simulation models, SCADA historical statistics, or simplified wake empirical formulas (such as Jensen models and Ainslie models). Their input parameters are static and updated with lag, making it difficult to respond to real-world conditions such as sudden changes in atmospheric stability, wind direction shifts, and terrain disturbances. When meteorological conditions deteriorate (such as low signal-to-noise ratio, strong turbulence, and rain / fog attenuation), existing systems are prone to miscontrol risks due to input distortion, hindering the large-scale implementation of highly reliable power generation control in complex mountainous wind farms.

[0021] Based on this, the present invention provides a closed-loop control method and system for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake, in order to solve the technical problems of low wind turbine wake sensing accuracy and poor power generation control stability in the prior art.

[0022] To facilitate understanding of this embodiment, a detailed description of the closed-loop control method for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake, as disclosed in this embodiment of the invention, will be provided first. (See [link to relevant documentation]). Figure 1 The diagram shows a closed-loop control method for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake. This method can be executed by electronic equipment and mainly includes the following steps S102 to S106: Step S102: Based on the three-dimensional reconstruction results of the wind turbine wake, extract the wake characteristic parameters and calculate the equivalent incoming wind speed of the downstream unit rotor plane.

[0023] Based on the three-dimensional reconstruction results of the wind turbine wake, the three-dimensional wake field information can be extracted first, and then the equivalent incoming wind speed in the rotor plane of the downstream wind turbine can be calculated. That is, based on the three-dimensional reconstruction results of the wind turbine wake, wake characteristic parameters directly related to wind farm power generation can be further extracted. Preferably, the wake characteristic parameters include the equivalent incoming wind speed deficit in the rotor plane of the downstream unit, the wake influence intensity and its changing trend over time, and the equivalent incoming wind speed in the rotor plane of the downstream unit can be further calculated based on the above parameters.

[0024] In one embodiment, the method of obtaining the three-dimensional reconstruction result of the wind turbine wake may include: performing three-dimensional reconstruction of the wind turbine wake based on radial wind speed observation data obtained by wind-measuring lidar, to obtain three-dimensional wind field data of the wind turbine wake region and corresponding reconstruction evaluation indicators; the three-dimensional wind field data includes the wind speed vector at each spatial location in the wind turbine wake region.

[0025] Specifically, a three-dimensional scanning wind-measuring lidar can be used to spatially scan the wake region of the wind turbine to obtain radial wind speed observation data; the observation data is then processed to unify the coordinates and construct a dataset for wind field reconstruction; based on this dataset and combined with prior physical constraints of the wind field, the three-dimensional wind speed vector field in the wake region is inverted and solved to obtain the three-dimensional wind field data of the region to achieve wind turbine wake reconstruction.

[0026] Preferably, the radial wind speed measured by the three-dimensional scanning wind-measuring lidar is the projection of the actual wind speed onto the radar's line-of-sight direction, and its basic observation relationship can be expressed as: ; in, These are radar radial wind speed observations. The three-dimensional wind speed vector to be estimated is... This is the unit vector in the radar line-of-sight direction.

[0027] In one embodiment, acquiring radial wind speed observation data may include: generating adaptive scanning strategy data based on current ambient wind direction, ambient wind speed, and wind turbine operating status data; and controlling a three-dimensional scanning wind-measuring lidar to perform intensive scanning of the core sub-region of the wind turbine wake region according to the adaptive scanning strategy data. The adaptive scanning strategy can dynamically adjust the distribution of scanning azimuth, pitch angle, and range gate according to the current wind direction, wind speed, and turbine operating status to improve the observation accuracy of key wake regions.

[0028] After acquiring radial wind speed observation data using a three-dimensional scanning wind lidar, the data can be further quality controlled by removing abnormal observation points with insufficient signal-to-noise ratio or those affected by rain, fog, or clutter. The valid observation points are then uniformly converted to the wind farm coordinate system to form a radial wind speed observation dataset for wake reconstruction.

[0029] As a concrete example, the inversion solution of the three-dimensional wind speed vector field can include: first, constructing a wake three-dimensional wind field reconstruction model with the objective function of minimizing the radial wind speed observation residual based on the radial wind speed observation dataset; then, introducing wind field physical prior constraints into the wake three-dimensional wind field reconstruction model, solving the three-dimensional wind speed vector field, and completing the reconstruction.

[0030] Among them, the aforementioned wind field physical prior constraints may include fluid continuity constraints and / or spatial smoothness constraints.

