System, method and device for adjusting monitoring parameters of laser wind finding radar and medium

Abstract

The embodiment of the invention provides a system for adjusting monitoring parameters of a laser wind-finding radar, and the system comprises a data collection module which is configured to obtain wind-finding data and working environment data of wind-finding equipment in a monitoring area, the wind-finding equipment comprises a laser radar and a wind-finding tower, the laser radar comprises at least one of a fixed radar and a mobile radar, and the wind-finding tower comprises at least one of the fixed radar and the mobile radar; the working environment data comprises site configuration information and / or overall environment information; the data analysis module is configured to determine no abnormal radar based on the wind measurement data; grouping non-abnormal radars, and determining wind measurement groups; determining 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; carrying out weighted summation on the local reference degree of the wind measurement group, and determining the monitoring reference degree of the non-abnormal radar; and the monitoring and adjusting module is configured to determine the to-be-adjusted radar and the adjusting parameters corresponding to the to-be-adjusted radar based on the monitoring reference degree and the working environment data.

Description

Case Analysis

[0001] This application is a divisional application of Chinese application filed on June 3, 2024, with application number 202410709507.8, entitled "A laser wind measurement radar-assisted control system, method, device and storage medium". Technical Field

[0002] This specification relates to the field of laser wind radar technology, and in particular to a system, method, device and medium for adjusting the monitoring parameters of a laser wind radar. Background Technology

[0003] Laser wind radar is a novel mobile wind measurement technology that utilizes the Doppler frequency shift principle of lasers. By measuring the frequency changes caused by light waves reflecting off the radar and encountering aerosol particles moving in the air, it can acquire wind data and calculate vector wind speed and direction at corresponding heights to assess wind resources in the monitored area. However, during use, the lack of monitoring of the laser wind radar's operational status means that wind data acquired when the radar is malfunctioning has lower reliability for wind resource assessment, potentially leading to inaccurate assessments of wind resources in the monitored area.

[0004] To improve the accuracy of wind resource assessment, CN107807367B provides a coherent wind-measuring lidar device. By emitting multi-wavelength laser signals, the output power of the laser signal in the lidar is increased, thereby improving the signal-to-noise ratio of the lidar echo signal and ultimately enhancing the lidar's detection performance. While this device improves the detection performance and accuracy of wind measurement data to some extent, it still cannot monitor the operating status of the lidar in real time or adjust it when the lidar's operating status is abnormal.

[0005] Therefore, it is desirable to provide a system, method, device, and medium for adjusting the monitoring parameters of a laser wind measuring radar, which can help improve the auxiliary control of the laser wind measuring radar and thus accurately assess and reflect the wind resources of the monitored area. Summary of the Invention

[0006] One embodiment of this specification provides a system for adjusting the monitoring parameters of a laser wind-measuring radar, comprising: a data collection module configured to acquire wind measurement data and operating environment data from wind-measuring equipment within a monitoring area, wherein the wind-measuring equipment includes a laser radar and a wind-measuring tower, the laser radar includes at least one of a fixed radar and a mobile radar, and the operating environment data includes site configuration information and / or overall environmental information; a data analysis module configured to: identify radars without anomalies based on the wind measurement data; group the radars without anomalies into 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-measuring tower, and the operating environment data; perform a weighted summation of the local reference degrees of the wind measurement groups to determine the monitoring reference degree of the radars without anomalies, wherein the weight of the local reference degree of the wind measurement group is related to the importance of the sub-region corresponding to the wind measurement group during the weighted summation; and a monitoring adjustment module 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 operating environment data.

[0007] One embodiment of this specification provides a method for adjusting the monitoring parameters of a laser wind-measuring radar, characterized in that it is executed by a processor and includes: acquiring wind measurement data and operating environment data of wind-measuring equipment within a monitoring area, wherein the wind-measuring equipment includes a laser radar and a wind-measuring tower, the laser radar includes at least one of a fixed radar and a mobile radar, and the operating environment data includes site configuration information and / or overall environmental information; based on the wind measurement data, identifying radars without anomalies; grouping the radars without anomalies into wind measurement groups; determining 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-measuring tower, and the operating environment data; performing a weighted summation of the local reference degrees of the wind measurement groups to determine the monitoring reference degree of the radars without anomalies, wherein during the weighted summation, the weight of the local reference degree of the wind measurement group is related to the importance of the sub-region corresponding to the wind measurement group; and determining the radar to be adjusted and the adjustment parameters corresponding to the radar to be adjusted based on the monitoring reference degree and the operating environment data.

[0008] One embodiment of this specification provides an apparatus for adjusting the monitoring parameters of a laser wind measuring radar. The apparatus includes at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least a portion of the computer instructions to implement a method for adjusting the monitoring parameters of the laser wind measuring radar.

[0009] One embodiment of this specification provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions, the computer executes a method for adjusting the monitoring parameters of a laser wind-measuring radar. Attached Figure Description

[0010] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein: Figure 1 This is an exemplary block diagram of a laser wind-measuring radar-assisted control system according to some embodiments of this specification; Figure 2 This is an exemplary flowchart of a laser wind-measuring radar-assisted control method according to some embodiments of this specification; Figure 3 This is an exemplary schematic diagram illustrating the determination of a monitoring reference level according to some embodiments of this specification; Figure 4 This is an exemplary schematic diagram of an anomaly analysis model according to some embodiments of this specification; Figure 5 This is an exemplary schematic diagram illustrating the determination of updated monitoring locations according to some embodiments of this specification. Detailed Implementation

[0011] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0012] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0013] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0014] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0015] In wind measurement technology, wind measurement towers are expensive and difficult to relocate. In monitoring areas with complex terrain, wind measurement towers are often insufficient to meet wind resource assessment requirements. Compared to wind measurement towers, laser wind measurement radar has significant advantages in wind farm wind measurement technology, and combining the two can effectively monitor and assess wind resources. However, the lack of monitoring of the laser wind measurement radar's operating status may lead to inaccurate wind resource assessment results. CN107807367B improves the performance of wind measurement laser radar by emitting multi-wavelength laser signals, but it cannot judge, process, or adjust the laser wind measurement radar's operating status in a timely manner. Some embodiments in this specification determine the monitoring reference degree of the laser radar based on wind measurement data and working environment data, and then determine the radar to be adjusted and the corresponding adjustment parameters. This allows for monitoring and adjustment of the laser radar's operating status, ensuring that the laser wind measurement radar acquires richer wind measurement data than wind measurement towers, which helps to more accurately reflect and assess the wind resources of the monitoring area.

[0016] Figure 1 These are exemplary block diagrams of a laser wind-measuring radar-assisted control system according to some embodiments of this specification. Figure 1 As shown, the laser wind radar auxiliary control system 100 may include a data collection module 110, a data analysis module 120, and a monitoring and adjustment module 130.

