Laser wind radar auxiliary control system, method, device and storage medium

Abstract

The embodiments of this specification provide a laser wind measurement radar auxiliary control system, method, device and storage medium, the system including a data collection module, a data analysis module and a monitoring and adjustment module, wherein the data collection module is configured to obtain wind measurement data and working environment data of wind measurement equipment in a monitoring area, the wind measurement equipment including a laser radar and a wind measurement tower, the laser radar including at least one of a fixed radar and a mobile radar, and the working environment data including site configuration information and / or overall environmental information; the data analysis module is configured to determine the monitoring reference degree of the laser radar based on the wind measurement data and the working environment data; the monitoring and adjustment module is configured to determine the radar to be adjusted and the adjustment parameters corresponding to the radar to be adjusted based on the monitoring reference degree and the working environment data.

Description

Technical Field

[0001] This specification relates to the technical field of laser wind radar, and in particular to a laser wind radar auxiliary control system, method, device and storage medium. Background Art

[0002] Laser wind radar is a new mobile wind measurement technology. It utilizes the Doppler shift principle of lasers to measure the frequency change caused by light waves reflected from wind-driven aerosol particles in the air. This data can be used to calculate vector wind speed and direction at the corresponding altitude, allowing for an assessment of wind resources in the monitored area. However, during use, the laser wind radar's operating status is not monitored. Wind data obtained when the laser wind radar is operating abnormally provides a low reference value for wind resource assessment, potentially leading to inaccurate wind resource assessments in the monitored area.

[0003] To improve the accuracy of wind resource assessments, CN107807367B provides a coherent wind lidar device. This device emits multi-wavelength laser signals to increase the output power of the lidar's laser signal, thereby improving the signal-to-noise ratio of the lidar's return signal and, in turn, enhancing the lidar's detection performance. While this device improves the lidar's detection performance and the accuracy of wind measurement data to a certain extent, it still cannot monitor the lidar's operating status in real time, nor can it adjust the lidar's operating status when it is abnormal.

[0004] Therefore, it is hoped to provide a laser wind radar auxiliary control system, method, device and storage medium to help improve the auxiliary control of the laser wind radar, thereby accurately evaluating and reflecting the wind resources in the monitoring area. Summary of the Invention

[0005] One of the embodiments of the present specification provides a laser wind measurement radar assisted control system, the system comprising: a data collection module, configured to obtain wind measurement data and working environment data of wind measurement equipment in a monitoring area, the wind measurement equipment comprising a laser radar and a wind measurement tower, the laser radar comprising at least one of a fixed radar and a mobile radar, the working environment data comprising site configuration information and / or overall environmental information; a data analysis module, configured to determine a monitoring reference degree of the laser radar based on the wind measurement data and the working environment data; a monitoring adjustment module, configured to determine a radar to be adjusted and adjustment parameters corresponding to the radar to be adjusted based on the monitoring reference degree and the working environment data.

[0006] One of the embodiments of the present specification provides a laser wind measurement radar auxiliary control method, which is executed by a processor. The method includes: obtaining wind measurement data and working environment data of wind measurement equipment in a monitoring area, the wind measurement equipment includes a laser radar and a wind measurement tower, the laser radar includes at least one of a fixed radar and a mobile radar, and the working environment data includes site configuration information and / or overall environmental information; based on the wind measurement data and the working environment data, determining the monitoring reference degree of the laser radar; based on the monitoring reference degree and the working environment data, determining the radar to be adjusted and the adjustment parameters corresponding to the radar to be adjusted.

[0007] One of the embodiments of this specification provides a laser wind measurement radar auxiliary control device, which includes at least one processor and at least one memory; the at least one memory is used to store computer instructions; and the at least one processor is used to execute at least part of the computer instructions to implement a laser wind measurement radar auxiliary control method.

[0008] One embodiment of this specification provides a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions, the computer executes a laser wind radar assisted control method. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0010] Figure 1 is an exemplary module diagram of a laser wind radar assisted control system according to some embodiments of this specification;

[0011] Figure 2 is an exemplary flow chart of a laser wind radar assisted control method according to some embodiments of this specification;

[0012] Figure 3 is an exemplary schematic diagram of determining a monitoring reference level according to some embodiments of this specification;

[0013] Figure 4 is an exemplary schematic diagram of an abnormality analysis model according to some embodiments of this specification;

[0014] Figure 5 This is an exemplary schematic diagram of determining and updating a monitoring location according to some embodiments of this specification. DETAILED DESCRIPTION

[0015] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0016] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0017] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0018] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0019] In wind measurement technology, wind towers are expensive and difficult to relocate. In monitoring areas with complex terrain, wind towers are difficult to meet the requirements of wind resource assessment. Compared with wind towers, laser wind radars have more obvious advantages in wind measurement technology in wind farms. The combination of the two can effectively monitor and evaluate wind resources. However, due to the lack of monitoring of the working status of the laser wind radar, the wind resource assessment results may be inaccurate. CN107807367B can improve the performance of the wind measurement laser radar by emitting multi-wavelength laser signals, but it is unable to judge the working status of the laser wind radar and process and adjust it in time. In some embodiments of this specification, the monitoring reference degree of the laser radar is determined based on the wind measurement data and the working environment data, and then the radar to be adjusted and the corresponding adjustment parameters are determined. The working status of the laser radar can be monitored and the laser radar can be adjusted, thereby ensuring that the laser wind radar obtains richer wind measurement data than the wind tower, which helps to more accurately reflect and evaluate the wind resources in the monitoring area.

[0020] Figure 1FIG is an exemplary module diagram of a laser wind radar assisted control system according to some embodiments of this specification. Figure 1 As shown, the laser wind radar assisted control system 100 may include a data collection module 110 , a data analysis module 120 and a monitoring and adjustment module 130 .

[0021] In some embodiments, the data collection module 110 may be configured to obtain wind measurement data and working environment data of wind measurement equipment within the monitoring area.

[0022] In some embodiments, the data analysis module 120 may be configured to determine a monitoring reference degree of the lidar based on wind measurement data and working environment data.

[0023] In some embodiments, the data analysis module 120 can be further configured to: determine radars without abnormalities based on wind measurement data; group radars without abnormalities to determine wind measurement groups; determine the local reference degree of the wind measurement group based on the wind measurement data corresponding to the wind measurement group, the wind measurement data corresponding to the wind measurement tower, and the working environment data; determine the monitoring reference degree of the radars without abnormalities based on the local reference degree.

[0024] In some embodiments, the monitoring and adjustment module 130 may be configured to determine the radar to be adjusted and the adjustment parameters corresponding to the radar to be adjusted based on the monitoring reference level and the working environment data.

[0025] In some embodiments, the monitoring adjustment module 130 may be further configured to: determine the target mobile radar based on the monitoring reference degree; and determine an updated monitoring position of the target mobile radar based on the working environment data.

[0026] In some embodiments, the monitoring adjustment module 130 may be further configured to: determine a risk sub-area of the monitoring area based on the working environment data and the monitoring reference degree; and determine to update the monitoring position based on the risk sub-area.

[0027] In some embodiments, the lidar-assisted control system 100 may include a processor. The processor may be configured to process information and / or data related to the lidar-assisted control system 100. In some embodiments, the processor may process data, information, and / or processing results obtained from other devices or system components, and execute program instructions based on the data, information, and / or processing results to perform one or more functions described herein.

