Meteorological disaster early warning method and system for wind-solar pumped storage power station

Abstract

The invention is applicable to the technical field of disaster early warning, and particularly relates to a meteorological disaster early warning method and system for a wind and light pumped storage power station, and the method comprises the steps: delimiting a control region of the wind and light pumped storage power station, obtaining regional meteorological data taking the control region as an object, segmenting the regional meteorological data into a plurality of elements, extracting risk factors, and carrying out the early warning of the meteorological disaster of the wind and light pumped storage power station. Traversing the occurrence time of the risk factor, configuring a buffer range for the occurrence time, and defining the buffer range as the flight time of the unmanned aerial vehicle; and when the flight time comes, issuing a flight instruction to the unmanned aerial vehicle, triggering a verification rule pre-embedded in the unmanned aerial vehicle, and verifying the regional meteorological data. By determining the sensitive equipment, meteorological disasters can be prevented in advance, faults or accidents of production equipment can be effectively prevented, normal operation of wind and light pumping and storage work can be guaranteed, pumping and storage scheduling can be optimized by generating the early warning notice, the stability of a power grid can be guaranteed, and the reliability and economical efficiency of overall operation of a power generation system can be improved.

Description

Technical Field

[0001] The present invention relates to the field of disaster warning technology, and in particular to a meteorological disaster warning method and system for a wind-solar pumped-storage power station. Background Art

[0002] A wind-solar-pumped-storage power station refers to a comprehensive power station that combines wind power generation, photovoltaic power generation (solar energy) and pumped storage technology. A wind-solar-pumped-storage power station is highly dependent on weather conditions. The power generation of wind and solar energy is directly affected by meteorological conditions, such as wind speed, solar radiation and temperature. In addition, extreme weather events such as typhoons, heavy rains and hail may also cause equipment damage or unstable power output, affecting the safety and stability of the power grid.

[0003] The meteorological data of wind-solar-pumped-storage power stations are generally provided by public meteorological service providers. The generation of these meteorological data often relies on fixed meteorological stations, and the coverage area may be large, but their spatial resolution is low. They cannot accurately reflect the microclimate changes in the specific location of the wind-solar-pumped-storage power stations, and cannot meet the high-precision and real-time requirements of wind-solar-pumped-storage power stations for meteorological data.

[0004] Therefore, “how to use drones to focus on monitoring meteorological data at sensitive equipment” is the technical problem that the present invention needs to solve. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for early warning of meteorological disasters in a wind-solar-pumped-storage power station, so as to solve the problem of "how to use drones to focus on monitoring meteorological data at sensitive equipment" raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A meteorological disaster early warning method for a wind-solar pumped storage power station, the method comprising:

[0008] Delineate the control area of ​​the wind-solar pumped-storage power station, obtain regional meteorological data with the control area as the object, and divide it into several elements, extract the risk factor, traverse the occurrence time of the risk factor, and configure a buffer range for the occurrence time, and define the buffer range as the flight time of the drone;

[0009] When the flight time arrives, a flight instruction is issued to the drone, triggering the verification rules pre-embedded in the drone, verifying the regional meteorological data, and generating a verification result;

[0010] If the verification result is established, calibrate the regional meteorological data and appearance time, obtain the flight trajectory of the drone, construct a risk verification area, delete the part of the risk verification area outside the control area, dynamically modify the boundary of the risk verification area, find out the production equipment located in the risk verification area, configure the equipment toughness of each production equipment, compare the equipment toughness with the regional meteorological data, and mark the sensitive equipment;

[0011] In the risk verification area, the sensitive equipment is defined as a waypoint, a flight route is generated, and the flight route is sent to the drone, real-time meteorological data along the flight route is collected, the sensitive equipment is adjusted, the sensitive equipment, regional meteorological data and occurrence time are integrated, an early warning notification is generated, and the early warning notification is pushed to a preset terminal.

[0012] Furthermore, the step of "delimiting the control area of ​​the wind-solar pumped storage power station and acquiring regional meteorological data with the control area as the object" includes:

[0013] Dividing the control area into multiple levels, wherein the levels at least include: a core area, a buffer area, and a peripheral monitoring area;

[0014] Establishing a mapping between each of the levels and regional meteorological data, wherein the regional meteorological data is provided by a meteorological service provider;

[0015] Determine the deployment location of on-site meteorological sensors, obtain real-time monitoring data, and optimize the meteorological data in the area.