[0017] In some embodiments, the data collection module 110 can be configured to acquire wind measurement data and working environment data of the wind measurement equipment within the monitoring area.

[0018] In some embodiments, the data analysis module 120 can be configured to determine the monitoring reference degree of the lidar based on wind measurement data and working environment data.

[0019] In some embodiments, the data analysis module 120 may be further configured to: determine the radars without anomalies based on wind measurement data; group the radars without anomalies into wind measurement groups; determine the local reference degree of the wind measurement groups based on the wind measurement data corresponding to the wind measurement groups, the wind measurement data corresponding to the wind measurement towers, and the working environment data; and determine the monitoring reference degree of the radars without anomalies based on the local reference degree.

[0020] In some embodiments, the monitoring and adjustment module 130 can be configured to determine the radar to be adjusted and the corresponding adjustment parameters of the radar to be adjusted based on the monitoring reference degree and the operating environment data.

[0021] In some embodiments, the monitoring and adjustment module 130 may be further configured to: determine the target moving radar based on the monitoring reference degree; and determine the updated monitoring position of the target moving radar based on the operating environment data.

[0022] In some embodiments, the monitoring adjustment module 130 may be further configured to: determine risk sub-regions of the monitoring area based on working environment data and monitoring reference levels; and determine updated monitoring locations based on the risk sub-regions.

[0023] In some embodiments, the laser wind radar-assisted control system 100 may include a processor. The processor may be used to process information and / or data related to the laser wind radar-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 this data, information, and / or processing results to perform one or more functions described herein.

[0024] 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.

[0025] In some embodiments, the laser wind-measuring radar-assisted control system 100 may include a network. The processor can acquire data and / or information related to the laser wind-measuring radar-assisted control system 100 via the network.

[0026] In some embodiments, the laser wind-measuring 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 the administrator or operator of the laser wind-measuring radar-assisted control system 100, etc. For example, a user terminal may include a mobile phone, tablet computer, interactive screen, etc.

[0027] Further details regarding the data collection module 110, data analysis module 120, and monitoring and adjustment module 130 can be found in the following description.

[0028] It should be noted that the above description of the laser wind-measuring radar-assisted control system 100 and its modules is for ease of description only and should not be construed as limiting this specification to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principles of this system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from these principles.

[0029] Figure 2 This is an exemplary flowchart of a laser wind-measuring radar-assisted control method according to some embodiments of this specification. In some embodiments, process 200 can be executed by a processor of the laser wind-measuring radar-assisted control system. Figure 2 As shown, process 200 includes the following steps: Step 210: Obtain wind measurement data and working environment data from the wind measurement equipment within the monitoring area.

[0030] The monitoring area refers to the area where wind resource assessment and monitoring are 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 measurement equipment is located, and the radiation area corresponding to the wind measurement equipment. For relevant information on the radiation area, please refer to [link to relevant documentation]. Figure 5 And related descriptions. In some embodiments, the size and extent of the monitoring area may be determined by a technician.

[0031] Wind measurement equipment refers to equipment used for wind resource assessment and monitoring, and can be used to acquire wind measurement data. In some embodiments, wind measurement equipment may include lidar and wind measurement towers.

[0032] LiDAR refers to a laser-based wind-measuring radar that uses laser signals to assess and monitor wind resources. In some embodiments, lidar may include at least one of fixed radar and mobile radar.

[0033] Fixed radar refers to a fixed lidar system installed on a ground-based observation platform. A ground-based observation platform refers to a surface meteorological observation platform with a fixed location.

[0034] Mobile radar refers to a portable lidar system mounted on a mobile loading platform. A mobile loading platform refers to a mobile meteorological observation platform used to mount mobile radar.

[0035] A wind measurement tower is a tower structure used for wind resource assessment and monitoring.

[0036] 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 historical preset time period. The historical preset time period refers to a predetermined period within historical time. The historical preset time period can be based on historical experience, such as one day.

[0037] Operating environment data refers to data related to the operating environment of the wind measurement equipment. In some embodiments, operating environment data may include site configuration information and / or overall environmental information.

[0038] Site configuration information refers to the configuration information related to the lidar. For example, site configuration information may include the lidar's location, altitude, transmission window height, lidar type, detection length, etc.

[0039] The location information of the lidar refers to information related to its location, such as its latitude and longitude. The transmission window height refers to the height of the window through which the lidar transmits its radar signal. The radar type refers to the type of radar signal emitted by the lidar, such as pulse coherent, continuous wave coherent, or other types. The detection length refers to the maximum spatial distance over which the lidar can acquire accurate wind measurement data under normal operating conditions.

[0040] 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 measurement tower.

[0041] The location information of the monitoring area refers to information related to the location of the monitoring area, such as the latitude and longitude range of the monitoring area. Topographic information refers to information related to the topography of the monitoring area; for example, topographic information can be a 3D model constructed based on surveying information of the monitoring area. The location information of the wind measuring tower refers to information related to the location of the wind measuring tower, such as the latitude and longitude of the tower's location.

[0042] In some embodiments, the processor can acquire wind measurement data from the anemometer in various ways. For example, the processor can communicate with the anemometer via a network to acquire the wind measurement data monitored by the anemometer.

[0043] In some embodiments, the processor can acquire working environment data in various ways. For example, the processor can acquire working environment data stored in a storage device. Another example is that the processor can connect to a user terminal via a network to acquire working environment data input by the user. Yet another example is that the processor can acquire the location information of the lidar and the wind measurement tower from the working environment data through positioning devices such as positioning chips and sensors deployed on the wind measurement equipment.

[0044] Step 220: Determine the monitoring reference degree of the lidar based on wind measurement data and working environment data.

[0045] The monitoring reference degree refers to the reference degree of wind measurement data acquired by lidar for assessing wind resources in the monitored area.

[0046] In some embodiments, the processor can determine the monitoring reference level in a variety of ways based on wind measurement data and operating environment data.

[0047] In some embodiments, the processor can determine the target wind measurement tower corresponding to the lidar based on working environment data; determine the wind measurement similarity based on the wind measurement data of the lidar and the wind measurement data of the target wind measurement tower; and determine the monitoring reference degree based on the wind measurement similarity and the distance between the lidar and the target wind measurement tower through a first preset rule.

[0048] The target wind measurement tower refers to the wind measurement tower that is closest to the lidar. In some embodiments, the processor can determine the wind measurement tower that is closest to the lidar as the target wind measurement tower based on the location information of the lidar and the location information of the wind measurement tower in the working environment data.

[0049] Wind measurement similarity refers to the degree of similarity between wind measurement data from a lidar system and wind measurement data from a target wind measurement tower. In some embodiments, the processor can determine the wind measurement similarity based on the lidar wind measurement data and the target wind measurement tower's wind measurement data using similarity calculation methods such as Euclidean distance and cosine similarity.