[0028] In some embodiments, the laser wind radar assisted control system 100 may include a storage device, etc., and the processor may obtain pre-stored data and / or information related to the laser wind radar assisted control system 100 from the storage device.

[0029] In some embodiments, the laser wind radar assisted control system 100 may include a network, and the processor may obtain data and / or information related to the laser wind radar assisted control system 100 through the network.

[0030] In some embodiments, the laser wind radar-assisted control system 100 may further include a user terminal. A user terminal may refer to one or more terminal devices or software used by a user. A user may refer to an administrator or operator of the laser wind radar-assisted control system 100. For example, a user terminal may include a mobile phone, tablet computer, interactive screen, etc.

[0031] For more information about the data collection module 110 , the data analysis module 120 , and the monitoring and adjustment module 130 , please refer to the following description.

[0032] It should be noted that the above description of the lidar-assisted control system 100 and its modules is for convenience only and does not limit this specification to the exemplary embodiments described. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the modules or form subsystems connected to other modules without departing from these principles.

[0033] Figure 2 FIG2 is an exemplary flow chart of a laser wind radar assisted control method according to some embodiments of this specification. In some embodiments, process 200 may be executed by a processor of a laser wind radar assisted control system. Figure 2 As shown, process 200 includes the following steps:

[0034] Step 210: Obtain wind measurement data and working environment data of wind measurement equipment in the monitoring area.

[0035] The monitoring area refers to the area where wind resource assessment and monitoring is required. For example, the monitoring area may include but is not limited to wind farms located in mountainous areas, oceans, etc. In some embodiments, the monitoring area may include the area where the wind measuring equipment is located, as well as the radiation area corresponding to the wind measuring equipment. For more information about the radiation area, please refer to Figure 5 In some embodiments, the size and scope of the monitoring area can be determined by a technician.

[0036] Wind measurement equipment refers to equipment for wind resource assessment and monitoring, and can be used to obtain wind measurement data. In some embodiments, the wind measurement equipment may include a laser radar and a wind tower.

[0037] A laser radar is a laser wind measurement radar that uses laser signals to assess and monitor wind resources. In some embodiments, the laser radar may include at least one of a fixed radar and a mobile radar.

[0038] Fixed radar refers to a fixed laser radar installed on a ground-based observation platform. A ground-based observation platform refers to a fixed surface meteorological observation platform.

[0039] Mobile radar refers to a movable laser radar installed on a mobile loading platform. Mobile loading platform refers to a movable meteorological observation platform used to carry mobile radar.

[0040] A wind tower is a tower structure used for wind resource assessment and monitoring.

[0041] Wind measurement data refers to meteorological observation data related to wind resources. For example, wind measurement data may include wind speed, wind direction, temperature, air pressure, relative humidity, etc. within a preset historical time period. The preset historical time period refers to a preset period of time in history. The preset historical time period can be preset based on historical experience, for example, one day.

[0042] The working environment data refers to data related to the working environment of the wind measuring device. In some embodiments, the working environment data may include site configuration information and / or overall environment information.

[0043] Site configuration information refers to configuration information related to the LiDAR. For example, site configuration information may include the LiDAR's location information, altitude, transmit window height, radar type, detection range, etc.

[0044] LiDAR location information refers to information related to the LiDAR's location, such as its latitude and longitude. Transmission window height refers to the height of the window through which the LiDAR transmits radar signals. Radar type refers to the type of radar signal emitted by the LiDAR, such as pulsed coherent, continuous wave coherent, or other types. Detection range refers to the maximum spatial distance over which the LiDAR can obtain accurate wind measurement data when operating normally.

[0045] Overall environmental information refers to information related to the overall environment of the monitoring area. For example, overall environmental information may include the location information of the monitoring area, topographic information, and the location information of the wind tower.

[0046] The location information of the monitoring area refers to information related to the location of the monitoring area, such as the longitude and latitude of the monitoring area. Topographic information refers to information related to the topography of the monitoring area. For example, the topographic information can be a three-dimensional model constructed based on surveying and mapping information of the monitoring area. The location information of the wind tower refers to information related to the location of the wind tower, such as the longitude and latitude of the tower.

[0047] In some embodiments, the processor may obtain the wind measurement data from the wind measurement device in various ways. For example, the processor may communicate with the wind measurement device via a network to obtain the wind measurement data monitored by the wind measurement device.

[0048] In some embodiments, the processor can obtain working environment data through various methods. For example, the processor can obtain working environment data stored in a storage device. In another example, the processor can connect to a user terminal via a network to obtain working environment data input by a user into the user terminal. In another example, the processor can obtain the location information of a lidar and a wind tower in the working environment data using positioning devices such as positioning chips and positioning sensors deployed in wind measurement equipment.

[0049] Step 220: Determine the monitoring reference degree of the lidar based on the wind measurement data and the working environment data.

[0050] Monitoring reference refers to the reference degree of wind measurement data obtained by lidar for evaluating wind resources in the monitoring area.

[0051] In some embodiments, the processor may determine the monitoring reference degree in a variety of ways based on wind measurement data and working environment data.

[0052] In some embodiments, the processor can determine the target wind tower corresponding to the lidar based on the working environment data; and determine the wind measurement similarity based on the wind measurement data of the lidar and the wind measurement data of the target wind tower; based on the wind measurement similarity and the distance between the lidar and the target wind tower, determine the monitoring reference degree through a first preset rule.

[0053] The target wind tower refers to the wind tower closest to the laser radar. In some embodiments, the processor can determine the wind tower closest to the laser radar as the target wind tower based on the position information of the laser radar and the position information of the wind tower in the working environment data.

[0054] Wind similarity refers to the degree of similarity between the wind data measured by the lidar and the wind data measured by the target wind tower. In some embodiments, the processor can determine the wind similarity based on the wind data measured by the lidar and the wind data measured by the target wind tower using a similarity calculation method such as Euclidean distance or cosine similarity.

[0055] The first preset rule is a pre-set rule for determining the monitoring reference level. As the distance between the lidar and the target wind tower increases and the wind measurement similarity decreases, the lidar's wind data can be used to supplement the tower's wind data when assessing wind resources in the monitoring area. The lidar's corresponding monitoring reference level increases accordingly.

[0056] An exemplary first preset rule may be: the higher the wind measurement similarity and the smaller the distance between the lidar and the target wind tower, the smaller the monitoring reference corresponding to the lidar. For example, the first preset rule may be the following formula: monitoring reference = k1 / wind measurement similarity + k2*distance between the lidar and the target wind tower.

[0057] Among them, k1 and k2 are calculation coefficients, and the specific values can be preset based on historical experience.

[0058] For fixed radar and mobile radar, when the wind measurement similarity and the distance from the target wind tower are the same, since the fixed radar cannot be moved, in actual use, the wind measurement data of the fixed radar tends to be used to evaluate the wind resources in the monitoring area, so as to facilitate the improvement of the corresponding monitoring reference of the mobile radar by moving the mobile radar in the subsequent evaluation and monitoring process.

[0059] In some embodiments, the values of k1 and k2 corresponding to the fixed radar and the mobile radar are different. When the values of k1 and k2 for the fixed radar and the mobile radar are preset, the values of k1 and k2 corresponding to the fixed radar are higher than the values of k1 and k2 corresponding to the mobile radar.

[0060] In some embodiments, the processor can determine the radar without abnormality based on the wind measurement data, and determine the monitoring reference degree of the radar without abnormality based on the local reference degree of the wind measurement group. For more information, please refer to Figure 3 and its related descriptions.