[0031] A three-dimensional wind field reconstruction model of the wake is constructed within the wake's influence area. Radial wind speed observations are combined with wind field physical constraints to invert and solve for the three-dimensional wind speed distribution in the wake region. Preferably, the solution for the three-dimensional wake wind field aims to minimize the radial wind speed observation residuals, with the core constraint relationship being: ; Furthermore, by introducing wind field continuity constraints and / or spatial smoothness constraints, the physical rationality and stability of the reconstruction results in space are ensured.

[0032] Introducing prior constraints on wind field logistics (fluid continuity constraints and spatial smoothness constraints) can typically formalize the basic physical laws that the wind speed field in the atmospheric boundary layer should satisfy into mathematical constraints. Among them, the continuity constraint is reflected in the global or local restriction on the three-dimensional wind speed divergence, ensuring that the reconstructed wind field conforms to the principle of mass conservation; the smoothness constraint is reflected in the suppression of the spatial rate of change of wind speed, preventing non-physical drastic jumps or artifacts in the reconstruction results due to sparse radar observations or noise.

[0033] As a concrete example, fluid continuity constraints include time constraints based on fluid mass conservation or momentum conservation equations. Preferably, this time constraint can be a restriction that the divergence of the reconstructed three-dimensional wind speed vector field approaches zero on a spatially discrete grid, so as to satisfy the basic physical premise of mass conservation of incompressible fluids, thereby ensuring the continuity of the wake centerline and the smooth transition of velocity deficit, and avoiding non-physical solutions (such as sudden changes in local wind speed or closed vortices).

[0034] As another specific example, spatial smoothing constraints include spatial constraint terms used to limit the magnitude of wind speed variation between adjacent spatial points in a three-dimensional wind speed vector field. Preferably, these spatial constraint terms can be upper limits set on the wind speed difference between adjacent spatial grid points or the introduction of second-order difference penalty terms to suppress high-frequency oscillations in the reconstruction results caused by sparse radar observation points, signal-to-noise ratio fluctuations, or scanning blind spots, thereby maintaining spatial consistency in macroscopic characteristics such as wake expansion patterns and deflection trends.

[0035] In one embodiment, the method of extracting wake feature parameters based on the three-dimensional reconstruction results of the wind turbine wake may include: analyzing and calculating the wind speed vectors at each spatial location in the three-dimensional wind field data to obtain wake feature parameters used to characterize the wake spatial structure.

[0036] Preferably, the wake characteristic parameters include: wake centerline position, wake velocity deficit distribution and / or wake deflection characteristics.

[0037] Specifically, the position of the wake centerline can be obtained by identifying and connecting the spatial point sequence with the largest axial velocity deficit in the three-dimensional wind field data; the velocity deficit distribution can be calculated by comparing the wind speed at each spatial location in the three-dimensional wind field data with the free-flow wind speed; and the deflection characteristics can be obtained by comparing the angle between the wake centerline position and the free-flow wind direction.

[0038] As a specific example, calculating the equivalent incoming wind speed in the rotor plane of the downstream unit can include: calculating the axial wind speed component within the swept area of ​​the downstream unit rotor based on wake characteristic parameters; and performing area averaging, weighted averaging, or power equivalence calculations on the axial wind speed component to obtain the equivalent incoming wind speed.

[0039] Among them, the downstream unit rotor plane refers to the spatial plane defined with the hub center of the target unit as the origin and perpendicular to its main axis direction (i.e. the design incoming flow direction). Its normal is aligned with the rated yaw angle of the wind turbine and is dynamically updated with the real-time yaw angle. This plane has a definite spatial pose in the three-dimensional wind field reconstruction coordinate system and can be used to extract the wind speed vector distribution at the corresponding position in the wake three-dimensional wind field data.

[0040] The rotor swept area is a circular region within the rotor plane, centered on the hub center and with the radius of the wind turbine blades as the radius. This region is mapped to a set of discrete spatial points in the three-dimensional wind field data grid. The coordinates of each point are uniquely determined by the rotor plane equation and the grid topology, thus allowing the extraction of the axial wind speed component u at the corresponding location. z (x, y) (i.e., the projection component of the wind speed vector onto the rotor plane normal).

[0041] Furthermore, the equivalent incoming air velocity in the downstream unit rotor plane is obtained by averaging the axial air velocity within the rotor swept area, and its expression is as follows: ; Among them, U eq The equivalent incoming air velocity is represented by the rotor plane of the downstream unit; A represents the swept area of ​​the rotor of the downstream unit, which can be determined based on the fan blade radius (rotor radius R), A=πR². The axial wind speed within the rotor swept area can be obtained by performing coordinate projection and regional sampling on the three-dimensional wind field data obtained in the above steps under the constraints of the rotor plane space.

