Meteorological early warning method and device, electronic equipment and storage medium

Abstract

The application discloses a meteorological early warning method and device, electronic equipment and a storage medium, and relates to the technical field of meteorological risk early warning. The method comprises the following steps: acquiring a predicted meteorological disaster coverage range and predicted meteorological data of a region where a device to be warned is located within a future preset time period based on a current time, and a geographical position of the device to be warned; determining a spatial relationship between the geographical position and the predicted meteorological disaster coverage range based on the predicted meteorological disaster coverage range and the geographical position; and warning a meteorological risk of the device to be warned based on the spatial relationship and the predicted meteorological data to obtain a warning result. Through the application, the problem that meteorological warning is not related to geographical information of a device is solved, accurate spatial matching between a meteorological risk and a device point is realized, and the warning accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of meteorological risk early warning technology, specifically to meteorological early warning methods, devices, electronic equipment, and storage media. Background Technology

[0002] In recent years, extreme weather events have become more frequent. Coupled with factors such as the high proportion of new energy grid connection, the wide distribution of energy facilities, and complex terrain environments, the impact of meteorological disasters on energy production, transmission, and storage has continued to intensify, leading to increasingly higher demands for the accuracy of meteorological warnings. Currently, equipment meteorological warnings mostly utilize general meteorological service systems, with meteorological departments at all levels providing comprehensive meteorological monitoring and forecast data. Meteorological disaster levels are determined based on fixed meteorological thresholds such as wind speed, rainfall, temperature, and snowfall, and warning information is pushed out in batches via SMS, pop-up windows, and other methods. However, this warning method generally suffers from insufficient accuracy, making it difficult to achieve precise meteorological risk warnings and failing to meet the energy industry's needs for refined and proactive meteorological risk management of equipment. Summary of the Invention

[0003] This invention provides a weather warning method, device, electronic device, and storage medium to solve the problem of insufficient accuracy in weather warnings from equipment.

[0004] In a first aspect, the present invention provides a weather warning method, the method comprising: Obtain the predicted meteorological disaster coverage and predicted meteorological data of the area where the warning device is located within a preset time period based on the current time, as well as the geographical location of the warning device; Based on the predicted coverage of meteorological disasters and geographical location, determine the spatial relationship between geographical location and the predicted coverage of meteorological disasters; Based on spatial relationships and forecast meteorological data, early warnings are issued for the meteorological risks of the equipment to be warned, and warning results are obtained.

[0005] In one optional implementation, based on spatial relationships and predicted meteorological data, a meteorological risk warning is issued for the equipment to be warned, and a warning result is obtained, including: Based on forecasted meteorological data and preset meteorological thresholds, an initial risk coefficient is determined, which is used to indicate the degree of risk of meteorological disasters occurring to the equipment to be warned. Based on spatial relationships, determine the risk correction coefficient; The initial risk coefficient is corrected based on the risk correction coefficient to obtain the final risk coefficient, which is used to indicate the early warning result.

[0006] In one optional implementation, an initial risk coefficient is determined based on predicted meteorological data and preset meteorological thresholds, including: Obtain equipment information of the devices to be warned; Based on the equipment information, the preset meteorological threshold is corrected to obtain the corrected threshold; The initial risk coefficient is determined based on the predicted meteorological data and the corrected threshold.

[0007] In an optional implementation, before determining the initial risk coefficient based on predicted meteorological data and preset meteorological thresholds, the method further includes: Obtain historical actual meteorological data of the area where the warning equipment is located, as well as historical forecast meteorological data corresponding to the historical actual meteorological data; Based on historical actual meteorological data and historical forecast meteorological data, the forecast meteorological data is corrected to obtain corrected forecast meteorological data, which is then used to determine the initial risk coefficient based on the corrected forecast meteorological data and preset meteorological thresholds.

[0008] In one optional implementation, the risk correction coefficient is determined based on spatial relationships, including: Obtain terrain information of the location of the device to be warned; Risk correction coefficients are determined based on terrain information and spatial relationships.

[0009] In one optional implementation, the risk correction coefficient is determined based on spatial relationships, including: When there is an overlap between the spatial relationship indicating the geographical location and the predicted meteorological disaster coverage, the risk correction coefficient is determined based on the overlapping range corresponding to the spatial relationship. or, When the spatial relationship indicates that there is no overlap between the geographical location and the predicted meteorological disaster coverage area, the risk correction coefficient is determined based on the distance between the geographical location and the predicted meteorological disaster coverage area.