[0061] Step 230: Determine the radar to be adjusted and the adjustment parameters corresponding to the radar to be adjusted based on the monitoring reference and the working environment data.

[0062] Adjustment parameters refer to parameters related to adjusting the radar to be adjusted. In some embodiments, the adjustment parameters may include updating the monitoring position and updating the monitoring parameters of the radar to be adjusted. It should be noted that when the radar to be adjusted is a fixed radar, the adjustment parameters include updating the monitoring parameters.

[0063] The updated monitoring location refers to the location where wind resource assessment monitoring is performed after the radar to be adjusted is adjusted. The updated monitoring parameters refer to the monitoring parameters of the radar to be adjusted after the radar to be adjusted is adjusted.

[0064] Monitoring parameters refer to parameters related to wind resource assessment and monitoring performed by the LiDAR. For example, monitoring parameters may include range gates, system parameters, and scanning parameters. The range gate refers to the ability of the LiDAR to distinguish between two different objects. The LiDAR can distinguish between two different objects that are separated by a distance not less than the range gate. System parameters refer to parameters related to the system operation of the LiDAR, such as the laser pulse width of the LiDAR. Scan parameters refer to parameters related to scanning performed by the LiDAR. For example, scanning parameters may include different scanning modes, number of scanning points, etc. Exemplary scanning modes may include circular discrete, circular continuous, sector discrete, sector continuous, etc.

[0065] In some embodiments, the processor may determine the radar to be adjusted and its corresponding adjustment parameters in a variety of ways based on the monitoring reference degree and the working environment data.

[0066] For example, the processor can determine a fixed radar whose monitoring reference meets the first preset condition as the radar to be adjusted, and determine the updated monitoring parameters in the adjustment parameters of the radar to be adjusted by querying the first preset table based on the working environment data of the radar to be adjusted.

[0067] The first preset condition refers to a pre-set condition used to determine whether a fixed radar is a radar to be adjusted. The first preset condition can be system- or user-defined. An example of the first preset condition is that the monitoring reference level of the fixed radar is lower than a first threshold. The first threshold refers to the lowest value of the monitoring reference level under normal operating conditions of the fixed radar. The first threshold can be system- or user-defined.

[0068] The first preset table may include the correspondence between different altitudes, different transmission window heights, different radar types and different updated monitoring parameters corresponding to the fixed radar in the working environment data. The first preset table may be preset based on historical experience or historical data.

[0069] For another example, the processor may determine a mobile radar whose monitoring reference degree meets the second preset condition as the radar to be adjusted, and determine the updated monitoring parameters when the radar to be adjusted is a mobile radar in a manner similar to the aforementioned determination of the updated monitoring parameters of the fixed radar; and upload the monitoring reference degree and working environment data of the radar to be adjusted to the user terminal, and the technical staff shall determine the updated monitoring position in the adjustment parameters of the radar to be adjusted.

[0070] The second preset condition refers to a pre-set condition for determining whether a mobile radar is a radar to be adjusted. The second preset condition can be system- or manually-set. An exemplary second preset condition may be: the mobile radar's monitoring reference level is lower than the highest value of the monitoring reference level in historical data when the mobile radar was repeatedly determined to be a radar to be adjusted.

[0071] In some embodiments, the processor can determine the target mobile radar based on the monitoring reference degree; and determine the updated monitoring position of the target mobile radar based on the working environment data. For more information on updating the monitoring position, please refer to Figure 2 Related description in the previous article.

[0072] The target mobile radar refers to a mobile radar determined as a radar to be adjusted.

[0073] In some embodiments, the processor can determine the target mobile radar based on the monitoring reference level using various methods. For example, the processor can determine a mobile radar whose monitoring reference level is below a second threshold as the target mobile radar. The second threshold is the lowest value of the monitoring reference level when the mobile radar is operating normally. The second threshold can be preset by the system or manually.

[0074] In some embodiments, the processor may determine an updated monitoring position of the target mobile radar based on the working environment data by a second preset rule.

[0075] The second preset rule refers to a pre-set rule for determining the updated monitoring position of the target mobile radar. The second preset rule can be preset by the system or manually. An exemplary second preset rule may include the following steps S11-S13.

[0076] Step S11: generating a plurality of candidate monitoring locations in the monitoring area.

[0077] Candidate monitoring locations are locations within the monitoring area that may be used as updated monitoring locations. In some embodiments, there is no wind measurement equipment within a first preset distance of the candidate monitoring location, and the distance between each of the candidate monitoring locations is greater than a second preset distance. Both the first preset distance and the second preset distance can be preset based on historical experience.

[0078] In some embodiments, the processor may randomly generate multiple locations within the monitoring area and determine as candidate monitoring locations those locations that have no wind measuring equipment within a first preset distance and are farther from other locations than a second preset distance. It should be noted that the number of candidate monitoring locations is greater than or equal to the number of target mobile radars within the monitoring area.

[0079] Step S12: For each candidate monitoring location, based on the working environment data, determine the minimum distance between the candidate monitoring location and the wind measurement equipment.

[0080] In some embodiments, the processor may determine the distances between the candidate monitoring location and all wind measuring devices based on the candidate monitoring location and location information of the wind measuring devices in the working environment data, and determine the minimum distance between the candidate monitoring location and the wind measuring devices as the minimum distance between the candidate monitoring location and the wind measuring devices.

[0081] Step S13 , arranging multiple candidate monitoring positions based on the minimum distances corresponding to the candidate monitoring positions, and determining an updated monitoring position based on the arrangement order of the candidate monitoring positions.

[0082] In some embodiments, the processor may arrange the plurality of candidate monitoring locations based on the minimum distance value corresponding to each candidate monitoring location. For example, the location with a larger minimum distance value is arranged at the front.

[0083] The processor may randomly assign the candidate monitoring positions of the target number arranged in the front to the target mobile radar, and the candidate monitoring position assigned to the corresponding target mobile radar is the updated monitoring position of the target mobile radar. The target number refers to the number of target mobile radars.

[0084] In some embodiments, the processor may determine an updated monitoring location based on the risk sub-area. For more information, see Figure 5 and its related descriptions.

[0085] In some embodiments of the present specification, a target mobile radar is determined based on a monitoring reference degree, and an updated monitoring position is determined based on working environment data. This allows the determined updated monitoring position to maintain a certain distance from the wind measuring equipment, thereby ensuring that the corresponding monitoring reference degree is improved when the target mobile radar is adjusted to the updated monitoring position. This is conducive to making full use of the wind measurement data obtained by the target mobile radar to evaluate and monitor the wind resources in the monitoring area.

[0086] In some embodiments of the present specification, the monitoring reference degree of the laser radar is determined by wind measurement data and working environment data, and then the radar to be adjusted and its corresponding adjustment parameters are determined. When the wind measurement data obtained by the radar to be adjusted has a low reference degree for wind resource assessment and monitoring in the monitoring area, the radar to be adjusted can be adjusted by adjusting the parameters, thereby improving the monitoring reference degree of the radar to be adjusted, which helps to obtain richer wind measurement data and conduct a more accurate and comprehensive assessment of the wind resources in the monitoring area.

[0087] It should be noted that the above description of the relevant processes is for illustration and purpose only and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the processes under the guidance of this specification. However, such modifications and changes are still within the scope of this specification.

[0088] Figure 3 This is an exemplary schematic diagram of determining a monitoring reference degree according to some embodiments of this specification.