[0042] As a concrete example, the equivalent incoming wind speed can be obtained by weighted average calculation: the wind speed distribution in the rotor plane is weighted and integrated with the local power density corresponding to the axial wind speed at each spatial point, so as to more realistically reflect the impact of wake non-uniformity on actual power generation potential.

[0043] As a specific example, the method to obtain the rotor plane equivalent inflow wind speed through power equivalent calculation can be as follows: Substitute the axial wind speed distribution of each spatial point in the rotor swept surface in the reconstructed three-dimensional wind field into the wind turbine aerodynamic power model (such as the mapping relationship based on Bates theory and the measured power curve correction), and integrate to calculate the total power that can be theoretically obtained under this wind speed distribution; then, find a uniform wind speed so that the power output under the same model is equal to the aforementioned integration result. This uniform wind speed is the power equivalent rotor plane equivalent inflow wind speed.

[0044] Step S104: Input the equivalent incoming wind speed and the operating status of each wind turbine into the power generation prediction model to obtain the corresponding wind farm power generation prediction results.

[0045] The operating status of each wind turbine can include its current yaw angle, pitch angle, power or torque command, etc. The power generation prediction model can be a pre-constructed power generation prediction model under the wake effect based on the aerodynamic characteristics of the wind turbine, the wake superposition effect, and the dynamic response constraints of the unit. It can be used to evaluate the impact of different control strategies on the power generation performance of the wind farm group.

[0046] Preferably, the power generation prediction model can be a lightweight mechanism model that uses the equivalent incoming wind speed as the core input variable and couples the real-time operating status of each wind turbine (yaw angle, pitch angle, speed, etc.) with the weight of the wake space influence. Structurally, this model explicitly embeds the wake characteristic parameters extracted in S102. For example, the wake velocity deficit distribution is used to correct the effective wind speed gain coefficient of the downstream turbines, and the wake deflection characteristics are used to dynamically adjust the wake shielding relationship matrix between the upstream and downstream turbines, thereby achieving an interpretable mapping from the three-dimensional wake reconstruction result to the overall power response. The output of this model can include the predicted value of the total power generation of the wind farm within a future short-term window (e.g., 1-5 minutes), which can be directly used as the quantitative basis for optimizing the objective function in the subsequent step S106.

[0047] Step S106: With the goal of maximizing the predicted power generation of the wind farm and constrained by the unit's operating safety parameters, determine the target control decision data and generate the corresponding wind turbine wake control command.

[0048] In one embodiment, the method of generating target control decision data may include: determining the target control parameters corresponding to maximizing the overall power generation of the wind farm based on the wind farm power generation prediction results and the unit operation safety boundary as a constraint; determining the cooperative yaw correction amount of the upstream units based on the target control parameters; and determining the target control decision data based on the cooperative yaw correction amount to adjust the operating state of the upstream wind turbines; the target control decision data includes at least two cooperative yaw correction amounts.

[0049] Preferably, the unit's operating safety parameters include the yaw angle change rate, maximum yaw angle, and operating safety threshold of the wind turbine generator.

[0050] The goal of this step, based on the wind farm power generation prediction results, is to maximize the wind farm power generation prediction results. Specifically, the optimization objective is to increase the overall power generation of the wind farm. This can include: constructing an explicit or implicit functional relationship between the total power of the wind farm and the yaw angle of each upstream unit. This function uses the equivalent inflow wind speed of the rotor plane of each downstream unit output in step S102 as an intermediate variable. By quantifying the mitigation effect of wake deflection on the wind speed loss of downstream units, a calculable mapping from local yaw action to overall power generation gain is achieved.

[0051] The optimization process described above does not only pursue the maximum power of a single unit, but rather balances the wind catch-up losses caused by the yaw of the upstream unit with the power gain of the downstream unit due to the weakening of the wake, ultimately achieving a local optimum in the net power generation of the wind farm.

[0052] The operational safety boundaries of a wind turbine can include: absolute limits for yaw angle, yaw rate thresholds, maximum permissible yaw acceleration, and a yaw safety margin dynamically adjusted based on the current wind speed, as defined by the wind turbine manufacturer's technical specifications. For example, the yaw angle adjustment range can be automatically narrowed in high-wind-speed sections to prevent overloading of the yaw mechanism or tower resonance.

[0053] Among them, the above-mentioned constraints can be dynamically coupled to the current sensing state. Specifically, the upper limit of the yaw angle change amplitude and rate can be adjusted online based on the wind direction stability (such as wind direction standard deviation), turbulence intensity estimation value, and the degree of abrupt change in the extracted wake deflection trend fed back by the three-dimensional scanning wind lidar. For example, when the wind direction swing is detected to be intensified or the wake centerline is rapidly deflected, the yaw angle adjustment range is automatically narrowed and the change rate is reduced to avoid the control action lagging behind the actual wake evolution and causing oscillation.