[0016] Furthermore, the step of "dividing into several elements, extracting risk factors, traversing the occurrence time of the risk factors, configuring a buffer range for the occurrence time, and defining the buffer range as the flight time of the drone" includes:

[0017] Determine whether the factor exceeds a set value, and if so, define the corresponding factor as a risk factor, and insert a label generated by the risk factor into the control area;

[0018] All risk factors are integrated to generate a risk set, and the risk set is embedded in the early warning notification.

[0019] Furthermore, the step of “when the flight time arrives, issuing a flight instruction to the drone, triggering a verification rule pre-embedded in the drone, verifying the regional meteorological data, and generating a verification result” includes:

[0020] Inputting the real-time meteorological data into the constructed numerical weather forecast model, and outputting calibration data;

[0021] Integrate the verification rule and calibration data to verify the regional meteorological data, wherein the verification rule is: calculate the difference between the calibration data and the regional meteorological data;

[0022] The difference is dynamically updated according to the real-time meteorological data. If the difference is less than a preset threshold, the verification result is defined as established, otherwise, it is defined as not established.

[0023] Furthermore, the step of "constructing a risk verification zone" includes:

[0024] Randomly select a number of target points from the flight trajectory, and determine the risk score of each target point based on the real-time meteorological data of the target point;

[0025] The target points whose risk scores are greater than a threshold are integrated and a risk verification area is constructed using a spatial interpolation method.

[0026] Furthermore, the step of "dynamically correcting the boundary of the risk verification area, finding the production equipment located in the risk verification area, configuring the equipment toughness of each of the production equipment, comparing the equipment toughness with regional meteorological data, and marking sensitive equipment" includes:

[0027] Selecting a number of test points from the boundary, determining risk scores of the test points, and modifying the boundary using the risk scores;

[0028] It is determined whether the risk factor in the regional meteorological data is greater than the equipment toughness. If so, the corresponding production equipment is defined as a sensitive equipment.

[0029] Furthermore, the step of "integrating the sensitive equipment, regional meteorological data and occurrence time, generating an early warning notification, and pushing the early warning notification to a preset terminal" includes:

[0030] Creating an emergency plan comparison table, wherein the emergency plan comparison table consists of risk factor items, plan items, and production equipment items;

[0031] Based on the risk factors, the emergency plan comparison table is queried to traverse the plans corresponding to the sensitive equipment, and the plans are activated.

[0032] Furthermore, the method further comprises:

[0033] If the verification result is not established, adjusting the buffer range using the calibration data;

[0034] After the buffer range ends, the risk factor is deleted and the flight instruction is revoked.

[0035] Furthermore, the system comprises:

[0036] A configuration module is used to define the control area of ​​the wind-solar pumped storage power station, obtain regional meteorological data with the control area as the object, and divide it into several elements, extract the risk factor, traverse the occurrence time of the risk factor, and configure a buffer range for the occurrence time, and define the buffer range as the flight time of the drone;

[0037] A verification module, used for sending a flight instruction to the UAV when the flight time arrives, triggering a verification rule pre-embedded in the UAV, verifying the regional meteorological data, and generating a verification result;

[0038] A marking module is used to calibrate the regional meteorological data and the appearance time when the verification result is established, obtain the flight trajectory of the drone, construct a risk verification area, delete the part of the risk verification area that is outside the control area, dynamically correct the boundary of the risk verification area, find out the production equipment located in the risk verification area, configure the equipment toughness of each of the production equipment, compare the equipment toughness with the regional meteorological data, and mark sensitive equipment;

[0039] The push module is used to define the sensitive equipment as a waypoint in the risk verification area, generate a flight route, and send the flight route to the drone, collect real-time meteorological data along the flight route, adjust the sensitive equipment, integrate the sensitive equipment, regional meteorological data and occurrence time, generate an early warning notification, and push the early warning notification to a preset terminal.

[0040] Furthermore, the configuration module includes:

[0041] A hierarchical unit, used to divide the control area into a plurality of levels, wherein the levels at least include: a core area, a buffer area and a peripheral monitoring area;

[0042] A mapping unit, used to establish a mapping between each of the levels and regional meteorological data, wherein the regional meteorological data is provided by a meteorological service provider;

[0043] An optimization unit, used to determine the deployment location of the on-site meteorological sensor, obtain real-time monitoring data, and optimize the meteorological data of the area;

[0044] a definition unit, configured to determine whether the element exceeds a set value, and if so, define the corresponding element as a risk factor, and insert a label generated by the risk factor into the control area;