[0089] In some embodiments, the processor can determine the radar without abnormality 320 based on the wind measurement data 310; group the radar without abnormality 320 to determine the wind measurement group 330; determine the local reference degree 360 of the wind measurement group based on the wind measurement data 340 corresponding to the wind measurement group, the wind measurement data 310-2 corresponding to the wind measurement tower, and the working environment data 350; and determine the monitoring reference degree 370 of the radar without abnormality based on the local reference degree 360. For relevant instructions on obtaining wind measurement data, working environment data, monitoring reference degree, and wind measurement data corresponding to the wind measurement tower, please refer to Figure 2 and its related descriptions.

[0090] The Abnormal-Free Radar 320 refers to a laser radar that monitors wind data accurately and without deviation.

[0091] In some embodiments, the processor can use various methods to determine if an abnormal radar is present based on wind measurement data. For example, the processor can obtain the duration of no change in each item of wind measurement data from the lidar and determine as an abnormal radar any lidar whose duration of no change in each item of wind measurement data is less than a preset duration. The preset duration can be system- or user-defined, for example, one hour. Each item of wind measurement data refers to different types of data within the wind measurement data, such as wind speed, wind direction, air pressure, relative humidity, etc.

[0092] The no-change duration refers to the duration during which the wind measurement data remains unchanged. No-change in wind measurement data means that the maximum absolute deviation of the wind measurement data is less than a deviation threshold. In some embodiments, the processor may determine the no-change duration as the duration during which the maximum absolute deviation of the wind measurement data remains less than the deviation threshold.

[0093] Absolute deviation is the difference between an individual measurement and the average of multiple measurements. An individual measurement can refer to one of multiple wind measurement data points. The average of multiple measurements refers to the average of multiple wind measurement data points. The maximum absolute deviation is the maximum absolute deviation of each wind measurement data point.

[0094] The deviation threshold refers to a pre-set threshold condition used to determine whether the wind measurement data has not changed. The deviation threshold can be preset based on historical experience. In some embodiments, the deviation threshold can be different depending on the data type of the wind measurement data. For example, the deviation threshold corresponding to wind speed can be 0.1m / s, the deviation threshold corresponding to wind direction can be 0.5°, the deviation threshold corresponding to air pressure can be 0.05hPa, the deviation threshold corresponding to relative humidity can be 0.05%, etc.

[0095] In some embodiments, the processor can also determine the radar without abnormalities through the abnormality analysis model. For more information, please refer to Figure 4 Related description.

[0096] The wind measurement group 330 refers to a group obtained after grouping the radars without abnormalities. In some embodiments, one radar without abnormalities may be in multiple wind measurement groups.

[0097] In some embodiments, the processor can obtain wind measurement groupings based on various methods. For example, the processor can randomly group multiple radars without anomalies. While performing the random grouping, the processor can also impose restrictions on the groupings, such as limiting the number of radars without anomalies in each wind measurement group or limiting the number of wind measurement groups. Specific restrictions can be preset by the system or manually.

[0098] In some embodiments, a wind measurement group corresponds to a wind measurement sub-area. A wind measurement sub-area refers to a monitoring area composed of radars without abnormalities in the wind measurement group. In some embodiments, the processor can determine the intersection of all radar radiation areas without abnormalities in the wind measurement group as the wind measurement sub-area. For more information about the radiation area, see Figure 5 and its related descriptions.

[0099] The wind measurement data 340 corresponding to the wind measurement group refers to the wind measurement data corresponding to all radars without abnormalities in the wind measurement group.

[0100] In some embodiments, the processor can determine the radars without abnormalities in the wind measurement group based on the wind measurement group 330, and based on the wind measurement data 310-1 corresponding to the laser radar, determine all wind measurement data corresponding to the radars without abnormalities in the wind measurement group as the wind measurement data 340 corresponding to the wind measurement group. For information on obtaining wind measurement data corresponding to the laser radar, please refer to Figure 2 , and the related description of step 210.

[0101] The local reference degree 360 refers to the reference degree of all wind measurement data in the wind measurement group for evaluating the wind resources in the entire monitoring area.

[0102] In some embodiments, the processor may determine the local reference degree 360 of the wind measurement group in a variety of ways based on the wind measurement data 340 corresponding to the wind measurement group, the wind measurement data 310 - 2 corresponding to the wind measurement tower, and the working environment data 350 .

[0103] In some embodiments, the processor can simulate the wind resource conditions of each grid point in the monitoring area through a Computational Fluid Dynamics (CFD) simulation model based on all wind measurement data 310 and working environment data 350 in the monitoring area; simulate the wind resource conditions of each grid point in the same monitoring area for each wind measurement group through a CFD simulation model based on the wind measurement data 340 corresponding to each wind measurement group, the wind measurement data 310-2 corresponding to the wind tower, and the working environment data 350; and determine the similarity of the wind resource conditions predicted by the two methods, and determine the similarity of the wind resource conditions as the local reference degree of the wind measurement group. In some embodiments, the processor can determine the similarity of the wind resource conditions based on multiple methods such as Euclidean distance and cosine similarity.

[0104] A grid point refers to a single area after the monitoring area is divided. In some embodiments, the processor can evenly divide the monitoring area and determine multiple grid points of equal size. The size of the grid point can be preset by the system, for example, the grid point can be 50m×50m in size.

[0105] In some embodiments, the processor may determine the monitoring reference level of the normal radar based on the local reference level in various ways. For example, the processor may determine the monitoring reference level of the normal radar as the average of the local reference levels of all wind measurement groups in which the normal radar is located.

[0106] In some embodiments, the processor may perform weighted summation on the local reference degrees to determine the monitoring reference degree of the radar without abnormalities.

[0107] In some embodiments, the processor may assign different weights to each local reference degree in the wind measurement group where the radar without abnormality is located, and determine the result of the weighted sum as the monitoring reference degree of the radar without abnormality.

[0108] In some embodiments, the weight of the local reference degree may be related to the importance of the sub-region corresponding to the wind measurement group.

[0109] Sub-area importance refers to the importance of the wind measurement sub-area corresponding to the wind measurement group.

[0110] In some embodiments, the processor may determine the importance of the sub-region according to a third preset rule based on the area, center position, and number of wind measurement towers included in the wind measurement sub-region.

[0111] The center position of a wind measurement sub-region refers to the geographic center position of the wind measurement sub-region corresponding to the wind measurement group. In some embodiments, the processor can determine the position of the wind measurement sub-region based on the location information of the radars without abnormalities in the wind measurement group and the corresponding radiation area, and determine the center position of the wind measurement sub-region using the geometric mean method, the minimum circumscribed circle method, or other feasible methods.

[0112] In some embodiments, the processor may determine the area of the wind measurement sub-area corresponding to the wind measurement group based on the location of the wind measurement sub-area by using Gaussian area or other feasible area calculation methods. In some embodiments, the processor may determine the number of wind measurement towers included in the wind measurement sub-area based on the location of the wind measurement sub-area and the location information of the wind measurement towers. The determination of the location information of the wind measurement towers can be referred to Figure 2 , and the related description of step 210.

[0113] The third preset rule is a pre-set rule for determining the importance of a sub-region. Exemplary third preset rules may include: a larger wind measurement sub-region, a smaller distance between the center of the wind measurement sub-region and the monitoring center, and a greater number of wind measurement towers within a wind measurement sub-region are associated with a higher importance of the corresponding sub-region.