[0054] As another specific example, the above method may also include: constructing an optimization model with the net increase in wind farm power generation as the objective function and dynamic safety constraints as the boundary of the feasible domain. In this model, wake characteristic parameters (such as wake centerline deflection angle and equivalent incoming wind speed loss rate of downstream unit rotor plane) are directly used as key coefficients affecting the gradient of the objective function, while the equivalent incoming wind speed of rotor plane is used as the core quantitative basis for evaluating the economy of yaw action (i.e., the downstream power gain brought by unit yaw angle); the optimal yaw angle set obtained by solving is converted into executable control commands or suggested values ​​after credibility verification.

[0055] This leads to a field-level optimization strategy dominated by yaw adjustment and coordinated response of multiple actuators: when the extracted wake characteristic parameters show that the downstream units are in the low to medium wind speed range and have a significant speed deficit, the pitch control command of the downstream units can be linked with the yaw command of the upstream units, and the aerodynamic efficiency under low wind speed can be improved by appropriately reducing the pitch angle to compensate for the inflow attenuation caused by the wake; when the wake reconstruction reliability is high and the trend of the equivalent inflow wind speed change is clear, power or torque commands can also be superimposed to perform feedforward power ramping or torque fine-tuning of the downstream units to smooth out short-term power fluctuations caused by wake deflection.

[0056] As a concrete example, the above steps, with the goal of maximizing the overall power generation of the wind farm, calculate the coordinated yaw correction of the upstream units based on the wake reconstruction results and the trend of equivalent incoming wind speed changes, guiding the wake deflection and reducing the impact of the wake on the downstream units. Preferably, the relationship between the total power generation of the wind farm and the equivalent incoming wind speed of each unit can be summarized as follows: ; Where P represents the total power generation of the wind farm, U eqThis represents the equivalent incoming wind speed in the rotor plane of the downstream unit; k can be a power conversion coefficient related to the individual characteristics of the wind turbine, such as the equivalent power gain coefficient of the unit, which can reflect its power response sensitivity under the current operating conditions.

[0057] Under the premise of meeting the constraints of the unit's yaw angle change rate, maximum yaw angle, and operational safety, the yaw control command or control recommendation value for each unit can be determined.

[0058] The above steps, by fusing extracted wake characteristic parameters with the equivalent incoming wind speed in the rotor plane, construct an optimization solution process with the objective of maximizing the net power generation increment of the wind farm, and embedding dynamic safety boundaries and wake reconstruction credibility weighting. This generates a coordinated yaw control decision for upstream wind turbines to proactively guide the impact of the wake space. Specifically, through appropriate and coordinated yaw of the upstream turbines, the wake centerline is controllably deflected, thereby reducing its coverage intensity on the rotor plane of key downstream turbines and increasing the equivalent incoming wind speed actually captured by the downstream turbines. In another embodiment, the above method may further include: dynamically adjusting the target control decision data according to reconstruction evaluation indicators. If the reconstruction evaluation indicators do not meet a preset threshold, the weight coefficient of the equivalent incoming wind speed in generating target control decision data / in the power generation prediction model is reduced, or control command data based on historical statistical data is output.

[0059] As a concrete example, reconstruction evaluation metrics include reconstruction reliability and / or reconstruction uncertainty. Reconstruction reliability is used to evaluate the wake 3D wind field reconstruction model, which is constructed based on radial wind speed observation data acquired by wind-measuring lidar to achieve 3D reconstruction of the wind turbine wake. Reconstruction uncertainty can be determined based on the signal-to-noise ratio and / or effective sampling rate of the radial wind speed observation data, and is used to evaluate the quality of the radial wind speed observation data.

[0060] Preferably, the reconstruction reliability can be obtained by comprehensively evaluating multiple inherent uncertainty parameters in the solution process of the wake 3D wind field reconstruction model, such as the standard deviation of observation residuals, the contribution rate of smoothing constraint terms, and the condition number of the Jacobian matrix. The reconstruction uncertainty can be obtained by comprehensively evaluating the number of effective sampling points of the observation data, the average signal-to-noise ratio (SNR), and the uniformity of coverage along the line of sight.

[0061] Specifically, when the reconstruction confidence level is lower than the first preset threshold, the weight coefficient of the wake characteristic parameters in generating target control decision data can be reduced; when the reconstruction confidence level is lower than the second preset threshold (less than the first preset threshold), the generation of target control decision data is stopped, and control command data of normal operation mode is output.