[0050] The first preset rule refers to the pre-set rules used to determine the monitoring reference degree. When the distance between the lidar and the target wind measurement tower is greater and the wind measurement similarity is lower, the wind measurement data of the lidar can be used as an auxiliary supplement to the wind measurement data of the wind measurement tower when assessing the wind resources in the monitoring area, and the monitoring reference degree of the lidar will increase accordingly.

[0051] An exemplary first preset rule could be: the higher the wind measurement similarity and the smaller the distance between the lidar and the target wind measurement tower, the lower the monitoring reference value corresponding to that lidar. For example, the first preset rule could be the following formula: Monitoring Reference Value = k1 / Wind Measurement Similarity + k2 Distance between the lidar and the target wind measurement tower.

[0052] Among them, k1 and k2 are calculation coefficients, and their specific values ​​can be preset based on historical experience.

[0053] When the similarity of wind measurements and the distance to the target wind measurement tower are the same for fixed and mobile radars, fixed radars are preferred in practice to assess wind resources in the monitoring area because they cannot be moved. This is to facilitate the improvement of the monitoring reference value of mobile radars by moving them during subsequent assessment and monitoring.

[0054] In some embodiments, the values ​​of k1 and k2 for fixed radar and mobile radar are different. When the values ​​of k1 and k2 for fixed radar and mobile radar are preset, the values ​​of k1 and k2 for fixed radar are higher than those for mobile radar.

[0055] In some embodiments, the processor can determine the anomaly-free radars based on wind measurement data and determine the monitoring reference degree of the anomaly-free radars based on the local reference degree of the wind measurement groups. For more details, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0056] Step 230: Based on the monitoring reference degree and working environment data, determine the radar to be adjusted and the corresponding adjustment parameters of the radar to be adjusted.

[0057] Adjustment parameters refer to the relevant parameters used to adjust the radar to be adjusted. In some embodiments, the adjustment parameters may include the updated monitoring position and updated 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 the updated monitoring parameters.

[0058] Updating the monitoring location refers to the location where wind resource assessment monitoring is conducted after the radar to be adjusted has been adjusted. Updating the monitoring parameters refers to the monitoring parameters of the radar to be adjusted after the radar to be adjusted has been adjusted.

[0059] Monitoring parameters refer to parameters related to wind resource assessment and monitoring using lidar. For example, monitoring parameters may include distance gates, system parameters, and scanning parameters. The distance gate refers to the lidar's ability to distinguish between two different objects; the lidar can differentiate between two different objects separated by a distance not less than the distance gate. System parameters refer to parameters related to the lidar's system operation, such as the lidar's laser pulse width. Scanning parameters refer to parameters related to the lidar's scanning operation; for example, scanning parameters may include different scanning modes and the number of scan points. Exemplary scanning modes may include ring discrete, ring continuous, sector discrete, and sector continuous.

[0060] In some embodiments, the processor can determine the radar to be adjusted and its corresponding adjustment parameters in a variety of ways based on monitoring reference degree and operating environment data.

[0061] For example, the processor can identify a fixed radar whose monitoring reference degree 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.

[0062] The first preset condition refers to the pre-set judgment condition used to determine whether a fixed radar is the radar to be adjusted. The first preset condition can be preset by the system or manually. An example first preset condition can be: the monitoring reference degree of the fixed radar is lower than a first threshold. The first threshold refers to the lowest value of the monitoring reference degree of the fixed radar under normal operating conditions. The first threshold can be preset by the system or manually.

[0063] The first preset table may include the correspondence between different altitudes, different transmission window heights, different radar types, and different updated monitoring parameters for fixed radars in the working environment data. The first preset table can be preset based on historical experience or historical data.

[0064] For example, the processor can identify 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 similar way to the aforementioned determination of the updated monitoring parameters of a fixed radar; and upload the monitoring reference degree and working environment data of the radar to be adjusted to the user terminal, so that technicians can determine the updated monitoring position in the adjustment parameters of the radar to be adjusted.

[0065] The second preset condition refers to the pre-set judgment condition used to determine whether a mobile radar is a radar to be adjusted. The second preset condition can be preset by the system or manually. An example second preset condition can be: the monitoring reference degree of the mobile radar is lower than the highest value of its monitoring reference degree when the mobile radar was repeatedly identified as a radar to be adjusted in historical data.

[0066] In some embodiments, the processor can determine the target moving radar based on the monitoring reference level and determine the updated monitoring location of the target moving radar based on the operating environment data. Further details regarding the updated monitoring location can be found in [link to relevant documentation]. Figure 2 The relevant descriptions in the preceding text.

[0067] Target moving radar refers to a moving radar that has been identified as the radar to be adjusted.

[0068] In some embodiments, the processor can determine the target mobile radar based on the monitoring reference level in various ways. For example, the processor can determine a mobile radar with a monitoring reference level below a second threshold as the target mobile radar. The second threshold refers to the lowest monitoring reference level of the mobile radar under normal operating conditions, and the second threshold can be preset by the system or manually.

[0069] In some embodiments, the processor can determine the updated monitoring location of the target moving radar based on the operating environment data and a second preset rule.

[0070] The second preset rule refers to a pre-set rule used to determine the updated monitoring location of the target moving 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.

[0071] Step S11: Generate multiple candidate monitoring locations in the monitoring area.

[0072] Candidate monitoring locations refer to locations within the monitoring area that may be used as updated monitoring locations. In some embodiments, there are no wind measuring devices within a first preset distance of a candidate monitoring location, and the distance between any two candidate monitoring locations is greater than a second preset distance. Both the first and second preset distances can be preset based on historical experience.

[0073] In some embodiments, the processor can randomly generate multiple location points within the monitoring area, and determine the location points that have no wind measuring equipment within a first preset distance and whose distance from other location points is greater than a second preset distance as candidate monitoring locations. It should be noted that the number of candidate monitoring locations is greater than or equal to the number of target moving radars within the monitoring area.

[0074] Step S12: For each candidate monitoring location, determine the minimum distance between the candidate monitoring location and the wind measurement equipment based on the working environment data.

[0075] In some embodiments, the processor can determine the distance between the candidate monitoring location and all the wind measuring devices based on the location information of the wind measuring devices in the candidate monitoring location and 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.

[0076] Step S13: Based on the minimum distance corresponding to the candidate monitoring locations, arrange multiple candidate monitoring locations, and determine the updated monitoring location based on the arrangement order of the candidate monitoring locations.

[0077] In some embodiments, the processor can arrange multiple candidate monitoring locations based on the minimum distance value corresponding to each candidate monitoring location; for example, the larger the corresponding minimum distance value, the higher the ranking.

[0078] The processor can randomly assign candidate monitoring positions from the top number of target moving radars to the target moving radars. The candidate monitoring position assigned to the corresponding target moving radar becomes the updated monitoring position for that target moving radar. Here, "target number" refers to the number of target moving radars.