[0045] The embedding unit is used to integrate all risk factors, generate a risk set, and embed the risk set into the early warning notification.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] By acquiring regional meteorological data, we can optimize power generation forecasts, improve power dispatching efficiency, and ensure grid stability. By determining risk factors, we can prevent risks brought by extreme weather and provide data support for preventing meteorological disasters. By issuing flight instructions, we can use drones to collect real-time meteorological data of wind and solar pumped-storage power stations, improve data accuracy, and enhance the disaster warning capabilities of wind and solar pumped-storage power stations. By constructing risk verification areas, we can demarcate dangerous areas, optimize grid dispatching and operations, and ensure the stability of power supply. By identifying sensitive equipment, we can prevent meteorological disasters in advance, effectively prevent production equipment failures, and improve the equipment stability of wind and solar pumped-storage power stations. By generating early warning notifications, we can provide data support for pumped-storage dispatching, ensure grid stability, and greatly enhance the disaster resistance of wind and solar pumped-storage power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of a meteorological disaster early warning method for a wind-solar pumped-storage power station provided in an embodiment of the present invention;

[0049] Figure 2 A first sub-process diagram of a method for early warning of meteorological disasters in a wind-solar pumped-storage power station provided in an embodiment of the present invention;

[0050] Figure 3 A second sub-flow chart of the meteorological disaster early warning method for a wind-solar pumped-storage power station provided in an embodiment of the present invention;

[0051] Figure 4 A third sub-flow chart of the meteorological disaster early warning method for a wind-solar pumped-storage power station provided in an embodiment of the present invention;

[0052] Figure 5 A fourth sub-flow chart of the meteorological disaster early warning method for a wind-solar pumped-storage power station provided in an embodiment of the present invention;

[0053] Figure 6 A block diagram of the composition of a meteorological disaster warning system for a wind-solar pumped-storage power station provided in an embodiment of the present invention;

[0054] Figure 7 A block diagram of the configuration module in the meteorological disaster warning system of a wind-solar pumped-storage power station provided by an embodiment of the present invention;

[0055] Figure 8 A block diagram of the composition of a verification module in a meteorological disaster warning system for a wind-solar pumped-storage power station provided by an embodiment of the present invention;

[0056] Fig. 9 A block diagram of the composition of a marking module in a meteorological disaster warning system for a wind-solar pumped-storage power station provided by an embodiment of the present invention;

[0057] Fig.10This is a block diagram of the composition of the push module in the meteorological disaster warning system of the wind-solar pumped-storage power station provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] In Example 1, Figure 1 The implementation process of the wind-solar pumped storage power station meteorological disaster warning method provided by the embodiment of the present invention is shown, and is described in detail below:

[0060] S100: Delineate the control area of ​​the wind-solar-pumped-storage power station, obtain regional meteorological data with the control area as the object, and divide it into several elements, extract risk factors, traverse the occurrence time of the risk factors, and configure a buffer range for the occurrence time, and define the buffer range as the flight time of the drone.

[0061] According to the actual planning or facility layout of the wind-solar-pumped-storage power station, the control area of ​​the wind-solar-pumped-storage power station is delineated, and the regional meteorological data including the control area is determined, where the regional meteorological data can be obtained from public channels, and the regional meteorological data at least includes wind force, temperature, sunshine intensity, sunrise and sunset time, etc.; the regional meteorological data is divided into several elements, such as wind elements and rain elements, etc. If an element is greater than the preset threshold, the element is defined as a risk factor, and the public channels are continued to be used to determine the occurrence time of the risk factor; the role of the buffer range is to use drones to calibrate the regional meteorological data within a certain period of time before and after the occurrence of the risk factor.

[0062] Within the buffer range, control the flight of the drone, collect real-time meteorological data in the control area, and calibrate the regional meteorological data. In real life, it should be noted that if the wind speed on that day is greater than the maximum take-off speed of the drone, mobile sensors, monitoring vehicles or weather balloons should be used to monitor the meteorological data of the wind and solar pumped storage power station in real time.

[0063] For example, the regional meteorological data of a wind-solar-pumped-storage power station is shown in the following table;

[0064]

[0065]

[0066] In the above table, the factors are wind speed 5.2m / s, wind speed 4.6m / s, wind speed 5.1m / s, wind speed 24m / s and wind direction 270°, etc. If the warning wind speed of the wind-solar pumped-storage power station is 22m / s (the warning wind speed is mainly used to remind staff to pay attention to wind changes in time, but at this wind speed, it will not cause equipment damage), the risk factor is the wind speed of 24m / s, the corresponding time is the occurrence time, and the buffer range is 1 hour or 2 hours before and after moment A. The specific time is determined by the staff.