[0010] In one alternative implementation, the method further includes: Based on predicted meteorological data and regional spatial distribution data, the evolution trend of meteorological disasters in the region is dynamically simulated.

[0011] Secondly, the present invention provides a weather warning device, the device comprising: The acquisition module is used to acquire the predicted meteorological disaster coverage and predicted meteorological data of the area where the warning device is located within a preset time period based on the current time, as well as the geographical location of the warning device. The determination module is used to determine the spatial relationship between the predicted meteorological disaster coverage area and the predicted meteorological disaster coverage area based on the predicted meteorological disaster coverage area and the geographical location. The early warning module is used to issue early warnings about the meteorological risks of the equipment to be warned based on spatial relationships and forecast meteorological data, and to obtain early warning results.

[0012] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the weather warning method of the first aspect or any corresponding embodiment described above.

[0013] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the weather warning method of the first aspect or any corresponding embodiment described above.

[0014] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the weather warning method of the first aspect or any corresponding embodiment described above.

[0015] This invention identifies the spatial relationship between the equipment to be warned and the disaster area by combining the predicted meteorological disaster coverage within a preset time period with the geographical location of the equipment. This solves the problems of coarse spatial matching and inability to distinguish the disaster differences of individual equipment in related technologies. Furthermore, by combining spatial relationships and predicted meteorological data, it achieves dual early warning based on both spatial location and disaster intensity, improving the accuracy and pertinence of equipment meteorological risk prediction. It adapts to the differentiated risk early warning needs of wind power, photovoltaic and other equipment, and helps to accurately identify meteorological safety hazards of various equipment, providing reliable support for the refined management and safe and stable operation of equipment meteorological risks. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of a weather warning method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the architecture of a weather warning system according to an embodiment of the present invention; Figure 3 This is a structural block diagram of a weather warning device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

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

[0019] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0020] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] According to an embodiment of the present invention, a weather warning method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0022] This embodiment provides a weather warning method that can be used on the aforementioned mobile terminals, such as mobile phones, tablets, etc. (the executing entity is described in conjunction with the actual situation). Figure 1 This is a flowchart of a weather warning method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: S101, obtain the predicted meteorological disaster coverage and predicted meteorological data of the area where the warning device is located within a preset time period based on the current time, as well as the geographical location of the warning device.

[0023] Specifically, the equipment to be monitored can be various energy infrastructures in energy companies, such as wind power, photovoltaic, thermal power, and hydropower. For example, wind turbines in mountain wind farms and photovoltaic modules in desert photovoltaic power stations.

[0024] The predicted meteorological disaster coverage area refers to the spatial distribution area of ​​various hazardous weather events predicted by the meteorological department within a future preset time period. For example, the predicted meteorological disaster coverage area can be described by latitude and longitude, weather type, etc. Examples include the coverage area of ​​disasters such as rainstorms, typhoons, thunderstorms, strong winds, blizzards, frost, high temperatures, and hail.

[0025] Forecast meteorological data includes, but is not limited to, wind speed, rainfall, temperature, snowfall, hail diameter, air pressure, and relative humidity. Preset future time periods can be 0-6 hours or 1-7 days. For example, forecast meteorological data could be the maximum wind speed for the next 2 hours.

[0026] The geographical location of the device to be warned can be its precise spatial geographic coordinates. For example, the latitude and longitude coordinates of the device obtained using high-precision GIS positioning. It can be understood that the geographical location of the device can be the location of its center, or it can be the geographical locations of multiple points within a certain range generated from that center at preset distances.

[0027] In one possible implementation, the predicted coverage of meteorological disasters and predicted meteorological data can be obtained through a meteorological forecasting service platform.

[0028] In this embodiment of the application, the predicted meteorological data can be 0-6 hour forecast data, short-term meteorological forecast data for the next 1-7 days, early warning information, etc., obtained from meteorological monitoring stations and satellite meteorological systems.

[0029] In one possible implementation, the forecast meteorological data is standardized global meteorological data generated by cleaning, denoising, and time-series synchronization processing to remove abnormal data caused by equipment vibration and signal interference.

[0030] S102, Based on the predicted meteorological disaster coverage and geographical location, determine the spatial relationship between geographical location and predicted meteorological disaster coverage.