[0114] The monitoring center refers to the center of the monitoring area and can be determined by a technician. For example, the technician can determine the location with the most abundant wind resources as the monitoring center based on a preliminary survey. The processor can determine the distance between the center of the wind measurement sub-area and the monitoring center based on the location of the wind measurement sub-area and the location of the monitoring center.

[0115] For example, the third preset rule may include the following formula: sub-region importance=a1*s+a2 / d.

[0116] Where s is the area of the wind measurement sub-area, d is the distance between the center of the wind measurement sub-area and the monitoring center, and a1 and a2 are calculation coefficients.

[0117] In some embodiments, the processor may determine the values of a1 and a2 corresponding to a wind measurement sub-region based on the number of wind measurement towers within the wind measurement sub-region using a first preset relationship. The first preset relationship refers to a preset relationship between the number of wind measurement towers within the wind measurement sub-region and a1 and a2. An exemplary first preset relationship may be: the greater the number of wind measurement towers within the wind measurement sub-region, the greater the values of a1 and a2 corresponding to the wind measurement sub-region.

[0118] In some embodiments, the weight of the local reference degree of the wind measurement grouping may be positively correlated with the sub-region importance. In some embodiments, the processor may determine the weight corresponding to the local reference degree of the wind measurement grouping through a fourth preset rule based on the sub-region importance. The fourth preset rule refers to a pre-set rule for determining the weight corresponding to the local reference degree of the wind measurement grouping. An exemplary fourth preset rule may be: the higher the sub-region importance corresponding to the wind measurement grouping, the higher the weight corresponding to the local reference degree of the wind measurement grouping.

[0119] For example, the fourth preset rule may be: determining the proportion of the sub-region importance corresponding to the wind measurement group in the sum of the sub-region importances of all wind measurement groups as the weight corresponding to the local reference degree of the wind measurement group.

[0120] In some embodiments of the present specification, by performing weighted summation on the local reference degrees of the wind measurement groups in which the radars without anomalies are located to determine the monitoring reference degree of the radars without anomalies, the local reference degrees corresponding to the wind measurement groups with greater sub-area importance can be given higher weights, which helps to improve the accuracy of determining the monitoring reference degrees.

[0121] In some embodiments of the present specification, radars without abnormalities are determined based on wind measurement data, wind measurement groups are determined, and local reference degrees are determined based on wind measurement data and working environment data, and then monitoring reference degrees are determined. This is conducive to fully combining the wind measurement data of radars without abnormalities in multiple wind measurement groups to comprehensively judge the monitoring reference degrees of radars without abnormalities, and can more accurately judge the monitoring reference degrees of radars without abnormalities, which is helpful for the subsequent determination of radars to be adjusted.

[0122] Figure 4 is an exemplary schematic diagram of an abnormality analysis model according to some embodiments of this specification.

[0123] In some embodiments, the processor may determine the absence of anomaly radar 320 based on the wind measurement data 310 through the anomaly analysis model 420 .

[0124] For more information about wind data, radar, etc., please refer to Figure 2 and Figure 3 Related description.

[0125] The abnormality analysis model 420 is a model used to determine whether the laser radar is abnormal.

[0126] In some embodiments, the anomaly analysis model may be a machine learning model, such as a neural network (NN) model.

[0127] In some embodiments, the input of the anomaly analysis model may include wind measurement data 310-1 corresponding to the lidar in the wind measurement data 310, wind measurement data 310-2 corresponding to the wind tower, and device distance 410, and the output may include a judgment result 430. Device distance 410 refers to the distance between the lidar and the wind tower.

[0128] In some embodiments, the processor can obtain the position information of the laser radar and the position information of the wind tower corresponding to the wind measurement data input into the abnormal analysis model based on the working environment data, and then determine the device distance. For more information about working environment data, please refer to Figure 2 and its related descriptions.

[0129] The judgment result 430 refers to the judgment result of whether the laser radar corresponding to the wind measurement data input to the abnormality analysis model is abnormal.

[0130] In some embodiments, the anomaly analysis model can be trained based on a large number of first training samples with a first label. For example, multiple first training samples with a first label can be input into an initial anomaly analysis model. A loss function can be constructed using the first label and the output of the initial anomaly analysis model. Based on the loss function, the parameters of the initial anomaly analysis model are iteratively updated using gradient descent or other methods. Model training is completed when preset iteration conditions are met, resulting in a trained anomaly analysis model. The preset iteration conditions may include convergence of the loss function, or a threshold number of iterations.

[0131] In some embodiments, the first training sample may include wind measurement data corresponding to a sample laser radar, wind measurement data corresponding to a sample wind tower, and a sample device distance. The first training sample may be obtained based on historical data.

[0132] In some embodiments, the first label may include a determination of whether the sample lidar corresponding to the first training sample is actually abnormal. The first label may be a value of 0 or 1, with a value of 0 indicating an abnormality in the corresponding sample lidar, and vice versa. The first label may be manually annotated.

[0133] In some embodiments, the processor may determine the result of the user's erroneous modification of wind measurement data without abnormalities in historical data as the first training sample, and mark the first label corresponding to the first training sample as 0 to expand the first training sample and avoid the problem of imbalance of the first training sample.

[0134] In some embodiments, the processor may determine that there is no abnormal radar based on the judgment result. The processor may determine that the corresponding laser radar is the radar without abnormality when the judgment result output by the abnormality analysis model is that there is no abnormality.

[0135] In some embodiments, the anomaly analysis model may include multiple sub-models, each corresponding to different types of wind measurement data. Figure 2 and Figure 3 Related description in the previous article.

[0136] Submodels are used within the anomaly analysis model to determine whether different types of wind data acquired by the lidar are abnormal. For example, submodel 1 can be used to analyze whether the wind speed in the wind data is abnormal; submodel 2 can be used to determine whether the wind direction in the wind data is abnormal.

[0137] When there are no abnormalities in the different types of wind measurement data obtained by the lidar, the corresponding lidar is an abnormality-free radar.

[0138] In some embodiments, the processor can input different types of wind measurement data into corresponding sub-models to determine whether different types of wind measurement data are abnormal, and determine the lidar whose different types of wind measurement data are normal as a normal radar.

[0139] In some embodiments of this specification, the accuracy of determining radar abnormalities can be improved by using different sub-models to classify and analyze wind measurement data of a lidar.

[0140] In some embodiments of this specification, the wind measurement data corresponding to the lidar, the wind measurement data corresponding to the wind tower, and the device distance are analyzed through an anomaly analysis model. The self-learning ability of the machine learning model can be used to find patterns in a large amount of data to efficiently and accurately determine whether there is an abnormal radar.

[0141] Figure 5 This is an exemplary schematic diagram of determining and updating a monitoring location according to some embodiments of this specification.

[0142] In some embodiments, the processor may determine a risk sub-area 510 of the monitoring area based on the working environment data 350 and the monitoring reference 370, and determine an updated monitoring position 550 based on the risk sub-area 510. For more information on working environment data and monitoring reference, see Figure 2 and its related descriptions.

[0143] Risk sub-area 510 refers to an area in the monitoring area where wind resource assessment monitoring is insufficient.

[0144] In some embodiments, the processor may determine the risk sub-area in a variety of ways based on the work environment data and the monitoring reference level.