[0079] In some embodiments, the processor can determine the updated monitoring location based on the risk sub-region; for more details, please refer to [link to relevant documentation]. Figure 5 And its related descriptions.

[0080] Some embodiments in this specification determine the target moving radar based on the monitoring reference degree and determine the updated monitoring position based on the working environment data. This can keep a certain distance between the determined updated monitoring position and the wind measuring equipment, thereby ensuring that the target moving radar improves its corresponding monitoring reference degree when it is adjusted to the updated monitoring position. This is beneficial for making full use of the wind measuring data acquired by the target moving radar to evaluate and monitor the wind resources in the monitoring area.

[0081] Some embodiments in this specification determine the monitoring reference degree of the lidar by using wind measurement data and working environment data, and then determine the lidar to be adjusted and its corresponding adjustment parameters. When the wind measurement data acquired by the lidar to be adjusted has little reference degree for the assessment and monitoring of wind resources in the monitoring area, the lidar to be adjusted can be adjusted by adjusting the parameters, thereby improving the monitoring reference degree of the lidar to be adjusted. This helps to obtain richer wind measurement data and make a more accurate and comprehensive assessment of wind resources in the monitoring area.

[0082] It should be noted that the above description of the process is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to the process under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0083] Figure 3 This is an exemplary schematic diagram illustrating the determination of a monitoring reference level according to some embodiments of this specification.

[0084] In some embodiments, the processor can determine the anomaly-free radar 320 based on the wind measurement data 310; group the anomaly-free radars 320 to determine wind measurement groups 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 operating environment data 350; and determine the monitoring reference degree 370 of the anomaly-free radar based on the local reference degree 360. For relevant explanations regarding the acquisition of wind measurement data, operating environment data, monitoring reference degree, and wind measurement data corresponding to the wind measurement tower, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0085] The No Anomaly Radar 320 refers to a lidar that monitors wind measurement data accurately and without deviation.

[0086] In some embodiments, the processor can determine the absence of anomalies in lidar based on wind measurement data using various methods. For example, the processor can acquire the duration of invariance in various wind measurement data of the lidar, and determine lidars whose duration of invariance in all wind measurement data is less than a preset duration as anomaly-free lidar. The preset duration can be preset by the system or manually, for example, one hour. Various wind measurement data refer to different data types within the wind measurement data, such as wind speed, wind direction, air pressure, and relative humidity.

[0087] The duration of no change refers to the length of time 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 define the duration of no change as the length of time during which the maximum absolute deviation of the wind measurement data remains less than the deviation threshold.

[0088] Absolute deviation refers to the difference between an individual measurement and the average of multiple measurements. An individual measurement can refer to one of multiple wind measurement data. The average of multiple measurements refers to the average of multiple wind measurement data. The maximum absolute deviation is the largest of the absolute deviations corresponding to each wind measurement data point.

[0089] A deviation threshold is a pre-set threshold condition used to determine whether wind measurement data remains unchanged. The deviation threshold can be preset based on historical experience. In some embodiments, the deviation threshold may differ depending on the data type of the wind measurement data. For example, the deviation threshold for wind speed may be 0.1 m / s, the deviation threshold for wind direction may be 0.5°, the deviation threshold for air pressure may be 0.05 hPa, and the deviation threshold for relative humidity may be 0.05%, etc.

[0090] In some embodiments, the processor can also determine radars without anomalies using an anomaly analysis model; for more details, please refer to [link to relevant documentation]. Figure 4 Related descriptions.

[0091] Wind measurement group 330 refers to the grouping obtained after grouping the anomaly-free radars. In some embodiments, an anomaly-free radar may be in multiple wind measurement groups.

[0092] In some embodiments, the processor can obtain wind measurement groups in various ways. For example, the processor can randomly group multiple anomaly-free radars. While randomly grouping the anomaly-free radars, the processor can also impose restrictions on the groups, such as limiting the number of anomaly-free radars in each wind measurement group or limiting the total number of wind measurement groups. Specific restrictions can be preset by the system or manually.

[0093] In some embodiments, a wind measurement group corresponds to a wind measurement sub-region. A wind measurement sub-region refers to the monitoring area comprised of the anomaly-free radars within the wind measurement group. In some embodiments, the processor can determine the wind measurement sub-region as the intersection of the radiation areas of all anomaly-free radars within the wind measurement group. Further explanation regarding radiation areas can be found in [link to relevant documentation]. Figure 5 And its related descriptions.

[0094] The wind measurement data 340 corresponding to the wind measurement group refers to the wind measurement data corresponding to all radars without anomalies within the wind measurement group.

[0095] In some embodiments, the processor can determine the anomaly-free radars within the wind measurement group 330 based on the wind measurement group 330, and determine all wind measurement data corresponding to the anomaly-free radars within the wind measurement group as the wind measurement data 340 corresponding to the wind measurement group based on the wind measurement data 310-1 corresponding to the lidar. For information on obtaining the wind measurement data corresponding to the lidar, please refer to [link to relevant documentation]. Figure 2 The relevant description of step 210.

[0096] Local reference degree 360 ​​refers to the reference degree of all wind measurement data in the wind measurement group for assessing wind resources in the entire monitoring area.

[0097] In some embodiments, the processor can determine the local reference degree 360 ​​of the wind measurement group in various 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.

[0098] In some embodiments, the processor can simulate the wind resource situation at each grid point in the monitoring area using a Computational Fluid Dynamics (CFD) simulation model, based on all wind measurement data 310 and working environment data 350 within the monitoring area. Based on the wind measurement data 340 corresponding to each wind measurement group, the wind measurement data 310-2 corresponding to the wind measurement tower, and the working environment data 350, the processor can simulate the wind resource situation at each grid point in the same monitoring area for each wind measurement group using a CFD simulation model. The processor then determines the similarity between the wind resource situations predicted by the two methods and uses this similarity as the local reference degree of the wind measurement group. In some embodiments, the processor can determine the similarity of the wind resource situations based on multiple methods such as Euclidean distance and cosine similarity.

[0099] Here, a grid point refers to a single region after the monitoring area has been divided. In some embodiments, the processor can uniformly divide the monitoring area to determine multiple grid points of the same size. The size of the grid point can be preset by the system; for example, a grid point can be 50m × 50m in size.

[0100] In some embodiments, the processor can determine the monitoring reference degree of the anomaly-free radar in various ways based on the local reference degree. For example, the processor can determine the monitoring reference degree of the anomaly-free radar as the average of the local reference degrees of all wind measurement groups to which the anomaly-free radar belongs.

[0101] In some embodiments, the processor can perform a weighted summation of the local reference degrees to determine the monitoring reference degree of the radar without anomalies.

[0102] In some embodiments, the processor may assign different weights to each local reference degree in the wind measurement group where the anomaly-free radar is located, and determine the result of the weighted summation as the monitoring reference degree of the anomaly-free radar.

[0103] 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.