[0031] Specifically, spatial relationships indicate the spatial correlation between the geographical location of the device to be warned and the predicted meteorological disaster coverage area. For example, the spatial relationship could be that the geographical location of the device to be warned completely falls within the predicted meteorological disaster coverage area. Alternatively, the spatial relationship could be that the geographical location of the device to be warned partially overlaps with the predicted meteorological disaster coverage area. Or, the spatial relationship could be that the geographical location of the device to be warned does not overlap with the predicted meteorological disaster coverage area.

[0032] For example, a spatial overlay analysis algorithm based on Geographic Information System (GIS) is used to perform surface-to-surface and point-to-surface intersection calculations on the geographic location and the predicted meteorological disaster coverage area, thereby obtaining the spatial relationship between the two.

[0033] S103, based on spatial relationships and predicted meteorological data, provides early warnings of meteorological risks to the equipment to be warned, and obtains early warning results.

[0034] In this way, early warning information is only sent to equipment and stations with meteorological risks, eliminating the problem of redundancy in the overall early warning system and avoiding interference from ineffective early warnings.

[0035] Specifically, meteorological risk refers to the potential threats posed by meteorological disasters to equipment operational safety, equipment lifespan, and production efficiency. Meteorological risk changes with spatial relationships and forecast meteorological data. For example, meteorological risk could be the risk of wind turbine blade damage from strong winds, or the risk of short circuits in photovoltaic power plants from heavy rain.

[0036] In one possible implementation, if the spatial relationship indicates that the overlap area between the geographical location of the device to be warned and the predicted meteorological disaster coverage area is greater than a preset overlap area, and the predicted meteorological data is greater than a preset meteorological threshold, then the warning result indicates that the device to be warned faces a meteorological risk. Conversely, if the spatial relationship indicates that the overlap area between the geographical location of the device to be warned and the predicted meteorological disaster coverage area is less than or equal to a preset overlap area, or the predicted meteorological data is less than or equal to a preset meteorological threshold, then the warning result indicates that the device to be warned does not face a meteorological risk.

[0037] In this embodiment of the invention, the spatial relationship between the device to be warned and the disaster area is identified by the predicted meteorological disaster coverage area and the geographical location of the device within a preset future time period. This solves the problem of coarse spatial matching of warnings and inability to distinguish the disaster differences of individual devices in related technologies. Furthermore, by combining spatial relationships and predicted meteorological data, dual warnings are achieved in terms of spatial location and disaster intensity, which improves the accuracy and pertinence of equipment meteorological risk prediction. It adapts to the differentiated risk warning needs of wind power, photovoltaic and other equipment, and helps to accurately identify meteorological safety hazards of various devices, providing reliable support for the refined management and safe and stable operation of equipment meteorological risks.

[0038] Furthermore, a pre-set prediction model can be trained based on historical meteorological data, topographic data, and equipment failure sample data to issue early warnings about meteorological risks to the equipment to be warned, thus obtaining warning results. The pre-set prediction module can be a reinforcement learning model, a neural network model, etc. This eliminates reliance on a single external meteorological platform, enabling short-term, accurate predictions of localized, small-scale, and sudden meteorological risks. Combined with spatial analysis, intelligent early warning judgments are completed, further improving the timeliness and accuracy of early warnings.

[0039] In some embodiments, based on the foregoing embodiments, a weather risk warning is issued for the device to be warned based on spatial relationships and predicted meteorological data, and a warning result is obtained. Specifically, the method includes the following steps: a1, based on predicted meteorological data and preset meteorological thresholds, determines the initial risk coefficient.

[0040] The initial risk coefficient is used to indicate the degree of risk of meteorological disasters occurring to the equipment to be warned.

[0041] Specifically, preset meteorological thresholds are critical values ​​of meteorological parameters set based on the type of equipment and its disaster resistance performance. Different equipment corresponds to different preset meteorological thresholds. For example, the gale warning threshold for wind power equipment is set at 10 m / s, while the rainstorm risk threshold for photovoltaic equipment is set at 50 mm / 2h.

[0042] The initial risk coefficient is determined based on forecast meteorological data and reflects the degree of danger of the current meteorological disaster. The initial risk coefficient is positively correlated with the intensity of the meteorological disaster; the higher the initial risk coefficient, the stronger the destructive power of the meteorological disaster. For example, when the wind speed reaches 20% of the preset meteorological threshold (i.e., the preset wind speed threshold), the initial risk coefficient is 0.2; when the wind speed reaches 90% of the preset meteorological threshold, the initial risk coefficient is 0.9.