[0145] In some embodiments, the processor can divide the monitoring area into multiple candidate risk areas, determine the radiation area of the lidar in the monitoring area based on the working environment data; determine the candidate monitoring heat based on the monitoring reference degree of the lidar in the candidate risk area and the radiation area of the lidar, and determine the candidate risk area whose candidate monitoring heat is lower than the first heat threshold as a risk sub-area.

[0146] The candidate risk area refers to an area in the monitoring area that may serve as a risk sub-area. In some embodiments, the processor may randomly divide the monitoring area to obtain multiple candidate risk areas.

[0147] The laser radar's radiation area refers to the range within which the laser radar can detect wind measurement data. In some embodiments, based on the laser radar's location information and the laser radar's detection range in the working environment data, the processor can determine a circular area within the monitoring area centered on the laser radar and with a radius equal to the laser radar's detection range as the laser radar's radiation area.

[0148] The candidate monitoring heat refers to the heat value of the candidate risk area within the wind resource assessment monitoring of the monitoring area. In some embodiments, based on the candidate risk area and the radiation area of the lidar, the processor may determine the maximum monitoring reference degree corresponding to all lidars within the radiation area that are partially or completely located in the candidate risk area as the candidate monitoring heat value for the candidate risk area.

[0149] The first heat threshold refers to a threshold condition for determining a risk sub-region based on the candidate monitored heat. In some embodiments, the first heat threshold can be system-based or manually preset.

[0150] In some embodiments, the processor may divide the monitoring area into a plurality of unit grids; determine the grid monitoring heat corresponding to the plurality of unit grids based on the site configuration information and the monitoring reference degree; and determine the risk sub-area based on the grid monitoring heat.

[0151] The unit grid refers to the grid of preset size in the monitoring area.

[0152] In some embodiments, the size of the unit grid can be preset by the system or manually. For example, the unit grid can be an area of 100m×100m. In some embodiments, the processor can divide the monitoring area into equal parts according to the size of the unit grid to obtain multiple unit grids.

[0153] The grid monitoring heat refers to the heat value of the unit grid in the monitoring area for wind resource assessment and monitoring.

[0154] In some embodiments, the processor may determine the ground grid monitoring heat level using a fifth preset rule based on the site configuration information and the monitoring reference level. The fifth preset rule refers to a pre-set rule for determining the ground grid monitoring heat level.

[0155] An exemplary fifth preset rule may be: determining the target wind measuring device corresponding to the unit grid based on the site configuration information, and determining the sum of the products of the radiation ratio of the target wind measuring device and the monitoring reference degree of the target wind measuring device as the grid monitoring heat.

[0156] For example, the fifth preset rule can be expressed by the following formula: Ground grid monitoring heat = ∑(s i ×r i ).

[0157] Among them, s i is the radiation ratio corresponding to the i-th target wind measurement device corresponding to the unit grid, r i is the monitoring reference degree of the i-th target wind measurement equipment corresponding to the unit grid.

[0158] The target wind measuring device refers to a wind measuring device that is partially or completely located in a unit grid within the radiation area. The radiation area of a wind tower refers to the range of wind data that can be detected by the wind tower. The processor can obtain the detection length of the wind tower from the storage device or user input, and then determine the radiation area of the wind tower. In some embodiments, the method for determining the radiation area of the wind tower is similar to that of the laser radar. Figure 5 Related description in the previous article.

[0159] The radiation ratio refers to the radiation ratio of the target wind measuring device to the unit grid. In some embodiments, the processor can determine the radiation ratio of the target wind measuring device corresponding to the unit grid as the ratio of the area of the unit grid within the radiation area of the target wind measuring device to the area of the unit grid.

[0160] When the target wind measurement device is a wind tower, the monitoring reference of the wind tower can be a default value. For example, the monitoring reference of the wind tower can be set by the system or manually. For the determination of the monitoring reference of the laser radar in the target wind measurement device, please refer to Figure 2 and Figure 3 Related description.

[0161] In some embodiments, the processor may determine as a risk sub-area an area consisting of unit grids whose grid heat level is below a second heat threshold. The second heat threshold refers to a threshold condition for determining a risk sub-area based on the grid heat level. In some embodiments, the second heat threshold may be system-based or manually preset.

[0162] In some embodiments, the processor may also determine the expected heat corresponding to a plurality of unit grids based on the overall environmental information; and determine the risk sub-area based on the monitored heat of the grids and the expected heat.

[0163] Expected heat refers to the expected value of the heat value of the wind resource assessment monitoring per unit grid in the monitoring area.

[0164] In some embodiments, the processor may determine the expected heat level of a unit grid in a variety of ways based on the overall environmental information.

[0165] In some embodiments, the processor may determine the expected heat corresponding to the unit grid through the expected heat model based on the overall environment information and the unit grid information.

[0166] Unit grid information refers to information related to the unit grid. For example, the unit grid information may include the latitude and longitude range, altitude range, altitude distribution, etc. corresponding to the unit grid.

[0167] In some embodiments, the processor may determine unit grid information based on the overall environmental information and the divided unit grids. The altitude distribution refers to the proportion of the altitude distribution in the unit grid in each altitude sub-range. The altitude sub-range may be preset by the system or manually. For example, the altitude sub-range may include 1000m-1100m, 1100m-1200m, etc.

[0168] The expected popularity model refers to a model used to determine the expected popularity. In some embodiments, the expected popularity model can be a machine learning model, such as a convolutional neural network (CNN).

[0169] In some embodiments, the input of the expected heat model may include overall environment information and unit grid information, and the output may include the expected heat corresponding to the unit grid.

[0170] In some embodiments, the expected heat model can be obtained by training based on the second training sample with the second label. The training process of the expected heat model is similar to the training process of the anomaly analysis model, which can be seen in Figure 4 and its related descriptions.

[0171] In some embodiments, the second training sample may include the sample overall environment information corresponding to the sample monitoring area and the sample unit grid information of the sample unit grid. The second training sample may be obtained based on historical data.

[0172] In some embodiments, the second label may include the expected heat of the sample per unit grid in the second training sample. In some embodiments, the second label may be obtained in a variety of ways. For example, the second label may be calibrated by a technician.

[0173] In some embodiments, the processor may further obtain a second tag through the following steps S21-S23 based on the predictability of the wind measurement data in the sample unit grid and the degree of association with extreme meteorological conditions:

[0174] Step S21: Divide the monitoring area into multiple parts, and determine the first unit grid in each part of the monitoring area based on the degree of correlation between the sample unit grid and the extreme meteorological conditions.

[0175] In some embodiments, the processor may randomly divide the monitoring area or divide it based on human factors. For example, a technician may divide the monitoring area into five parts: east, west, north, south, and center.

[0176] The first unit grid refers to a sample unit grid having an expected heat value of a high expected heat value. The high expected heat value can be preset by the system or manually.

[0177] Extreme weather conditions may include hail, strong winds, tornadoes, thunderstorms, tropical cyclones, etc. The degree of correlation between the sample unit grid and the extreme weather conditions refers to the degree of correlation between the wind measurement data of the sample unit grid and the extreme weather conditions.

[0178] In some embodiments, the processor may determine the degree of association between the sample unit grid and the extreme weather conditions based on statistics of historical data.

[0179] For example, the processor can count the wind measurement data of the sample unit grids within a preset time (such as one week, etc.) before each extreme weather condition occurs in the historical data, and determine the sample unit grids whose wind measurement data meet the third preset condition as being associated with the extreme weather condition, and determine the ratio of the number of times the sample unit grid is associated with all extreme weather conditions to the total number of all extreme weather conditions as the degree of association between the sample unit grid and the extreme weather condition.