[0104] Subregion importance refers to the degree of importance of the wind measurement subregion corresponding to the wind measurement group.

[0105] In some embodiments, the processor can determine the importance of a sub-region based on its area, center location, and number of meteorological towers it contains, using a third preset rule.

[0106] The center location of the wind measurement sub-region refers to the geographical center location of the wind measurement sub-region corresponding to the wind measurement group. In some embodiments, the processor can determine the location of the wind measurement sub-region based on the location information of the radar without abnormalities in the wind measurement group and the corresponding radiation area, and determine the center location of the wind measurement sub-region by geometric mean method, least circumcircle method or other feasible methods.

[0107] In some embodiments, the processor can determine the area of ​​the wind measurement sub-region corresponding to the wind measurement group based on the location of the wind measurement sub-region, using Gaussian area calculation or other feasible area calculation methods. In some embodiments, the processor can determine the number of wind measurement towers contained in the wind measurement sub-region based on the location of the wind measurement sub-region and the location information of the wind measurement towers. The determination of the location information of the wind measurement towers can be found in [reference needed]. Figure 2 The relevant description of step 210.

[0108] The third preset rule refers to the pre-set rules used to determine the importance of a sub-region. An example third preset rule could be: the larger the area of ​​the wind measurement sub-region, the smaller the distance between the center of the wind measurement sub-region and the monitoring center, and the more wind measurement towers the wind measurement sub-region contains, the higher the importance of the corresponding sub-region.

[0109] The monitoring center refers to the center of the monitoring area, which can be determined by technical personnel. For example, technical personnel can determine the location with the richest wind resources obtained from preliminary surveys as the monitoring center. The processor can determine the distance between the center of the wind measurement sub-region and the monitoring center based on the location of the wind measurement sub-region and the location of the monitoring center.

[0110] For example, the third preset rule may include the following formula: Sub-region importance = a1 s+a2 / d.

[0111] Where s is the area of ​​the wind measurement sub-region, d is the distance between the center of the wind measurement sub-region and the monitoring center, and a1 and a2 are calculation coefficients.

[0112] In some embodiments, the processor can determine the values ​​of a1 and a2 corresponding to a wind-measuring sub-region based on the number of wind-measuring towers contained in the sub-region, through a first preset relationship. The first preset relationship refers to a pre-set correspondence between the number of wind-measuring towers contained in the sub-region and a1 and a2. An exemplary first preset relationship may be: the larger the number of wind-measuring towers contained in the sub-region, the larger the values ​​of a1 and a2 corresponding to the sub-region.

[0113] In some embodiments, the weight of the local reference degree of a wind measurement group can be positively correlated with the importance of the sub-region. In some embodiments, the processor can determine the weight corresponding to the local reference degree of the wind measurement group based on the importance of the sub-region using a fourth preset rule. The fourth preset rule refers to a pre-set rule used to determine the weight corresponding to the local reference degree of the wind measurement group. An exemplary fourth preset rule could be: the higher the importance of the sub-region corresponding to the wind measurement group, the higher the weight corresponding to the local reference degree of the wind measurement group.

[0114] For example, the fourth preset rule can be: the proportion of the sub-region importance corresponding to the wind measurement group in the sum of the sub-region importance of all wind measurement groups is determined as the weight corresponding to the local reference degree of the wind measurement group.

[0115] In some embodiments of this specification, by weighted summation of the local reference degrees of the wind measurement group to which the anomaly-free radar is located, the monitoring reference degree of the anomaly-free radar can be determined. This can assign higher weights to the local reference degrees of the wind measurement groups with high sub-region importance, which helps to improve the accuracy of determining the monitoring reference degree.

[0116] In some embodiments of this specification, anomaly-free radars are identified 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, thereby determining the monitoring reference degree. This approach is beneficial for comprehensively judging the monitoring reference degree of anomaly-free radars by fully combining wind measurement data from multiple wind measurement groups, which can more accurately determine the monitoring reference degree of anomaly-free radars and helps in the subsequent determination of the radars to be adjusted.

[0117] Figure 4 This is an exemplary schematic diagram of an anomaly analysis model shown according to some embodiments of this specification.

[0118] In some embodiments, the processor can determine the absence of anomalies in the radar 320 based on the wind measurement data 310 and the anomaly analysis model 420.

[0119] For more information on wind measurement data, anomaly-free radar, etc., please refer to [link / reference]. Figure 2 and Figure 3 Related descriptions.

[0120] Anomaly analysis model 420 is a model used to determine whether a lidar is malfunctioning.

[0121] In some embodiments, the anomaly analysis model can be a machine learning model, such as a neural network (NN) model.

[0122] In some embodiments, the input to the anomaly analysis model may include wind measurement data 310-1 corresponding to the lidar, wind measurement data 310-2 corresponding to the wind measurement tower, and device distance 410 in the wind measurement data 310, and the output may include a judgment result 430. Here, device distance 410 refers to the distance between the lidar and the wind measurement tower.

[0123] In some embodiments, the processor can obtain the location information of the lidar and the location information of the wind measurement tower corresponding to the wind measurement data input to the anomaly analysis model based on the working environment data, thereby determining the device distance. Further details regarding the working environment data can be found in [link to relevant documentation]. Figure 2 And its related descriptions.

[0124] Judgment result 430 refers to the judgment result on whether the lidar corresponding to the wind measurement data of the input anomaly analysis model is abnormal.

[0125] 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 the first label can be input into the initial anomaly analysis model. A loss function is constructed using the first label and the output of the initial anomaly analysis model. The parameters of the initial anomaly analysis model are iteratively updated based on the loss function using gradient descent or other methods. The model training is completed when a preset iteration condition is met, resulting in a trained anomaly analysis model. The preset iteration condition can be the convergence of the loss function, the number of iterations reaching a threshold, etc.

[0126] In some embodiments, the first training sample may include wind measurement data corresponding to the sample lidar, wind measurement data corresponding to the sample wind measurement tower, and the distance to the sample device. The first training sample may be obtained based on historical data.

[0127] In some embodiments, the first label may include a judgment result indicating whether the sample LiDAR corresponding to the first training sample is actually abnormal. The first label can be a value of 0 or 1, where a first label of 0 indicates that the corresponding sample LiDAR is abnormal, and vice versa. The first label may be manually labeled.

[0128] In some embodiments, the processor can determine the result of the user erroneously modifying the historical wind measurement data that has no abnormalities as the first training sample, and mark the first label corresponding to the first training sample as 0, so as to expand the first training sample and avoid the problem of imbalance of the first training sample.

[0129] In some embodiments, the processor can determine the radar without anomalies based on the judgment result. The processor can determine the lidar corresponding to the judgment result of no anomaly output by the anomaly analysis model as the radar without anomalies.