[0043] In one possible implementation, the initial risk coefficient is determined based on predicted meteorological data and preset meteorological thresholds, specifically including the following steps: First, obtain the equipment information of the devices to be warned.

[0044] Specifically, equipment information is used to distinguish different equipment attributes, disaster resistance capabilities, and operational characteristics. For example, equipment information includes, but is not limited to, equipment type, rated parameters, service life, disaster resistance level, operating status, connection relationships, data transmission type, importance level, basic information of the company owning the equipment, and terrain information of the equipment's location. Among these, equipment type can be wind turbines, photovoltaic systems, thermal power transformers, power transmission cables, etc. Equipment operating status can be full-load operation, low-load operation, or shutdown for maintenance, etc. Service life can be brand new equipment or equipment exceeding its service life, etc. Basic information of the company owning the equipment can include the company's geographical location, boundaries, address, industry type, administrative region, business scope, and production and operation information, etc.

[0045] Then, based on the equipment information, the preset meteorological threshold is corrected to obtain the corrected threshold.

[0046] Specifically, the revised threshold incorporates equipment information, enabling dynamic adjustment of preset meteorological thresholds. For example, photovoltaic equipment that has exceeded its service life has weaker resistance to rain and flooding, so the 50mm rainstorm threshold can be revised to 40mm.

[0047] Optionally, based on the mapping relationship between preset equipment information and correction weights, the correction weights of preset meteorological thresholds are determined, and the preset meteorological thresholds are corrected based on the correction weights, such as by weighted summation of the preset meteorological thresholds and correction weights to obtain the corrected thresholds.

[0048] For example, taking the service life of the equipment to be warned as an example, the correction weight for equipment with a service life exceeding the first preset service life is less than 1, such as 0.8, thereby lowering the meteorological threshold corresponding to the equipment, so that the corrected threshold matches the actual disaster resistance capability of the equipment. The correction weight for equipment with a service life less than the second preset service life is greater than 1, such as 1.2, thereby raising the meteorological threshold corresponding to the equipment, so that the corrected threshold matches the actual disaster resistance capability of the equipment. Here, equipment with a service life exceeding the first preset service life (such as the service life corresponding to the equipment) can be understood as equipment that has exceeded its service life. Equipment with a service life less than the second preset service life (such as the warranty period corresponding to the equipment) can be understood as brand new equipment.

[0049] For example, taking the importance of the equipment to be warned as an indicator of equipment information, the correction weight for important equipment is greater than 1, and the correction weight for unimportant equipment is less than 1.

[0050] Therefore, different preset meteorological thresholds are set for core main equipment, auxiliary equipment, outdoor exposed lines, and enclosed stations to avoid the problems of over-warning of equipment with strong disaster resistance, excessively high warning thresholds for equipment with weak disaster resistance, and missed risk assessment when using fixed preset meteorological thresholds.

[0051] Finally, the initial risk coefficient is determined based on the predicted meteorological data and the corrected threshold.

[0052] Optionally, the difference between the predicted meteorological data and the corrected threshold is calculated, and the proportion of the difference to the corrected threshold is linearly normalized to obtain the initial risk coefficient in the 0-1 interval. The greater the difference between the predicted meteorological data and the corrected threshold, the higher the initial risk coefficient.

[0053] In this way, by adjusting the preset meteorological threshold based on the equipment information of the equipment to be warned, the preset meteorological threshold can be personalized according to the individual differences of the equipment information, so as to obtain the corrected threshold for the disaster resistance capability of the equipment.

[0054] In one possible implementation, before determining the initial risk coefficient based on predicted meteorological data and preset meteorological thresholds, the method provided by this invention further includes the following: First, obtain historical actual meteorological data of the area where the warning equipment is located, as well as historical forecast meteorological data corresponding to the historical actual meteorological data.

[0055] For example, historical actual meteorological data can be obtained through the local miniature meteorological sensor terminal where the device is located.

[0056] Then, based on historical actual meteorological data and historical forecast meteorological data, the forecast meteorological data is corrected to obtain corrected forecast meteorological data, which is used to determine the initial risk coefficient based on the corrected forecast meteorological data and the preset meteorological threshold.

[0057] Optionally, the deviation between historical actual meteorological data and historical forecast meteorological data is statistically analyzed, and a deviation correction coefficient is obtained based on this deviation. The current forecast meteorological data is then multiplied by the deviation correction coefficient to obtain the corrected forecast meteorological data.