[0062] The first and second preset thresholds can be set comprehensively based on the actual operating experience of wind farms, the performance calibration data of lidar equipment, and the statistical results of wake reconstruction error under typical operating conditions.

[0063] Specifically, the first preset threshold can correspond to the critical state where the wake structure can still be identified but the spatial accuracy decreases (e.g., the radial observation point density drops to 60% of the design value, or the standard deviation of the inversion residual exceeds twice the mean). At this time, the system retains the cooperative control framework, only weakens the influence of wake characteristic parameters on the optimization objective, and instead enhances the dependence on historical statistical models or steady-state empirical formulas. The second preset threshold can correspond to a failure state where the physical structure of the wake is difficult to reliably identify (e.g., the effective signal-to-noise ratio is consistently below 15dB, or the observation coverage of key areas is zero). At this time, the system actively exits the collaborative boosting mode and switches to the wind turbine's normal independent operation strategy to avoid erroneous yaw due to input distortion.

[0064] Preferably, the reconstruction uncertainty can specifically include the quality and quantity of radial wind speed observation data. The quality of the observation data can be used to reflect the confidence level of single-point observations (such as signal-to-noise ratio SNR, echo intensity stability), while the quantity of observation data can be used to reflect the integrity of spatial coverage (such as the number of effective azimuth-pitch angle combinations, range gate sampling density in key wake regions). Together, they determine the credibility level of the reconstructed three-dimensional wind field in terms of structural morphology (such as wake centerline position) and dynamic characteristics (such as velocity deficit gradient).

[0065] When the quality of radial wind speed observation data decreases or the amount of data falls below the third preset threshold, it indicates that the current radar perception capability is limited, the reconstruction results have significant uncertainties, and the wake characteristic parameters extracted from them may be distorted. If they are still used as the main driver for the collaborative optimization in step S104, the yaw command will deviate from the actual wake evolution direction, and may even cause power fluctuations or unit malfunctions. This will trigger the weight decay of the wake characteristic parameters in the optimization model, making the decision more reliant on the more robust historical statistical model or steady-state empirical relationship, and ensuring the safety and continuity of the control output under the condition of perception degradation.

[0066] Preferably, the third preset threshold can be determined comprehensively based on the factory calibration parameters of the lidar equipment, the long-term observation statistics under typical meteorological conditions of the wind farm, and the empirical sensitivity analysis of the inversion solution to the minimum number of observable points. For example, it can be set that the number of effective radial wind speed observation points is not less than 40% of the total number of theoretical grid points in the wake core area. This ratio can not only ensure the basic spatial structure identification capability, but also reserve a reasonable margin for observation fluctuations under complex weather conditions (such as light fog and weak turbulence).

[0067] In another embodiment, steps S102 to S106 can be repeated according to a preset control cycle, and wake reconstruction and control decisions can be updated in real time based on the latest radar observation results. When the radar data quality is insufficient or the credibility of the wake reconstruction result is lower than the threshold, the weight of the wake reconstruction result in the control decision is automatically reduced, or the collaborative boosting control mode is exited and switched to the normal operation strategy to ensure the safety and control stability of the wind turbine operation.

[0068] Based on this, the wind farm power generation enhancement closed-loop control method based on three-dimensional reconstruction of wind turbine wake provided by the embodiments of the present invention includes: extracting wake feature parameters based on the three-dimensional reconstruction results of wind turbine wake, and calculating the equivalent incoming wind speed in the rotor plane of the downstream unit; inputting the equivalent incoming wind speed and the operating status of each wind turbine into the power generation prediction model to obtain the corresponding wind farm power generation prediction results; determining the target control decision data with the goal of maximizing the wind farm power generation prediction results and the unit operating safety parameters as constraints, and generating the corresponding wind turbine wake control command. This solves the problems of low wake perception accuracy and poor power generation enhancement control stability in the prior art, and realizes high-reliability perception-driven closed-loop power generation enhancement control.

[0069] To overcome the problems of existing technologies, such as reliance on empirical models for wind turbine wakes, insufficient spatial resolution, difficulty in reflecting the true evolution characteristics of wakes online, and difficulty in directly using wake information for wind farm collaborative power generation enhancement control, this invention aims to provide a closed-loop control method for power generation enhancement based on three-dimensional reconstruction of wind turbine wakes. This method achieves: near real-time three-dimensional reconstruction of the spatial structure, velocity deficit, and deflection characteristics of wind turbine wakes to improve wake perception accuracy; direct conversion of the reconstructed wake information into wind farm collaborative yaw optimization control variables to achieve overall power generation enhancement of the wind farm; and under complex meteorological and observational conditions, ensuring the stability and engineering availability of power generation enhancement control through uncertainty assessment and control degradation strategies.