[0130] In some embodiments, the anomaly analysis model may include multiple sub-models, each corresponding to different types of wind measurement data. For a detailed explanation of wind measurement data and its different types, please refer to [link to relevant documentation]. Figure 2 and Figure 3 The relevant descriptions in the preceding text.

[0131] A sub-model is a model within the anomaly analysis model used to determine whether different types of wind measurement data acquired by lidar exhibit anomalies. For example, sub-model 1 can be used to analyze whether the wind speed in the wind measurement data is abnormal; sub-model 2 can be used to determine whether the wind direction in the wind measurement data is abnormal, and so on.

[0132] If no abnormalities are found in the different types of wind measurement data acquired by the lidar, then the corresponding lidar is considered an anomaly-free lidar.

[0133] In some embodiments, the processor can input different types of wind measurement data into the corresponding sub-model to determine whether different types of wind measurement data are abnormal, and determine the lidar with no abnormalities in different types of wind measurement data as an abnormal lidar.

[0134] Some embodiments in this specification improve the accuracy of identifying radars without anomalies by classifying and analyzing wind measurement data from lidar using different sub-models.

[0135] Some embodiments in this specification analyze wind measurement data corresponding to lidar, wind measurement data corresponding to wind measurement towers, and equipment distance using anomaly analysis models. By utilizing the self-learning capability of machine learning models, patterns can be found from a large amount of data to efficiently and accurately identify radars without anomalies.

[0136] Figure 5 This is an exemplary schematic diagram illustrating the determination of updated monitoring locations according to some embodiments of this specification.

[0137] In some embodiments, the processor may determine a risk sub-region 510 of the monitoring area based on the operating environment data 350 and the monitoring reference level 370, and determine an updated monitoring location 550 based on the risk sub-region 510. Further explanation of the operating environment data and the monitoring reference level can be found in [link to relevant documentation]. Figure 2 And its related descriptions.

[0138] Risk sub-region 510 refers to areas in the monitoring area where wind resource assessment and monitoring are insufficient.

[0139] In some embodiments, the processor can determine risk sub-regions in a variety of ways based on operating environment data and monitoring references.

[0140] In some embodiments, the processor can divide the monitoring area into multiple candidate risk areas, determine the radiation area of ​​the lidar within the monitoring area based on 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 with the candidate monitoring heat lower than the first heat threshold as the risk sub-region.

[0141] A candidate risk region refers to a region within the monitoring area that may be considered a risk sub-region. In some embodiments, the processor may randomly divide the monitoring area to obtain multiple candidate risk regions.

[0142] The radiation area of ​​a lidar refers to the range of wind data that the lidar can detect. In some embodiments, the processor can determine the radiation area of ​​the lidar as a circular region centered on the lidar and with the lidar's detection length as its radius, based on the lidar's location information and detection length in the working environment data.

[0143] Candidate monitoring heat refers to the heat value of a candidate risk area in the wind resource assessment and monitoring of the monitoring area. In some embodiments, the processor can determine the candidate monitoring heat of the candidate risk area based on the candidate risk area and the radiation area of ​​the lidar, by selecting the maximum value of the monitoring reference degree of all lidars in the radiation area that are partially or entirely located in the candidate risk area.

[0144] The first heat threshold refers to the threshold condition for judging risk sub-regions based on the heat of candidate monitoring. In some embodiments, the first heat threshold can be preset by the system or manually.

[0145] In some embodiments, the processor can divide the monitoring area into multiple unit grids; determine the grid monitoring heat corresponding to the multiple unit grids based on site configuration information and monitoring reference degree; and determine the risk sub-regions based on the grid monitoring heat.

[0146] A unit grid refers to a grid of a preset size within the monitoring area.

[0147] In some embodiments, the size of a unit grid can be preset by the system or by a user. For example, a 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.

[0148] The heat index of a geogrid monitoring unit refers to the heat index value of a geogrid unit in the wind resource assessment and monitoring of the monitoring area.

[0149] In some embodiments, the processor can determine the geogrid monitoring heat level based on site configuration information and monitoring reference level using a fifth preset rule. The fifth preset rule refers to a pre-set rule used to determine the geogrid monitoring heat level.

[0150] An exemplary fifth preset rule could be: determining the target wind measurement device corresponding to a unit grid based on the site configuration information, and determining the sum of the products of the radiation ratio of the target wind measurement device and the monitoring reference degree of the target wind measurement device as the grid monitoring heat.

[0151] For example, the fifth preset rule can be expressed by the following formula: Grid monitoring heat = .

[0152] in, This represents the emissivity of the i-th target wind measuring device per unit grid. The monitoring reference degree is the i-th target wind measurement device corresponding to the unit grid.

[0153] A target wind measuring device refers to a wind measuring device located partially or entirely within a unit grid within its radiation area. The radiation area of ​​a wind measuring tower refers to the range of wind data that the tower can detect. The processor can obtain the detection length of the wind measuring tower from storage devices or user input, and then determine the radiation area of ​​the tower. In some embodiments, the determination of the radiation area of ​​the wind measuring tower is similar to that of a lidar system; see [link to relevant documentation]. Figure 5 The relevant descriptions in the preceding text.

[0154] Radiation ratio refers to the proportion of radiation emitted by a target wind measuring device per unit grid cell. In some embodiments, the processor can determine the radiation ratio of the target wind measuring device per unit grid cell as the ratio of the area of ​​the radiation region of the target wind measuring device located within a unit grid cell to the area of ​​the unit grid cell.

[0155] When the target anemometer is a wind-measuring tower, the tower's monitoring reference value can be a default value, for example, it can be set by the system or manually. For information on determining the monitoring reference value of the lidar in the target anemometer, please refer to [link to relevant documentation]. Figure 2 and Figure 3 Related descriptions.

[0156] In some embodiments, the processor can define a region consisting of unit grids whose grid monitoring heat level is lower than a second heat level threshold as a risk sub-region. The second heat level threshold refers to a threshold condition for determining a risk sub-region based on grid monitoring heat level. In some embodiments, the second heat level threshold can be preset by the system or manually.

[0157] In some embodiments, the processor can also determine the expected heat corresponding to multiple unit grids based on overall environmental information; and determine risk sub-regions based on grid monitoring heat and expected heat.

[0158] Expected heat refers to the expected value of the heat value of a unit grid in the wind resource assessment and monitoring of the monitoring area.

[0159] In some embodiments, the processor can determine the expected heat of a unit grid in a variety of ways based on overall environmental information.

[0160] In some embodiments, the processor can determine the expected heat corresponding to a unit grid based on overall environmental information and unit grid information using an expected heat model.

[0161] Unit grid information refers to information related to a unit grid. For example, unit grid information may include the latitude and longitude range, altitude range, and altitude distribution corresponding to the unit grid.

[0162] In some embodiments, the processor can determine unit grid information based on overall environmental information and the divided unit grids. Here, altitude distribution refers to the proportion of altitude distribution within a unit grid across various altitude sub-ranges. Altitude sub-ranges can be preset by the system or manually. For example, altitude sub-ranges may include 1000m-1100m, 1100m-1200m, etc.