[0058] Optionally, a linear regression model can be constructed based on historical actual meteorological data and historical forecast meteorological data to fit the mapping relationship between the predicted and actual values. The current forecast meteorological data can then be input into this model to obtain the corrected forecast meteorological data.

[0059] In this way, by acquiring historical meteorological data and exploring the discrepancies between predicted and actual data, the predicted meteorological data can be further corrected to obtain corrected predicted meteorological data that more closely reflects the actual meteorological evolution trend.

[0060] a2, based on spatial relationships, determines the risk correction coefficient.

[0061] Specifically, the risk correction factor is used to correct the initial risk factor, characterizing the degree of influence of the equipment's spatial location on meteorological risk. For example, when the equipment's geographical location is within the predicted meteorological disaster coverage area, the risk correction factor is 1; when the equipment is not within the predicted meteorological disaster coverage area at all, the risk correction factor is 0.1.

[0062] In one possible implementation, the risk correction coefficient is determined based on spatial relationships, specifically including the following steps: First, obtain the terrain information of the location of the device to be warned.

[0063] Specifically, topographic information characterizes the natural topography surrounding the location of the warning equipment, reflecting the characteristics of topography's influence on meteorological disasters. For example, topographic information includes, but is not limited to, topographic type (such as mountains, low-lying areas, and high-altitude cold regions), altitude, proximity to water, sea areas, and watersheds. For instance, windward slopes of mountains significantly amplify wind speeds, and low-lying valleys are prone to accumulating rainwater, leading to waterlogging.

[0064] For example, terrain information of the device's location can be obtained through high-precision digital elevation data parsing.

[0065] Then, based on terrain information and spatial relationships, risk correction coefficients are determined.

[0066] For example, a mapping relationship between terrain information, spatial relationships, and risk correction coefficients is pre-constructed. Based on this mapping relationship, the risk correction coefficient corresponding to the terrain information and spatial relationships of the equipment to be warned is determined. Taking wind speed as an example, when the geographical location of the equipment with mountainous terrain information is within the predicted meteorological disaster coverage area, the corresponding risk correction coefficient is 1.5; when the geographical location of the equipment with low-lying terrain information is within the predicted meteorological disaster coverage area, the corresponding risk correction coefficient is 0.9.

[0067] By incorporating topographic information such as terrain features into the risk correction coefficient, differentiated and graded early warnings can be implemented for complex working conditions such as plains, mountains, coastlines, and high-altitude cold regions, as well as for different types of equipment. This allows for accurate determination of the real-time meteorological risk status of each piece of equipment, making the risk correction results more consistent with the actual natural environment in which the equipment is located, and improving the rationality of the risk correction.

[0068] In another possible implementation, the risk correction coefficient is determined based on spatial relationships, specifically including the following: When there is an overlap between the spatial relationship indicating geographical location and the predicted meteorological disaster coverage, the risk correction coefficient is determined based on the overlapping range corresponding to the spatial relationship.

[0069] Specifically, the overlap range is used to quantify the degree of overlap between the equipment and the meteorological load area, including but not limited to the overlap area between the equipment and the predicted meteorological disaster coverage area, and the ratio of the overlap area to the total area of ​​the equipment.

[0070] Taking the overlap range as the ratio of the overlapping area between the geographical location and the predicted meteorological disaster coverage area to the total area of ​​the equipment as an example, the ratio is normalized to obtain the risk correction coefficient in the range of 0-1. The higher the overlap ratio, the greater the risk correction coefficient.

[0071] Alternatively, if the spatial relationship indicates that there is no overlap between the geographical location and the predicted meteorological disaster coverage area, the risk correction coefficient can be determined based on the distance between the geographical location and the predicted meteorological disaster coverage area.

[0072] Specifically, the distance between the geographical location and the predicted meteorological disaster coverage area can be the Euclidean distance between the centerline point of the equipment and the center point of the predicted meteorological disaster coverage area, or it can be the minimum distance between the edge of the equipment and the edge of the predicted meteorological disaster coverage area.

[0073] For example, multiple distance ranges are preset, with different distance ranges corresponding to risk correction coefficients. For instance, the risk correction coefficient for the close-range risk zone (0-2km) is 1.2, the risk correction coefficient for the medium-range impact zone (2-5km) is 1, and the risk correction coefficient for the distant no-impact zone (above 5km) is 0.5.