[0067] S200: When the flight time arrives, a flight instruction is issued to the drone, triggering a verification rule pre-embedded in the drone, verifying the regional meteorological data, and generating a verification result.

[0068] When the flight time arrives, a flight instruction is sent to the UAV integrated with the meteorological monitoring equipment, wherein the meteorological monitoring equipment here includes but is not limited to temperature and humidity sensors, air pressure sensors, wind speed and wind direction sensors, etc., and the meteorological monitoring equipment is used to collect real-time meteorological data of the control area; wherein the verification rule is a specific method for comparing the real-time meteorological data with the regional meteorological data; the regional meteorological data is verified by using the verification rule to determine whether the regional meteorological data is valid; for example, the collected real-time meteorological data is input into the numerical weather forecast model, and the predicted value at time A is output to determine whether the predicted value is a wind speed of 24m / s. If the difference between the two is less than the preset threshold, the verification result is valid.

[0069] S300: If the verification result is established, calibrate the regional meteorological data and appearance time, obtain the flight trajectory of the drone, construct a risk verification area, delete the part of the risk verification area that is outside the control area, dynamically correct the boundary of the risk verification area, find out the production equipment located in the risk verification area, configure the equipment toughness of each of the production equipment, compare the equipment toughness with the regional meteorological data, and mark sensitive equipment.

[0070] If the verification result is established, the regional meteorological data is calibrated using the output of the numerical weather forecast model. In real life, wind-solar pumped-storage power stations generally occupy a large area, and the local microclimate may be different. Therefore, it is necessary to refine the meteorological data of wind-solar pumped-storage power stations, which requires dividing one or more risk verification areas. From the flight trajectory of the drone, multiple target points are randomly selected, and the risk score is determined based on the real-time meteorological data of each target point. Continuing to elaborate on the example in S100, if the forecast of the wind-solar pumped-storage power station The warning wind speed is 22m / s, the real-time wind speed of target point A is 18m / s, the real-time wind speed of target point B is 10m / s, and the real-time wind speed of target point C is 20m / s. The difference in real-time wind speed may be caused by mountain obstruction and wind turbulence. At this time, the risk score of A can be determined as 3 points, the risk score of B can be determined as 0 points, and the risk score of C can be determined as 4 points. The specific risk score can be determined by querying a pre-constructed comparison table, which is pre-prepared by the staff. All target points with a risk score greater than 3 points can be used to construct a risk verification area.

[0071] After the risk verification area is delineated, the boundaries of the risk verification area are dynamically updated using drones. It should be noted that since the meteorological data is changing dynamically, the boundaries of the risk verification area must also be changing dynamically. Production equipment located in the risk verification area and whose equipment toughness is less than the regional meteorological data is identified as sensitive equipment. Equipment toughness refers to the equipment's ability to withstand meteorological disasters. The greater the equipment toughness, the greater the wind force and temperature difference it can withstand.

[0072] S400: In the risk verification area, the sensitive equipment is defined as a waypoint, a flight route is generated, and the flight route is sent to the drone, real-time meteorological data along the flight route is collected, the sensitive equipment is adjusted, the sensitive equipment, regional meteorological data and occurrence time are integrated, an early warning notification is generated, and the early warning notification is pushed to a preset terminal.

[0073] With sensitive equipment as the transit point, real-time meteorological data at the sensitive equipment is collected. If the real-time meteorological data exceeds the equipment resilience, an early warning notification is generated and pushed to the preset terminal, where the preset terminal is the staff terminal of the wind-solar pumped-storage power station.

[0074] In Example 2, Figure 2 The implementation process of the wind-solar pumped storage power station meteorological disaster warning method provided by the embodiment of the present invention is shown. The steps of "delimiting the control area of ​​the wind-solar pumped storage power station and obtaining regional meteorological data with the control area as the object" are described in detail as follows:

[0075] S101: Divide the control area into multiple levels, wherein the levels at least include: a core area, a buffer area, and a peripheral monitoring area.

[0076] The control area is divided into multiple levels; in actual production, not all parts of the control area need to pay close attention to meteorological changes. The core area is the part of the wind, solar and pumped storage power station that is more sensitive to meteorological data.

[0077] S102: Establishing a mapping between each of the levels and regional meteorological data, wherein the regional meteorological data is provided by a meteorological service provider.

[0078] The regional meteorological data is segmented to determine the regional meteorological data at each level.