[0163] An expected popularity model is a model used to determine expected popularity. In some embodiments, the expected popularity model can be a machine learning model, such as a convolutional neural network (CNN).

[0164] In some embodiments, the input to the desired heat model may include overall environmental information and unit grid information, and the output may include the desired heat corresponding to the unit grid.

[0165] In some embodiments, the expected popularity model can be trained based on a second training sample with a second label. The training process of the expected popularity model is similar to that of the anomaly analysis model, and can be found in [reference needed]. Figure 4 And its related descriptions.

[0166] In some embodiments, the second training sample may include overall environmental information of the sample monitoring area and unit grid information of the sample unit grid. The second training sample may be obtained based on historical data.

[0167] In some embodiments, the second label may include the expected heat of a sample unit grid in the second training samples. In some embodiments, the second label may be obtained in various ways. For example, the second label may be calibrated by a technician.

[0168] In some embodiments, the processor may also obtain a second label based on the predictability of wind measurement data in the sample unit grid and its correlation with extreme weather conditions through the following steps S21-S23: 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 correlation between the sample unit grid and extreme weather conditions.

[0169] In some embodiments, the processor can divide the monitoring area randomly or based on manual division. For example, the monitoring area can be divided into five parts: east, south, west, north, and center, by technicians.

[0170] The first unit grid refers to the sample unit grid with a high expected heat value. The high expected heat value can be preset by the system or manually.

[0171] Extreme weather conditions can include hail, strong winds, tornadoes, thunderstorms, tropical cyclones, and other extreme weather events. The correlation between a sample unit grid and extreme weather conditions refers to the degree of correlation between the wind measurement data of the sample unit grid and the extreme weather conditions.

[0172] In some embodiments, the processor can determine the degree of correlation between sample unit grids and extreme weather conditions based on statistics of historical data.

[0173] For example, the processor can statistically analyze the wind measurement data of sample units within a preset time (e.g., one week) before each extreme weather event in historical data, identify sample units whose wind measurement data meets the third preset condition as being associated with that extreme weather event, and determine the degree of association between the sample units and the extreme weather events as the ratio of the number of times the sample units are associated with all extreme weather events to the total number of all extreme weather events.

[0174] The third preset condition refers to the criteria used to determine whether a 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: wind speed exceeding a maximum wind speed threshold and / or air pressure exceeding a maximum air pressure threshold within a preset time period before the occurrence of extreme weather conditions. It should be noted that the wind measurement data for the sample unit grid can be obtained based on wind measurement data acquired by wind measurement equipment from historical data, or it can be based on… Figure 3 The wind resource situation simulated by the aforementioned CFD model is obtained.

[0175] For example, if a sample unit grid is located in an area where ten extreme weather events occur, and the wind measurement data of the sample unit grid meets the third preset condition when eight of the extreme weather events occur, then the sample unit grid is associated with all extreme weather events eight times, and the degree of association between the sample unit grid and the extreme weather events is 80%.

[0176] In some embodiments, the processor can sort multiple sample unit grids in descending order of their correlation with extreme weather conditions, determine the sample unit grids that are ranked before a preset number of grids as the first unit grid, and determine the expected heat of the sample in the second label corresponding to the first unit grid as the previously determined high expected heat value.

[0177] Step S22: Based on the first unit grid, determine the second unit grid, and based on the predictability of the wind measurement data of the second unit grid, determine the expected heat of the sample in the second label corresponding to the second unit grid.

[0178] The second unit grid refers to the sample unit grid located in the middle of a plurality of first unit grids. In some embodiments, the processor can connect the plurality of first unit grids pairwise and determine the sample unit grid at the midpoint of the connection as the second unit grid.

[0179] In some embodiments, the processor can determine the predictability of the wind measurement data of the second unit grid in a variety of ways based on historical data.

[0180] For example, the processor can perform modeling or use various data analysis algorithms, such as regression analysis and discriminant analysis, to process the wind measurement data of multiple first unit grids and second unit grids in historical data. It can determine the number of wind measurement data of the second unit grids that have the same functional relationship as the wind measurement data of the first unit grids, and determine the predictability of the wind measurement data of the second unit grids by taking the proportion of the number of wind measurement data of the second unit grids that have the same functional relationship as the wind measurement data of the first unit grids in all the wind measurement data of the second unit grids.

[0181] For example, if the historical data includes 100 wind measurement data points in the second unit grid, and 75 of these wind measurement data points have the same functional relationship as the wind measurement data points in the first unit grid, then the predictability of the wind measurement data points in the second unit grid is 75%.

[0182] In some embodiments, the processor can determine the expected heat of the sample in the second label corresponding to the second unit grid based on the predictability of the wind measurement data of the second unit grid by using a sixth preset rule.

[0183] The sixth preset rule refers to a pre-set rule used to determine the expected heat of the sample corresponding to the second unit grid. An exemplary sixth preset rule could be: the greater the predictability of the wind measurement data of the second unit grid, the smaller the expected heat of the corresponding sample.

[0184] 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 previously determined high expected heat value.

[0185] Step S23: Based on the first unit grid and the second unit grid, determine the expected heat of the remaining sample unit grids and their corresponding second labels using the method described in step S22 above, until all sample unit grids have been traversed.

[0186] In some embodiments, the processor can determine risk sub-regions based on grid monitoring heat and expected heat. For example, the processor can determine a region consisting of grid units with grid monitoring heat lower than expected heat as a risk sub-region.

[0187] Some embodiments in this specification determine risk sub-regions based on grid monitoring heat and expected heat, which can make the determination of risk sub-regions more in line with actual expectations and help to determine the location of subsequent updated monitoring.

[0188] Some embodiments in this specification divide the monitoring area into multiple unit grids, and then determine the risk sub-regions based on the monitoring heat of the grids, which can more detailed and comprehensively determine the risk sub-regions.

[0189] In some embodiments, the processor can determine updated monitoring locations based on risk sub-regions in various ways. For example, the processor can randomly generate updated monitoring locations within the risk sub-regions equal to the number of target moving radars. Further details regarding target moving radars can be found in [link to relevant documentation]. Figure 2 And its related descriptions.

[0190] In some embodiments, the processor may determine an updated monitoring location group 520 based on a 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 an updated monitoring location 550 based on the regional monitoring heat 530 and regional reference degree 540. For further explanation regarding updated monitoring locations, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0191] Updated monitoring location group 520 refers to a location group consisting of multiple candidate monitoring locations. For more information on candidate monitoring locations, please refer to [link / reference needed]. Figure 2 And its related descriptions.

[0192] In some embodiments, the processor may randomly generate multiple groups of updated monitoring locations within a risk sub-region. The number of candidate monitoring locations in each updated monitoring location group is the same as the number of target moving radars.