[0180] The third preset condition refers to a judgment condition for determining whether the sample unit grid is associated with extreme weather conditions based on wind measurement data. The third preset condition can be preset by the system or manually. Exemplary third preset conditions may include: within a preset time before the occurrence of extreme weather conditions, the wind speed exceeds the maximum wind speed threshold and / or the air pressure is higher than the maximum air pressure threshold. It should be noted that the wind measurement data of the sample unit grid can be obtained based on the wind measurement data obtained by the wind measurement equipment in the historical data, or based on Figure 3 The wind resource conditions simulated by the aforementioned CFD model are obtained.

[0181] For example, if there are ten extreme weather conditions in the area where the sample unit grid is located, and eight of them occur, the wind measurement data of the sample unit grid meets the third preset condition. Then the number of times the sample unit grid is associated with all extreme weather conditions is eight, and the degree of association between the sample unit grid and the extreme weather conditions is 80%.

[0182] In some embodiments, the processor can sort the degree of correlation between multiple sample unit grids and extreme weather conditions from large to small, determine the sample unit grid that is arranged before a preset number as the first unit grid, and determine that the sample expected heat in the second label corresponding to the first unit grid is the high expected heat value determined above.

[0183] Step S22: Based on the first unit grid, determine the second unit grid, and determine the expected heat of the sample in the second tag corresponding to the second unit grid based on the predictability of the wind measurement data of the second unit grid.

[0184] The second unit grid is a sample unit grid located in the middle of the plurality of first unit grids. In some embodiments, the processor may connect the plurality of first unit grids in pairs and determine the sample unit grid at the midpoint of the connection as the second unit grid.

[0185] In some embodiments, the processor may determine the predictability of the wind measurement data of the second unit grid in a variety of ways based on historical data.

[0186] For example, the processor can perform modeling or adopt various data analysis algorithms, such as regression analysis, discriminant analysis, etc., to process the wind measurement data of multiple first unit grids and the wind measurement data of second unit grids in the historical data, determine the number of wind measurement data of the second unit grid that have the same functional relationship with the wind measurement data of the first unit grid, and determine the proportion of the number of wind measurement data of the second unit grid that have the same functional relationship with the wind measurement data of the first unit grid to the total number of wind measurement data of the second unit grid as the predictability of the wind measurement data of the second unit grid.

[0187] For example, the historical data includes 100 wind measurement data in the second unit grid, of which 75 wind measurement data have the same functional relationship with the wind measurement data of the first unit grid, then the predictability of the wind measurement data of the second unit grid is 75%.

[0188] In some embodiments, the processor may determine the expected heat of the sample in the second tag corresponding to the second unit grid using a sixth preset rule based on the predictability of the wind measurement data of the second unit grid.

[0189] The sixth preset rule refers to a preset rule for determining the expected heat of the sample corresponding to the second unit grid. An exemplary sixth preset rule may be: the greater the predictability of the wind measurement data of the second unit grid, the smaller the corresponding expected heat of the sample.

[0190] For example, the sixth preset rule may include the following formula: expected heat of the sample corresponding to the second unit grid = (100% - x) × α, where x is the predictability of the wind measurement data of the second unit grid, and α is the high expected heat value determined above.

[0191] Step S23: Based on the first unit grid and the second unit grid, the expected heat of the samples in the remaining sample unit grids and their corresponding second tags is determined by the method of the aforementioned step S22 until all sample unit grids are traversed.

[0192] In some embodiments, the processor may determine a risk sub-region based on the monitored heat level of the grid and the expected heat level. For example, the processor may determine an area consisting of grids where the monitored heat level is lower than the expected heat level as a risk sub-region.

[0193] In some embodiments of this specification, risk sub-areas are determined based on the monitored heat of the grid and the expected heat, which can make the determination of the risk sub-areas more consistent with actual expectations and facilitate the subsequent determination of updated monitoring locations.

[0194] In some embodiments of this specification, the risk sub-areas may be determined more meticulously and comprehensively by dividing the monitoring area into a plurality of unit grids and then determining the risk sub-areas based on the monitoring heat of the grids.

[0195] In some embodiments, the processor can determine the updated monitoring positions based on the risk sub-area in a variety of ways. For example, the processor can randomly generate the same number of updated monitoring positions as the target mobile radar in the risk sub-area. For more information about the target mobile radar, see Figure 2 and its related descriptions.

[0196] In some embodiments, the processor may determine to update the monitoring location group 520 based on the risk sub-region 510; determine the regional monitoring heat 530 and regional reference degree 540 of the risk sub-region based on the updated monitoring location group 520; and determine to update the monitoring location 550 based on the regional monitoring heat 530 and regional reference degree 540. For instructions on updating the monitoring location, please refer to Figure 2 and its related descriptions.

[0197] The updated monitoring location group 520 is a location group consisting of multiple candidate monitoring locations. For more information about the candidate monitoring locations, see Figure 2 and its related descriptions.

[0198] In some embodiments, the processor may randomly generate multiple groups of updated monitoring positions in the risk sub-region based on the risk sub-region, wherein the number of candidate monitoring positions in each group of updated monitoring positions is the same as the number of target mobile radars.

[0199] The regional monitoring heat 530 refers to the heat value of the wind resource assessment monitoring of the risk sub-region in the monitoring area after the target mobile radar is adjusted based on the updated monitoring position group.

[0200] In some embodiments, the processor may determine the regional monitoring heat based on the updated monitoring position group. For example, the processor may determine the average of the grid monitoring heat corresponding to the unit grids included in the risk sub-area after adjusting the target mobile radar based on the updated monitoring position group as the regional monitoring heat. For relevant instructions on grid monitoring heat, please refer to Figure 5 Related description in the previous article.

[0201] When calculating the regional monitoring heat, the target wind measurement equipment includes a target mobile radar adjusted to a candidate monitoring position in the updated monitoring position group.

[0202] Regional reference refers to the reference of wind measurement data in the risk sub-area to the wind resources in the monitoring area after the target mobile radar is adjusted based on the updated monitoring position group.

[0203] In some embodiments, the processor may determine the regional reference degree based on the updated monitoring location group. For example, the processor may adjust the target mobile radar based on the updated monitoring location group and simulate the wind measurement data and working environment data in the risk sub-area through a CFD model to obtain the regional reference degree.

[0204] The wind measurement data in the risk sub-area includes the wind measurement data of the target mobile radar adjusted to the candidate monitoring position of the updated monitoring position group in the risk sub-area. The determination method of the regional reference degree is similar to the determination method of the local reference degree, which can be seen in Figure 3 and its related descriptions.

[0205] In some embodiments, the processor may determine, based on the regional monitoring heat and the regional reference degree, a candidate monitoring location in the corresponding updated monitoring location group when the regional monitoring heat and the regional reference degree satisfy a fourth preset condition as an updated monitoring location.

[0206] The fourth preset condition refers to a pre-set condition for determining an updated monitoring location based on the regional monitoring heat level and the regional reference level. Exemplary fourth preset conditions may include: the regional monitoring heat level is greater than a third heat threshold, and the regional reference level is greater than a reference level threshold. The third heat threshold and the reference level threshold may be system-based or manually preset.

[0207] In some embodiments of this specification, the updated monitoring position is determined by the regional reference degree and the regional monitoring heat. After the target mobile radar is adjusted to the updated monitoring position, the regional reference degree and the regional monitoring heat of the risk sub-area can be ensured to meet the requirements, thereby improving the effect of determining the updated monitoring position.