[0079] S103: Determine the deployment location of the on-site meteorological sensor, obtain real-time monitoring data, and optimize the meteorological data of the area.

[0080] In the core area, multiple groups of on-site meteorological sensors are deployed, and the collected real-time monitoring data are used to optimize the regional meteorological data to further improve the accuracy of the regional meteorological data.

[0081] In Example 3, Figure 2 The implementation process of the meteorological disaster warning method for a wind-solar pumped storage power station provided by an embodiment of the present invention is shown. The steps of "dividing into several elements, extracting risk factors, traversing the occurrence time of the risk factors, configuring a buffer range for the occurrence time, and defining the buffer range as the flight time of the drone" are described in detail as follows:

[0082] S104: Determine whether the factor exceeds a set value. If so, define the corresponding factor as a risk factor and insert a label generated by the risk factor into the control area.

[0083] The configuration process of the set value includes: in each element, find the minimum equipment resilience, which is the set value; in other words, if the wind speed exceeds the set value, there will be safety hazards in the wind-solar pumped-storage power station.

[0084] S105: Integrate all risk factors, generate a risk set, and embed the risk set into the early warning notification.

[0085] In the regional meteorological data, there may be no risk factor or there may be multiple risk factors. If there are multiple risk factors, a risk set is generated.

[0086] In Example 4, Figure 3The implementation process of the wind-solar pumped storage power station meteorological disaster warning method provided by the embodiment of the present invention is shown. The following is a detailed description of "when the flight time arrives, a flight instruction is issued to the drone, the verification rules pre-embedded in the drone are triggered, the regional meteorological data is verified, and the verification result is generated", as follows:

[0087] S201: inputting the real-time meteorological data into the constructed numerical weather forecast model, and outputting calibration data.

[0088] The real-time meteorological data collected by the drone is input into the numerical weather forecast model, and the calibration data is output. The calibration data is the meteorological forecast data for the wind, solar and pumped storage power stations. The regional meteorological data is calibrated using this meteorological forecast data.

[0089] S202: Integrate the verification rule and calibration data to verify the regional meteorological data, wherein the verification rule is: calculate the difference between the calibration data and the regional meteorological data;

[0090] S203: dynamically updating the difference according to the real-time meteorological data, and if the difference is less than a preset threshold, defining the verification result as established, otherwise, defining it as not established.

[0091] If the difference between the calibration data and the regional meteorological data is less than the preset threshold, the verification passes.

[0092] In Example 5, Figure 4 The implementation process of the meteorological disaster early warning method for a wind-solar pumped storage power station provided by an embodiment of the present invention is shown. The steps of "constructing a risk verification area" are described in detail as follows:

[0093] S301: Randomly select a number of target points from the flight trajectory, and determine the risk score of each target point based on the real-time meteorological data of the target points.

[0094] Determine the risk score of the target point based on the real-time meteorological data of the target point.

[0095] S302: Integrate the target points whose risk scores are greater than a threshold value and construct a risk verification area using a spatial interpolation method.

[0096] Find all target points with risk scores greater than the threshold. These target points represent potential risk areas. Use spatial interpolation to smooth and estimate these target points. The spatial interpolation method can infer the risk values ​​of other points in the flight trajectory based on the risk scores of existing target points, thereby obtaining a continuous risk distribution map and delineating the risk verification area.

[0097] In Example 6, Figure 4The implementation process of the meteorological disaster warning method for wind-solar pumped storage power station provided by an embodiment of the present invention is shown. The steps of "dynamically correcting the boundary of the risk verification area, finding the production equipment located in the risk verification area, configuring the equipment toughness of each of the production equipment, comparing the equipment toughness with the regional meteorological data, and marking the sensitive equipment" are described in detail as follows:

[0098] S303: Select a number of test points from the boundary, determine risk scores of the test points, and modify the boundary using the risk scores.

[0099] In the boundary of the risk verification area, a test point is selected, and the risk score of the test point is calculated, the test point is offset, and the boundary is corrected.

[0100] S304: Determine whether the risk factor in the regional meteorological data is greater than the equipment resilience. If so, define the corresponding production equipment as sensitive equipment.

[0101] In Example 7, Figure 5 The implementation process of the meteorological disaster warning method for a wind-solar pumped storage power station provided by an embodiment of the present invention is shown. The steps of "integrating the sensitive equipment, regional meteorological data and occurrence time, generating a warning notification, and pushing the warning notification to a preset terminal" are described in detail as follows:

[0102] S401: Create an emergency plan comparison table, wherein the emergency plan comparison table consists of risk factor items, plan items, and production equipment items.