[0074] Understandably, risk correction coefficients can also be determined based on the terrain information and the overlapping range corresponding to the spatial relationship of the device to be warned. For example, based on the pre-constructed mapping relationship between overlapping range, terrain information, and risk correction coefficient, and based on this mapping relationship, as well as the overlapping range and terrain information corresponding to the device to be warned, the risk correction coefficient can be determined.

[0075] Similarly, risk correction coefficients can be determined based on the terrain information and spatial relationships of the equipment to be warned. For example, based on a pre-built mapping relationship between distance, terrain information, and risk correction coefficients, and using this mapping relationship, along with the distance and terrain information corresponding to the equipment to be warned, the risk correction coefficients can be determined.

[0076] In another possible implementation, a mapping relationship between spatial relationships and risk correction coefficients is pre-established. For example, when the device's geographical location is in the center of the predicted meteorological disaster coverage area, the risk correction coefficient is set to a higher value; if the device is on the edge of the predicted meteorological disaster coverage area, the risk correction coefficient is set to a lower value.

[0077] a3, based on the risk correction coefficient, corrects the initial risk coefficient to obtain the final risk coefficient.

[0078] The final risk coefficient is used to indicate the early warning result.

[0079] Specifically, the final risk coefficient is a comprehensive risk quantification value obtained by integrating the intensity of meteorological disasters and the spatial location of the equipment. For example, if the initial risk coefficient is 0.7, and the equipment is located in the center of the predicted meteorological disaster coverage area, with a correction coefficient of 1, then the corrected final risk coefficient is 0.7. If the equipment is not within the predicted meteorological disaster coverage area, with a correction coefficient of 0.2, then the final risk coefficient is only 0.14.

[0080] For example, the final risk coefficient is obtained by weighted summing of the risk adjustment coefficient and the initial risk coefficient.

[0081] In this embodiment, an initial risk coefficient is obtained based on predicted meteorological data and preset meteorological thresholds. At the same time, a risk correction coefficient is determined by the spatial relationship between the equipment and the coverage area of ​​the predicted meteorological load. The final risk coefficient is determined from both meteorological and spatial dimensions. This enables the output of differentiated early warning results for equipment in different locations and different scenarios, thereby improving the accuracy of equipment meteorological risk early warning.

[0082] Furthermore, based on the final risk coefficient and the preset mapping relationship between the risk coefficient and the warning level, the current warning level of the equipment to be warned is determined. For example, meteorological risks are uniformly divided into four warning levels: blue (final risk coefficient 0-0.25), yellow (final risk coefficient 0.25-0.5), orange (final risk coefficient 0.5-0.75), and red (final risk coefficient 0.75-1), to achieve refined graded warning judgment.

[0083] In some embodiments, based on any of the foregoing embodiments, the method provided in this application further includes the following: Based on predicted meteorological data and regional spatial distribution data, the evolution trend of meteorological disasters in the region is dynamically simulated.

[0084] Specifically, regional spatial distribution data is used to describe the spatial distribution characteristics of features, equipment, landforms, and grids within a region, including but not limited to regional meteorological grid distribution, topographic elevation distribution, and underlying surface type distribution. Examples include spatial arrangement grids of wind farm equipment clusters, geographical zoning data for mountainous canyons, and spatial distribution data of low-lying areas.

[0085] The evolution trend of meteorological disasters refers to the dynamic characteristics of meteorological disasters over time and space in the future, including the dynamic evolution patterns of their movement direction, diffusion and contraction range, intensity increase and decrease, coverage area changes, and duration changes. For example, rainstorm disasters gradually spread from west to east, while strong wind disasters continuously intensify in valley areas.

[0086] In one possible implementation, based on time-series meteorological forecast data and spatial distribution data, a trend extrapolation algorithm is used to dynamically simulate the spread path, intensity decay and amplification patterns, and coverage evolution trends of meteorological disasters.

[0087] In another possible implementation, the predicted meteorological data is decomposed into time-series meteorological parameters at each time point and at each grid point. Combined with regional spatial distribution data, a unified geographic grid extrapolation model is constructed. Based on the variation law of meteorological parameters in each grid, the generation, movement, enhancement, weakening and dissipation process of meteorological disasters in different grid areas is simulated frame by frame. The dynamic coverage and intensity change trend of the spatiotemporal evolution of disasters are output, which is suitable for the whole-domain refined disaster evolution simulation scenario.