[0070] To facilitate understanding, this invention also provides an application example of a closed-loop control method for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake. By integrating wake space reconstruction, key wake index extraction, and farm-group coordinated yaw optimization into a single control closed loop, stable power generation enhancement of the wind farm is achieved under complex inflow conditions. See also Figure 2 The diagram shows another closed-loop control method for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake. This method mainly includes the following steps S201 to S208: Step S201: Perform wake space scanning using a three-dimensional scanning wind-measuring lidar; A three-dimensional scanning lidar was used to spatially scan the wake region downstream of the wind turbine, acquiring radial wind speed observation data at multiple spatial locations within the wake region.

[0071] Step S202: Data quality control and coordinate unification; The radial wind speed observation data were subjected to quality control and coordinate unification to construct a radial wind speed observation dataset for wake reconstruction.

[0072] This method first uses a three-dimensional scanning wind lidar to perform spatial scanning measurements on the downstream wake region of the wind turbine, obtaining radial wind speed observation data at different azimuth angles, pitch angles, and distance gates. Then, it combines indicators such as echo signal-to-noise ratio and effective sampling rate to perform quality control and screening of the raw data.

[0073] Based on this, an adaptive wake observation scanning strategy is generated according to the current wind direction, wind speed and unit operating status, and the core area and shear layer area of ​​the wake are scanned in a focused and intensified manner, thereby improving the observation accuracy of key areas of the wake within a limited scanning time.

[0074] Step S203: Reconstruct the three-dimensional wind field of the wake; Based on the radial wind speed observation data and combined with the physical constraints of the wind field, the three-dimensional wind speed distribution in the wake region is inverted and solved to obtain the three-dimensional wind field of the wake.

[0075] Preprocessed radial wind speed observation data are uniformly mapped to the wind farm coordinate system, and a three-dimensional wind field reconstruction model of the wake is constructed by combining the wake physics prior model and continuity constraints. By introducing radial observation constraints, spatial smoothing constraints, and wake structure prior constraints, the three-dimensional wind speed vector field in the wake region is inverted and solved to obtain three-dimensional wake field information, including the wake centerline position, wake velocity deficit distribution, wake expansion characteristics, and deflection angle, and the corresponding uncertainty or confidence index is output simultaneously.

[0076] Step S204: Extract wake feature parameters; From the three-dimensional wake field, wake characteristic parameters such as wake centerline, wake velocity deficit, and wake deflection characteristics are extracted.

[0077] Step S205: Calculate the equivalent incoming air velocity; Based on the three-dimensional reconstruction results of the wake, wake characteristic parameters directly related to wind farm power generation are further extracted, including the equivalent inflow wind speed deficit in the rotor plane of downstream units, the wake influence intensity, and its changing trend over time. These parameters are then used to construct a control input feature vector characterizing future short-term inflow conditions. Combining the current yaw angle, pitch angle, power or torque command, and other operating state variables of each wind turbine, a power generation prediction model under wake effects is established to evaluate the impact of different control strategies on the power generation performance of the wind farm cluster.

[0078] Step S206: Generate collaborative yaw boosting control decisions; With the overall power generation of the wind farm as the optimization goal and the unit operation safety boundary and wake reconstruction credibility as constraints, a wind turbine coordinated yaw power generation increase control decision is generated based on the wake characteristic parameters.

[0079] Step S207, output control instructions / suggestions; During the control decision-making phase, maximizing the overall power generation of the wind farm is used as the objective function. The yaw angle change rate of the turbines, the maximum yaw amplitude, the turbine operating safety boundary, and the wake reconstruction uncertainty threshold are used as constraints to obtain the coordinated yaw correction command or control recommendation value for the upstream turbines. By appropriately yawing the upstream turbines, the wake is guided to deflect, reducing the impact of the wake on the downstream turbines, thereby increasing the overall power generation of the wind farm.

[0080] Step S208, credibility assessment control downgrade; Based on the radar observation quality or wake reconstruction reliability, the additional launch control decision is adjusted or downgraded to generate a corresponding cooperative yaw additional launch control decision, as well as corresponding control commands or control suggestions.