[0103] Create an emergency plan comparison table, where the emergency plan comparison table is a collection of emergency plans.

[0104] S402: Based on the risk factors, query the emergency plan comparison table, traverse the plans corresponding to the sensitive equipment, and start the plans.

[0105] After determining the risk factor of a sensitive device, query the emergency plan comparison table, determine the plan, and use this plan to carry out emergency treatment of the sensitive equipment.

[0106] In Example 8, different from Example 1, in this embodiment of the present invention, the method further includes:

[0107] If the verification result is not established, adjusting the buffer range using the calibration data;

[0108] After the buffer range ends, the risk factor is deleted and the flight instruction is revoked.

[0109] If the verification is successful, it means that the prediction result of the regional meteorological data is accurate; otherwise, it means that the prediction result is wrong and there is a large deviation between the regional meteorological data and the actual situation. In this case, the calibration data will be used to increase the flight time of the UAV and calibrate the regional meteorological data. If there is no risk factor in the calibrated regional meteorological data, the flight command will be revoked. In other words, when it is predicted based on the regional meteorological data that there may be meteorological disasters within a certain period of time, the UAV will be launched and the regional meteorological data will be calibrated.

[0110] Figure 6 The structure diagram of the meteorological disaster warning system for a wind-solar pumped-storage power station provided by an embodiment of the present invention is shown. The meteorological disaster warning system 1 for a wind-solar pumped-storage power station includes:

[0111] Configuration module 11, used to delineate the control area of ​​the wind-solar pumped storage power station, obtain regional meteorological data with the control area as the object, and divide it into several elements, extract risk factors, traverse the occurrence time of the risk factors, and configure a buffer range for the occurrence time, and define the buffer range as the flight time of the drone;

[0112] The verification module 12 is used to issue a flight instruction to the UAV when the flight time arrives, trigger the verification rules pre-embedded in the UAV, verify the regional meteorological data, and generate a verification result;

[0113] The marking module 13 is used to calibrate the regional meteorological data and the appearance time when the verification result is established, obtain the flight trajectory of the drone, construct a risk verification area, delete the part of the risk verification area that is outside the control area, dynamically correct the boundary of the risk verification area, find out the production equipment located in the risk verification area, configure the equipment toughness of each of the production equipment, compare the equipment toughness with the regional meteorological data, and mark the sensitive equipment;

[0114] The push module 14 is used to define the sensitive equipment as a waypoint in the risk verification area, generate a flight route, and send the flight route to the drone, collect real-time meteorological data along the flight route, adjust the sensitive equipment, integrate the sensitive equipment, regional meteorological data and occurrence time, generate an early warning notification, and push the early warning notification to a preset terminal.

[0115] Figure 7 The structure diagram of the meteorological disaster warning system for a wind-solar pumped storage power station provided by an embodiment of the present invention is shown. The configuration module 11 includes:

[0116] A hierarchical unit 111, configured to divide the control area into a plurality of levels, wherein the levels at least include: a core area, a buffer area, and a peripheral monitoring area;

[0117] A mapping unit 112, configured to establish a mapping between each of the levels and regional meteorological data, wherein the regional meteorological data is provided by a meteorological service provider;

[0118] The optimization unit 113 is used to determine the deployment location of the on-site meteorological sensor, obtain real-time monitoring data, and optimize the regional meteorological data;

[0119] A definition unit 114, configured to determine whether the element exceeds the equipment toughness, and if so, define the corresponding element as a risk factor, and insert a label generated by the risk factor into the control area;

[0120] The embedding unit 115 is used to integrate all risk factors, generate a risk set, and embed the risk set into the warning notification.

[0121] Figure 8 The structure diagram of the meteorological disaster warning system for a wind-solar pumped storage power station provided by an embodiment of the present invention is shown. The verification module 12 includes:

[0122] The output unit 121 is used to input the real-time meteorological data into the constructed numerical weather forecast model and output calibration data;

[0123] The calculation unit 122 is used to integrate the verification rule and the calibration data to verify the regional meteorological data, wherein the verification rule is: calculating the difference between the calibration data and the regional meteorological data;

[0124] The updating unit 123 is used to dynamically update the difference according to the real-time meteorological data. If the difference is less than a preset threshold, the verification result is defined as established, otherwise, it is defined as not established.