[0088] In this embodiment of the invention, based on meteorological data and spatial distribution data, the spread path, intensity changes and risk evolution trend of meteorological disasters can be predicted in advance, realizing medium- and long-term and short-term early warning of meteorological risks. This changes the drawbacks of passive alarms after the fact and provides sufficient window period for equipment shutdown, on-site protection, scheduling optimization and early inspection, effectively reducing the incidence of meteorological disaster safety accidents.

[0089] Furthermore, the final risk coefficients, equipment lists, and risk escalation trends of equipment at different time periods are visualized on the electronic map interface, enabling advance prediction of meteorological risks. Here, the map can be a two-dimensional or three-dimensional electronic map. By constructing three-dimensional models of energy facilities and terrain, spatial features such as altitude differences, equipment distribution, and terrain obstruction are accurately reconstructed. This allows for three-dimensional risk quantification analysis of coupled meteorological risks such as strong winds, lightning, and landslides, further improving the accuracy of early warnings in complex mountainous and hilly areas. For example, the final risk coefficients of each piece of equipment can be dynamically displayed on the GIS map for the next 0–72 hours.

[0090] Furthermore, based on the final risk coefficient, targeted and precise early warning information is delivered. The warning content includes the name of the company where the equipment is located, the equipment coordinates, the risk type, the risk level corresponding to the final risk coefficient, the duration, and a standardized emergency response plan. Simultaneously, corresponding work orders are automatically generated and dispatched based on the warning level, supporting on-site handling feedback, progress updates, risk verification, and post-event archiving and review, thus constructing a complete closed-loop management system of early warning, location, work order dispatch, handling, and review.

[0091] This application also provides a weather warning system in its embodiments. For example... Figure 2 As shown, the system adopts a layered modular architecture design, with data acquisition layer, data fusion and processing layer, map spatial analysis layer, intelligent early warning decision layer, and visualization and handling layer set up from bottom to top. Each layer and module operates independently and works together to complete the entire process of meteorological early warning business processing.

[0092] The data acquisition layer provides comprehensive basic data support, collecting high-precision electronic map data, actual observation data from meteorological facilities, multi-dimensional forecast meteorological data, equipment ledger data, basic enterprise information, enterprise video information, and historical meteorological disaster data in real time. Electronic map data includes remote sensing imagery, topographic maps, topography, elevation, sea areas, watersheds, and buffer zone spatial parameters. Actual observation data from meteorological facilities includes real-time and historical data on wind speed, wind direction, rainfall, temperature, snowfall, irradiance, humidity, and air pressure. Multi-dimensional meteorological forecast / warning data includes forecast data and disaster warning data at different spatiotemporal resolutions for severe weather events such as precipitation, snowfall, strong winds, dense fog, dust storms, high temperatures, low temperatures, cooling, heavy precipitation, and typhoons, obtained from the central government, various meteorological bureaus, or other channels. Equipment ledger data includes equipment geographical location, equipment type, hardware parameters, importance level, service life, protection level, topology connections, and data transmission type. Basic enterprise information includes enterprise geographical location, boundary range, address, industry type, administrative region, business scope, and production and operation information. Enterprise video information includes real-time video footage and historical recordings of key locations within the enterprise. Historical meteorological disaster data includes disaster type, disaster level, occurrence time, impact description, damage, and response status.

[0093] The data fusion processing layer addresses issues such as format differences, temporal misalignment, and abnormal interference in multi-source heterogeneous data. It performs data cleaning, deduplication, format normalization, and temporal alignment. It establishes multi-dimensional correlation mapping relationships among geospatial data, meteorological data, equipment ledger data, enterprise basic information, enterprise video information, and historical meteorological disaster data, and eliminates invalid and abnormal data, providing standardized and high-quality datasets for subsequent spatial analysis and intelligent decision-making.

[0094] The map spatial analysis layer is used to achieve precise coordinate positioning of equipment and enterprise boundaries, overlay of meteorological risk buffer zones and analysis of disaster distance (i.e., spatial relationship), and calculation of risk coefficients. It can accurately locate all equipment within the meteorological risk coverage area and complete the quantitative identification of risk space.