[0081] In other words, embodiments of the present invention can also construct a reliability assessment and degradation strategy for augmentation control based on the observation quality and wake reconstruction uncertainty of the three-dimensional scanning wind lidar. When the radar echo quality deteriorates, the effective observation samples are insufficient, or the wake reconstruction uncertainty exceeds a preset threshold, the weight of the wake reconstruction result in the control decision is automatically reduced, and a control mode based on historical statistics or a conservative model is switched. When the observation conditions further deteriorate, the coordinated augmentation control is suspended, and only the unit's normal operation strategy is maintained, thereby avoiding the adverse effects of unreliable wake information on unit operation and ensuring the safety and engineering reliability of the system under complex environmental conditions.

[0082] Furthermore, without departing from the technical concept and effects of the embodiments of the present invention, the implementation methods of the present invention can be made in various equivalent ways. For example, the deployment location, number, and scanning method of the three-dimensional scanning wind-measuring lidar can be adjusted according to the wind farm layout and the prevailing wind direction; three-dimensional wake reconstruction can be achieved using different algorithms such as optimization inversion, filtering estimation, data assimilation, or machine learning; wake characteristic parameters and equivalent incoming wind speeds can be calculated using area averaging, weighted averaging, or power equivalence methods; the boosting control strategy can adopt rule-based control, rolling optimization, or predictive control, and output in the form of control commands or control suggestions. All of the above alternative methods can achieve equivalent technical effects of wake reconstruction and boosting control, and all fall within the protection scope of the present invention.

[0083] Through the above technical solution, this embodiment realizes closed-loop collaborative control of wake 3D perception, power generation decision, control execution and reliability assessment, providing an engineering-implementable technical path for stable power generation operation in complex terrain and densely arranged wind farms.

[0084] Based on the same inventive concept, this invention also provides a closed-loop control system for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake, see [link to relevant documentation]. Figure 3 As shown, the system mainly includes the following parts: The wake feature extraction module 310 is used to extract wake feature parameters based on the three-dimensional reconstruction results of the wind turbine wake, and calculate the equivalent incoming wind speed in the rotor plane of the downstream unit. The power generation prediction module 320 is used to input the equivalent incoming wind speed and the operating status of each wind turbine into the power generation prediction model to obtain the corresponding wind farm power generation prediction results. The control decision generation module 330 is used to determine target control decision data and generate corresponding wind turbine wake control commands with the goal of maximizing the predicted power generation of the wind farm and with the unit operation safety parameters as constraints.

[0085] In one embodiment, the target control decision data and corresponding control commands (i.e., coordinated yaw boosting control decisions) generated by the control decision generation module are yaw control as the core and can selectively link at least two types of actuators among pitch control, power control or torque control.

[0086] The aforementioned system can be implemented in the form of software, hardware, or a combination of both, and can be deployed on any of the following: a local server in the wind farm, an edge computing device, or a cloud platform.

[0087] As a concrete example, corresponding to the above Figure 2An application example of the method, this embodiment of the invention also provides a wind turbine wake three-dimensional reconstruction and wind farm power generation control system based on a three-dimensional scanning wind measurement lidar. Preferably, the system may include: (1) a three-dimensional scanning wind measurement lidar for acquiring radial wind speed observation data in the wind turbine wake region; (2) a data processing module for quality control and coordinate unification of the radial wind speed observation data; (3) a wake reconstruction module for reconstructing the wake three-dimensional wind field based on the radial wind speed observation data and combined with physical constraints; (4) a wake feature extraction module for extracting wake feature parameters from the wake three-dimensional wind field; and (5) equivalent incoming wind speed. The calculation module is used to calculate the equivalent incoming wind speed loss of the downstream unit rotor plane, the intensity of the wake influence and its changing trend over time based on the extracted wake characteristic parameters; (6) The control decision module is used to generate a coordinated yaw power generation control decision based on the wake characteristic parameters with the goal of increasing the overall power generation of the wind farm and with the unit operation safety boundary and wake reconstruction credibility as constraints; (7) The control command output module is used to generate and output the corresponding control command or control suggestion based on the coordinated yaw power generation control decision; (8) The credibility assessment and downgrade module is used to adjust or downgrade the control decision based on the observation quality or reconstruction credibility.

[0088] The wind farm power generation enhancement closed-loop control system based on three-dimensional reconstruction of wind turbine wake provided in this embodiment of the invention can be specific hardware on the equipment or software or firmware installed on the equipment. The system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiments. For the sake of brevity, any parts not mentioned in the system embodiments can be referred to the corresponding content in the aforementioned method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0089] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, specifically, the electronic device includes a processor and a storage device; the storage device stores a computer program, and the computer program, when run by the processor, executes the method described in any of the above embodiments.

[0090] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 400 includes: a processor 410, a memory 420, a communication interface 430, and a bus 440. The memory 420 stores machine-readable instructions that can be executed by the processor 410. When the electronic device is running, the processor 410 communicates with the memory 420 through the bus 440. The processor 410 executes the machine-readable instructions to perform the steps of the method described above.