[0125] Fig. 9 The structure diagram of the meteorological disaster warning system for a wind-solar pumped storage power station provided by an embodiment of the present invention is shown. The marking module 13 includes:

[0126] A selection unit 131 is used to randomly select a number of target points from the flight trajectory, and determine a risk score for each target point based on real-time meteorological data of the target points;

[0127] A construction unit 132, used to integrate the target points whose risk scores are greater than a threshold value and construct a risk verification area using a spatial interpolation method;

[0128] A correction unit 133, configured to select a number of test points from the boundary, determine risk scores of the test points, and correct the boundary using the risk scores;

[0129] The judgment unit 134 is used to judge whether the risk factor in the regional meteorological data is greater than the equipment resilience. If so, the corresponding production equipment is defined as a sensitive equipment.

[0130] Fig.10 The structure diagram of the meteorological disaster warning system for a wind-solar pumped storage power station provided by an embodiment of the present invention is shown. The push module 14 includes:

[0131] A creation unit 141 is used to create an emergency plan comparison table, wherein the emergency plan comparison table is composed of risk factor items, plan items and production equipment items;

[0132] The starting unit 142 is used to query the emergency plan comparison table according to the risk factor, traverse the plan corresponding to the sensitive equipment, and start the plan.

[0133] The configuration module 11 is mainly used to complete step S100, the verification module 12 is mainly used to complete step S200, the marking module 13 is mainly used to complete step S300, and the push module 14 is mainly used to complete step S400;

[0134] The stratification unit 111 is mainly used to complete step S101, the mapping unit 112 is mainly used to complete step S102, the optimization unit 113 is mainly used to complete step S103, the definition unit 114 is mainly used to complete step S104, and the embedding unit 115 is mainly used to complete step S105;

[0135] The output unit 121 is mainly used to complete step S201, the calculation unit 122 is mainly used to complete step S202, and the update unit 123 is mainly used to complete step S203;

[0136] The selection unit 131 is mainly used to complete step S301, the construction unit 132 is mainly used to complete step S302, the correction unit 133 is mainly used to complete step S303, and the judgment unit 134 is mainly used to complete step S304;

[0137] The creation unit 141 is mainly used to complete step S401, and the starting unit 142 is mainly used to complete step S402.

[0138] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0140] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A meteorological disaster early warning method for a wind-solar pumped storage power station, characterized in that: The method comprises: Delineate the control area of ​​the wind-solar pumped-storage power station, obtain regional meteorological data with the control area as the object, and divide it into several elements, extract the risk factor, traverse the occurrence time of the risk factor, and configure a buffer range for the occurrence time, and define the buffer range as the flight time of the drone; When the flight time arrives, a flight instruction is issued to the drone, triggering the verification rules pre-embedded in the drone, verifying the regional meteorological data, and generating a verification result; If the verification result is established, calibrate the regional meteorological data and appearance time, obtain the flight trajectory of the drone, construct a risk verification area, delete the part of the risk verification area outside the control area, dynamically modify the boundary of the risk verification area, find out the production equipment located in the risk verification area, configure the equipment toughness of each production equipment, compare the equipment toughness with the regional meteorological data, and mark the sensitive equipment; In the risk verification area, the sensitive equipment is defined as a waypoint, a flight route is generated, and the flight route is sent to the drone, real-time meteorological data along the flight route is collected, the sensitive equipment is adjusted, the sensitive equipment, regional meteorological data and occurrence time are integrated, an early warning notification is generated, and the early warning notification is pushed to a preset terminal.

2. The meteorological disaster early warning method for wind-solar pumped storage power station according to claim 1 is characterized in that: The step of "delimiting the control area of ​​the wind-solar pumped storage power station and acquiring regional meteorological data with the control area as the object" includes: Dividing the control area into multiple levels, wherein the levels at least include: a core area, a buffer area, and a peripheral monitoring area; Establishing a mapping between each of the levels and regional meteorological data, wherein the regional meteorological data is provided by a meteorological service provider; Determine the deployment location of on-site meteorological sensors, obtain real-time monitoring data, and optimize the meteorological data in the area.

3. The meteorological disaster early warning method for wind-solar pumped storage power station according to claim 2 is characterized in that: The step of "dividing into several elements, extracting risk factors, traversing the occurrence time of the risk factors, configuring a buffer range for the occurrence time, and defining the buffer range as the flight time of the drone" includes: Determine whether the factor exceeds a set value, and if so, define the corresponding factor as a risk factor, and insert a label generated by the risk factor into the control area; All risk factors are integrated to generate a risk set, and the risk set is embedded in the early warning notification.