[0193] The regional monitoring heat index 530 refers to the heat index value of the risk sub-region in the monitoring area for wind resource assessment monitoring after the target mobile radar is adjusted based on the updated monitoring location group.

[0194] In some embodiments, the processor can determine the regional monitoring heat based on an updated monitoring location group. For example, the processor can determine the regional monitoring heat as the average value of the grid monitoring heat corresponding to each grid cell within a risk sub-region after adjusting the target mobile radar, based on the updated monitoring location group. For further explanation regarding grid monitoring heat, please refer to [link to relevant documentation]. Figure 5 The relevant descriptions in the preceding text.

[0195] When calculating regional monitoring heat, the target wind measurement equipment includes target mobile radar adjusted to candidate monitoring positions in the updated monitoring position group.

[0196] Regional reference degree refers to the reference degree of wind measurement data in the risk sub-region for assessing wind resources in the monitoring area after adjusting the target moving radar based on the updated monitoring location group.

[0197] In some embodiments, the processor can determine the regional reference degree based on an updated monitoring location group. For example, the processor can obtain the regional reference degree by simulating wind measurement data and working environment data in the risk sub-region using a CFD model after adjusting the target moving radar based on the updated monitoring location group.

[0198] Wind measurement data in the risk sub-region includes wind measurement data from target-moving radars adjusted to candidate monitoring positions in the updated monitoring location group within the risk sub-region. The determination of regional reference degree is similar to that of local reference degree; please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0199] In some embodiments, the processor may determine the candidate monitoring location in the updated monitoring location group corresponding to the area monitoring heat and area reference degree when the area monitoring heat and area reference degree meet the fourth preset condition as the updated monitoring location.

[0200] The fourth preset condition refers to the pre-set conditions for determining the updated monitoring location based on regional monitoring heat and regional reference degree. An example of the fourth preset condition could be: regional monitoring heat is higher than the third heat threshold and regional reference degree is higher than the reference degree threshold, etc. The third heat threshold and reference degree threshold can be preset by the system or manually.

[0201] Some embodiments in this specification determine the updated monitoring location by using regional reference degree and regional monitoring heat. This ensures that the regional reference degree and regional monitoring heat of the risk sub-region meet the requirements after the target moving radar is adjusted to the updated monitoring location, thereby improving the effectiveness of determining the updated monitoring location.

[0202] Some embodiments in this specification determine the updated monitoring location based on risk sub-regions. This can adjust the target moving radar to areas that need to be monitored but where wind resource assessment monitoring is insufficient. This improves the monitoring reference value of the target moving radar while ensuring the acquisition of richer wind measurement data in the monitoring area, which helps to conduct more comprehensive wind resource assessment monitoring of the monitoring area.

[0203] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0204] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0205] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the flow and sequence of the laminar flow shield. Although various examples have been discussed in the foregoing disclosure of some embodiments that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments described in this specification. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0206] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0207] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0208] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0209] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A system for adjusting monitoring parameters of a laser wind-measuring radar, characterized in that, include: The data collection module is configured to acquire wind measurement data and working environment data of wind measurement equipment within the monitoring area. The wind measurement equipment includes lidar and wind measurement tower. The lidar includes at least one of fixed radar and mobile radar. The working environment data includes site configuration information and / or overall environmental information. The data analysis module is configured as follows: Based on the wind measurement data, it was determined that there were no abnormal radars. The radars without anomalies are grouped to determine the 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, the local reference degree of the wind measurement group is determined; The local reference degrees of the wind measurement groups are weighted and summed to determine the monitoring reference degree of the radar without anomalies. When performing the weighted summation, the weight of the local reference degree of the wind measurement group is related to the importance of the sub-region corresponding to the wind measurement group. The monitoring and adjustment module is configured to determine the radar to be adjusted and the corresponding adjustment parameters of the radar to be adjusted based on the monitoring reference degree and the working environment data.

2. The system according to claim 1, characterized in that, The monitoring and adjustment module is further configured to: Based on the aforementioned monitoring reference degree, the target moving radar is determined; Based on the operating environment data, the updated monitoring location of the target movement radar is determined.

3. The system according to claim 2, characterized in that, The monitoring and adjustment module is further configured to: Based on the work environment data and the monitoring reference level, risk sub-regions of the monitoring area are determined; Based on the risk sub-region, the updated monitoring location is determined.

4. The system according to claim 3, characterized in that, The monitoring and adjustment module is further configured to: The monitoring area is divided into multiple unit grids; Based on the site configuration information and the monitoring reference degree, the monitoring heat of the grid corresponding to the multiple unit grids is determined; Based on the heat map data, the risk sub-regions are determined.

5. The system according to claim 4, characterized in that, The monitoring and adjustment module is further configured to: Based on the overall environmental information, the expected heat corresponding to the multiple unit grids is determined; The risk sub-regions are determined based on the monitored heat and the expected heat.

6. The system according to claim 3, characterized in that, The monitoring and adjustment module is further configured to: Based on the aforementioned risk sub-regions, determine the updated monitoring location group; Based on the updated monitoring location group, the regional monitoring heat and regional reference degree of the risk sub-region are determined; The updated monitoring location is determined based on the regional monitoring heat and the regional reference degree.

7. A method for adjusting monitoring parameters of a laser wind-measuring radar, characterized in that, Executed by the processor, including: Acquire wind measurement data and working environment data of wind measurement equipment within the monitoring area. The wind measurement equipment includes lidar and wind measurement tower. The lidar includes at least one of fixed radar and mobile radar. The working environment data includes site configuration information and / or overall environmental information. Based on the wind measurement data, it was determined that there were no abnormal radars. The radars without anomalies are grouped to determine the 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, the local reference degree of the wind measurement group is determined; The local reference degrees of the wind measurement groups are weighted and summed to determine the monitoring reference degree of the radar without anomalies. When performing the weighted summation, the weight of the local reference degree of the wind measurement group is related to the importance of the sub-region corresponding to the wind measurement group. Based on the monitoring reference degree and the working environment data, the radar to be adjusted and the corresponding adjustment parameters of the radar to be adjusted are determined.

8. The method according to claim 7, characterized in that, The process of determining the radar to be adjusted and its corresponding adjustment parameters based on the monitoring reference level and the operating environment data includes: Based on the aforementioned monitoring reference degree, the target moving radar is determined; Based on the operating environment data, the updated monitoring location of the target movement radar is determined.

9. A device for adjusting the monitoring parameters of a laser wind-measuring radar, characterized in that, The device includes at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is configured to execute at least a portion of the computer instructions to implement the method for adjusting the monitoring parameters of a laser wind-measuring radar as described in any one of claims 7 to 8.

10. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions, and when the computer reads the computer instructions, the computer executes the method for adjusting the monitoring parameters of the laser wind measuring radar as described in any one of claims 7 to 8.

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