[0208] In some embodiments of the present specification, the updated monitoring position is determined based on the risk sub-area, and the target mobile radar can be adjusted to an area that needs to be monitored and where wind resource assessment and monitoring are insufficient. This improves the monitoring reference of the target mobile radar while ensuring that more abundant wind measurement data is obtained in the monitoring area, which helps to conduct more comprehensive wind resource assessment and monitoring of the monitoring area.

[0209] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0210] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0211] In addition, unless expressly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and laminar flow hoods in this specification. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0212] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0213] In some embodiments, numbers are used to describe the quantity of components and attributes. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may change according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the settings of such numerical values are as accurate as possible within the feasible range.

[0214] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, and documents, cited in this specification is hereby incorporated by reference in its entirety. This includes application history documents that are inconsistent with or conflict with the content of this specification, as well as documents (currently or subsequently attached to this specification) that limit the broadest scope of the claims of this specification. It should be noted that if the descriptions, definitions, and / or terminology used in the accompanying materials are inconsistent or conflicting with the content of this specification, the descriptions, definitions, and / or terminology used in this specification will control.

[0215] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A laser wind radar assisted control system, characterized in that: include: a data collection module configured to obtain wind measurement data and working environment data of wind measurement equipment in a monitoring area, wherein the wind measurement equipment includes a laser radar and a wind tower, and the laser radar includes at least one of a fixed radar and a mobile radar; and the working environment data includes site configuration information and / or overall environmental information; The data analysis module is configured as follows: Determining a target wind tower corresponding to the laser radar based on the working environment data; the target wind tower refers to the wind tower closest to the laser radar; Determining wind measurement similarity based on the wind measurement data of the laser radar and the wind measurement data of the target wind measurement tower; the wind measurement similarity refers to the degree of similarity between the wind measurement data of the laser radar and the wind measurement data of the target wind measurement tower; Determining a monitoring reference degree according to a first preset rule based on the wind measurement similarity and the distance between the laser radar and the target wind measurement tower; The first preset rule includes: the higher the wind measurement similarity and the smaller the distance, the smaller the monitoring reference corresponding to the laser radar; the monitoring reference refers to the reference of the wind measurement data obtained by the laser radar to the evaluation of wind resources in the monitoring area; The monitoring and adjustment module is configured as follows: determining a target mobile radar based on the monitoring reference degree; Dividing the monitoring area into a plurality of unit grids; determining the grid monitoring heat corresponding to the plurality of unit grids based on the site configuration information and the monitoring reference degree; Determining expected heat corresponding to the plurality of unit grids based on the overall environmental information; Determining a risk sub-area based on the grid monitored heat and the expected heat, and determining a regional monitored heat and a regional reference degree for the risk sub-area; Based on the area monitoring heat and the area reference degree, an updated monitoring position of the target mobile radar is determined.

2. The system according to claim 1, wherein: The monitoring reference level includes a monitoring reference level of a radar without abnormalities; and the data analysis module is further configured to: Based on the wind measurement data, determining that there is no abnormal radar; Grouping the radars without abnormalities to determine wind measurement groups; Determining a local reference degree of the wind measurement group based on the wind measurement data corresponding to the wind measurement group, the wind measurement data corresponding to the wind measurement tower, and the working environment data; The local reference degree refers to the reference degree of all wind measurement data in the wind measurement group for evaluating the wind resources in the entire monitoring area; in order to determine the local reference degree, the data analysis module is further configured to: Based on all the wind measurement data and the working environment data in the monitoring area, simulating the wind resource conditions of each grid point in the monitoring area through a fluid dynamics simulation model; Based on the wind measurement data corresponding to each wind measurement group, the wind measurement data corresponding to the wind measurement tower, and the working environment data, simulating the wind resource conditions of each grid point of each wind measurement group in the same monitoring area through a fluid dynamics simulation model; Determining the similarity of wind resource conditions predicted by two methods, and determining the similarity as the local reference degree of the wind measurement group; Based on the local reference degree, a monitoring reference degree of the radar without abnormality is determined.

3. A laser wind radar assisted control method, characterized in that: Executed by the processor, including: Obtaining wind measurement data and working environment data of wind measurement equipment within the monitoring area, wherein the wind measurement equipment includes a laser radar and a wind tower, and the laser radar includes at least one of a fixed radar and a mobile radar, and the working environment data includes site configuration information and / or overall environmental information; Determining a target wind tower corresponding to the laser radar based on the working environment data; the target wind tower refers to the wind tower closest to the laser radar; Determining wind measurement similarity based on the wind measurement data of the laser radar and the wind measurement data of the target wind measurement tower; the wind measurement similarity refers to the degree of similarity between the wind measurement data of the laser radar and the wind measurement data of the target wind measurement tower; Based on the wind measurement similarity and the distance between the laser radar and the target wind measurement tower, a monitoring reference degree is determined according to a first preset rule; the first preset rule includes: the higher the wind measurement similarity and the smaller the distance, the smaller the monitoring reference degree corresponding to the laser radar; the monitoring reference degree refers to the reference degree of the wind measurement data obtained by the laser radar for evaluating the wind resources in the monitoring area; determining a target mobile radar based on the monitoring reference degree; Dividing the monitoring area into a plurality of unit grids; determining the grid monitoring heat corresponding to the plurality of unit grids based on the site configuration information and the monitoring reference degree; Determining expected heat corresponding to the plurality of unit grids based on the overall environmental information; Determining a risk sub-area based on the grid monitored heat and the expected heat, and determining a regional monitored heat and a regional reference degree for the risk sub-area; Based on the area monitoring heat and the area reference degree, an updated monitoring position of the target mobile radar is determined.

4. The method according to claim 3, characterized in that The determining of the monitoring reference degree of the laser radar based on the wind measurement data and the working environment data includes: Based on the wind measurement data, determining that there is no abnormal radar; Grouping the radars without abnormalities to determine wind measurement groups; Based on the wind measurement data corresponding to the wind measurement group, the wind measurement data corresponding to the wind measurement tower, and the working environment data, a local reference degree of the wind measurement group is determined; the local reference degree refers to the reference degree of all wind measurement data in the wind measurement group for evaluating the wind resources in the entire monitoring area; in order to determine the local reference degree, the data analysis module is further configured to: Based on all the wind measurement data and the working environment data in the monitoring area, simulating the wind resource conditions of each grid point in the monitoring area through a fluid dynamics simulation model; Based on the wind measurement data corresponding to each wind measurement group, the wind measurement data corresponding to the wind measurement tower, and the working environment data, simulating the wind resource conditions of each grid point of each wind measurement group in the same monitoring area through a fluid dynamics simulation model; Determining the similarity of wind resource conditions predicted by two methods, and determining the similarity as the local reference degree of the wind measurement group; Based on the local reference degree, a monitoring reference degree of the radar without abnormality is determined.

5. A laser wind radar auxiliary control device, characterized in that: The apparatus comprises at least one processor and at least one memory; The at least one memory is for storing computer instructions; The at least one processor is configured to execute at least part of the computer instructions to implement the laser wind radar assisted control method according to any one of claims 3 to 4.

6. A computer-readable storage medium, characterized in that The storage medium stores computer instructions. When a computer reads the computer instructions, the computer executes the laser wind radar assisted control method according to any one of claims 3 to 4.

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