4. The meteorological disaster early warning method for wind-solar pumped storage power station according to claim 1 is characterized in that: The step of "when the flight time arrives, issuing a flight instruction to the drone, triggering the verification rules pre-embedded in the drone, verifying the regional meteorological data, and generating a verification result" includes: Inputting the real-time meteorological data into the constructed numerical weather forecast model, and outputting calibration data; Integrate the verification rule and calibration data to verify the regional meteorological data, wherein the verification rule is: calculate the difference between the calibration data and the regional meteorological data; The difference is dynamically updated according to the real-time meteorological data. If the difference is less than a preset threshold, the verification result is defined as established, otherwise, it is defined as not established.

5. The meteorological disaster early warning method for wind-solar pumped storage power station according to claim 4 is characterized in that: The step of "constructing a risk verification zone" includes: Randomly select a number of target points from the flight trajectory, and determine a risk score for each target point based on real-time meteorological data of the target points; The target points whose risk scores are greater than a threshold are integrated and a risk verification area is constructed using a spatial interpolation method.

6. The meteorological disaster early warning method for wind-solar pumped storage power station according to claim 1 is characterized in that: The step of "dynamically correcting the boundary of the risk verification area, finding out the production equipment located in the risk verification area, configuring the equipment toughness of each of the production equipment, comparing the equipment toughness with regional meteorological data, and marking sensitive equipment" includes: Selecting a number of test points from the boundary, determining risk scores of the test points, and modifying the boundary using the risk scores; It is determined whether the risk factor in the regional meteorological data is greater than the equipment toughness. If so, the corresponding production equipment is defined as sensitive equipment.

7. The meteorological disaster early warning method for wind-solar pumped storage power station according to claim 1 is characterized in that: The step of "integrating the sensitive equipment, regional meteorological data and occurrence time, generating an early warning notification, and pushing the early warning notification to a preset terminal" includes: Creating an emergency plan comparison table, wherein the emergency plan comparison table consists of risk factor items, plan items, and production equipment items; Based on the risk factors, the emergency plan comparison table is queried to traverse the plans corresponding to the sensitive equipment, and the plans are activated.

8. The meteorological disaster early warning method for wind-solar pumped storage power station according to claim 4 is characterized in that: The method further comprises: If the verification result is not established, adjusting the buffer range using the calibration data; After the buffer range ends, the risk factor is deleted and the flight instruction is revoked.

9. A meteorological disaster warning system for a wind-solar pumped storage power station, characterized in that: The system comprises: A configuration module is used to define the control area of ​​the wind-solar pumped storage power station, obtain regional meteorological data with the control area as the object, and divide it into several elements, extract the risk factor, traverse the occurrence time of the risk factor, and configure a buffer range for the occurrence time, and define the buffer range as the flight time of the drone; A verification module, used for sending a flight instruction to the UAV when the flight time arrives, triggering a verification rule pre-embedded in the UAV, verifying the regional meteorological data, and generating a verification result; A marking module is used to calibrate the regional meteorological data and the appearance time when the verification result is established, obtain the flight trajectory of the drone, construct a risk verification area, delete the part of the risk verification area that is outside the control area, dynamically correct the boundary of the risk verification area, find out the production equipment located in the risk verification area, configure the equipment toughness of each of the production equipment, compare the equipment toughness with the regional meteorological data, and mark sensitive equipment; The push module is used to define the sensitive equipment as a waypoint in the risk verification area, generate a flight route, and send the flight route to the drone, collect real-time meteorological data along the flight route, adjust the sensitive equipment, integrate the sensitive equipment, regional meteorological data and occurrence time, generate an early warning notification, and push the early warning notification to a preset terminal.

10. The meteorological disaster early warning system for wind-solar pumped storage power station according to claim 9 is characterized in that: The configuration module includes: A hierarchical unit, used to divide the control area into a plurality of levels, wherein the levels at least include: a core area, a buffer area and a peripheral monitoring area; A mapping unit, used to establish a mapping between each of the levels and regional meteorological data, wherein the regional meteorological data is provided by a meteorological service provider; An optimization unit, used to determine the deployment location of the on-site meteorological sensor, obtain real-time monitoring data, and optimize the meteorological data of the area; a definition unit, configured to determine whether the element exceeds a set value, and if so, define the corresponding element as a risk factor, and insert a label generated by the risk factor into the control area; The embedding unit is used to integrate all risk factors, generate a risk set, and embed the risk set into the early warning notification.

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