[0091] Specifically, the memory 420 and processor 410 can be general-purpose memory and processor, without any specific limitations. When the processor 410 runs the computer program stored in the memory 420, it can execute the above method.

[0092] Processor 410 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 410 or by instructions in software form. The processor 410 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 420, and processor 410 reads the information from memory 420 and, in conjunction with its hardware, completes the steps of the above method.

[0093] Corresponding to the above method, this embodiment of the invention also provides a computer-readable storage medium storing machine-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to perform the steps of the above method.

[0094] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0095] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0096] Furthermore, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0097] It should be noted that if the functionality is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0098] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0099] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A closed-loop control method for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake, characterized in that, include: Based on the three-dimensional reconstruction results of the wind turbine wake, wake characteristic parameters are extracted, and the equivalent incoming wind speed in the rotor plane of the downstream unit is calculated. The equivalent incoming wind speed and the operating status of each wind turbine are input into the power generation prediction model to obtain the corresponding wind farm power generation prediction results. With the goal of maximizing the predicted power generation of the wind farm, and constrained by the unit's operating safety parameters, target control decision data is determined, and corresponding wind turbine wake control commands are generated.

2. The method according to claim 1, characterized in that, The methods for obtaining the three-dimensional reconstruction results of the wind turbine wake include: Three-dimensional reconstruction of the wind turbine wake is performed based on radial wind speed observation data acquired by wind-measuring lidar to obtain three-dimensional wind field data and corresponding reconstruction evaluation indicators for the wind turbine wake region; the three-dimensional wind field data includes wind speed vectors at various spatial locations in the wind turbine wake region.

3. The method according to claim 2, characterized in that, Based on the 3D reconstruction results of the wind turbine wake, wake feature parameters are extracted, including: The wind speed vectors at each spatial location in the three-dimensional wind field data are analyzed and calculated to obtain wake characteristic parameters used to characterize the wake spatial structure; The wake characteristic parameters include: wake centerline position, wake velocity deficit distribution, and / or wake deflection characteristics.

4. The method according to claim 3, characterized in that, Calculate the equivalent incoming air velocity at the rotor plane of the downstream unit, including: Based on the aforementioned wake characteristic parameters, the axial wind speed component within the swept area of ​​the downstream unit rotor is calculated; The axial wind speed component is subjected to area averaging, weighted averaging, or power equivalence calculation to obtain the equivalent incoming wind speed.

5. The method according to claim 1, characterized in that, The methods for generating the target control decision data include: Based on the wind farm power generation prediction results, and with the unit operation safety boundary as a constraint, the target control parameters corresponding to maximizing the overall power generation of the wind farm are determined. Based on the target control parameters, determine the cooperative yaw correction amount of the upstream unit; Based on the cooperative yaw correction, target control decision data is determined to adjust the operating status of the upstream wind turbine; the target control decision data includes at least two of the cooperative yaw corrections.

6. The method according to claim 2, characterized in that, The method further includes: dynamically adjusting the target control decision data according to the reconstructed evaluation indicators; If the reconstructed evaluation index does not meet the preset threshold, the weight coefficient of the equivalent incoming wind speed in the generation of the target control decision data / in the power generation prediction model is reduced, or control command data based on historical statistical data is output.

7. The method according to claim 6, characterized in that, The reconstruction evaluation metrics include reconstruction credibility and / or reconstruction uncertainty; The reconstruction credibility is used to evaluate the wake three-dimensional wind field reconstruction model; the wake three-dimensional wind field reconstruction model is constructed based on the radial wind speed observation data obtained by the wind measurement lidar, and is used to realize the three-dimensional reconstruction of the wind turbine wake. The reconstruction uncertainty is determined based on the signal-to-noise ratio and / or effective sampling rate of the radial wind speed observation data, and is used to evaluate the quality of the radial wind speed observation data.

8. A closed-loop control system for wind farm power generation enhancement based on three-dimensional reconstruction of wind turbine wake, characterized in that, include: The wake feature extraction module is used to extract wake feature parameters based on the three-dimensional reconstruction results of the wind turbine wake, and calculate the equivalent incoming wind speed in the rotor plane of the downstream unit. The power generation prediction module is used to input the equivalent incoming wind speed and the operating status of each wind turbine into the power generation prediction model to obtain the corresponding wind farm power generation prediction results. The control decision generation module is used to determine target control decision data and generate corresponding wind turbine wake control commands with the goal of maximizing the predicted power generation of the wind farm and constrained by the unit's operating safety parameters.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.

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