[0095] The intelligent early warning decision layer incorporates a graded / classified early warning model adapted to energy industry scenarios, a dynamic threshold correction algorithm, and a meteorological risk extrapolation algorithm. Based on spatial analysis and quantitative results, combined with information such as industry type, production and operation information, equipment attributes, terrain features, and service status, it dynamically outputs the enterprise's meteorological risk type, impact period, differentiated risk level, and matching standardized emergency response strategy, providing technical support for meteorological risk early warning, response, and scientific decision-making.

[0096] The visualization and response layer, based on electronic maps, enables comprehensive risk visualization, tiered early warning and targeted push notifications, intelligent dispatch of emergency work orders, recording of the response process, and post-event data review. It streamlines the early warning and response workflow, achieving a closed-loop process for meteorological risk management. This layer supports application display on various terminals, including large screens, PCs, and mobile devices.

[0097] This system adopts a layered design, adaptable to all types of energy scenarios including wind power, photovoltaics, thermal power, energy storage, and oil and gas, and supports the rapid integration of new meteorological monitoring terminals and equipment. It can also be expanded and overlaid with multi-dimensional risk warning modules for geology, hydrology, and typhoons, adapting to subsequent intelligent upgrades and iterations. Furthermore, this system deeply integrates spatial positioning, risk quantification analysis, tiered intelligent early warning, automatic work order dispatch, on-site closed-loop handling, and data review and archiving across the entire business chain, breaking down the information silos between traditional meteorological early warning and on-site operation and maintenance. It eliminates the need for manual verification of equipment parameters, querying emergency plans, and identifying risk points, significantly reducing emergency response time and comprehensively improving the intelligence, standardization, and precision of meteorological risk management for energy companies.

[0098] This embodiment also provides a weather warning device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0099] This embodiment provides a weather warning device, such as Figure 3 As shown, it includes: The acquisition module 301 is used to acquire the predicted meteorological disaster coverage area and predicted meteorological data of the area where the warning device is located within a preset future time period based on the current time, as well as the geographical location of the warning device; Module 302 is used to determine the spatial relationship between the predicted meteorological disaster coverage area and the predicted meteorological disaster coverage area based on the predicted meteorological disaster coverage area and the geographical location. The early warning module 303 is used to provide early warning of meteorological risks to the equipment to be warned based on spatial relationships and forecast meteorological data, and to obtain early warning results.

[0100] This device identifies the spatial relationship between the equipment to be warned and the disaster area by combining the predicted meteorological disaster coverage within a preset time period with the equipment's geographical location. This solves the problems of coarse spatial matching in related technologies and the inability to distinguish the disaster differences of individual equipment. Furthermore, by combining spatial relationships and predicted meteorological data, it achieves dual warnings based on both spatial location and disaster intensity, improving the accuracy and relevance of equipment meteorological risk prediction. It adapts to the differentiated risk warning needs of wind power, photovoltaic, and other equipment, and helps to accurately identify meteorological safety hazards of various equipment, providing reliable support for the refined management and safe and stable operation of equipment meteorological risks.

[0101] In one possible implementation, the early warning module 303 is specifically used to determine an initial risk coefficient based on predicted meteorological data and preset meteorological thresholds, wherein the initial risk coefficient is used to indicate the degree of risk of meteorological disasters occurring to the equipment to be warned; Based on spatial relationships, determine the risk correction coefficient; The initial risk coefficient is corrected based on the risk correction coefficient to obtain the final risk coefficient, which is used to indicate the early warning result.

[0102] In one possible implementation, the early warning module 303 is specifically used to acquire the device information of the device to be warned; Based on the equipment information, the preset meteorological threshold is corrected to obtain the corrected threshold; The initial risk coefficient is determined based on the predicted meteorological data and the corrected threshold.

[0103] In one possible implementation, the early warning module 303 is also used to obtain historical actual meteorological data of the area where the early warning device is located, as well as historical predicted meteorological data corresponding to the historical actual meteorological data, before determining the initial risk coefficient based on predicted meteorological data and preset meteorological thresholds. Based on historical actual meteorological data and historical forecast meteorological data, the forecast meteorological data is corrected to obtain corrected forecast meteorological data, which is then used to determine the initial risk coefficient based on the corrected forecast meteorological data and preset meteorological thresholds.

[0104] In one possible implementation, the early warning module 303 is specifically used to acquire terrain information of the location of the device to be warned; Risk correction coefficients are determined based on terrain information and spatial relationships.

[0105] In one possible implementation, the early warning module 303 is specifically used to determine a risk correction coefficient based on the overlapping range corresponding to the spatial relationship when there is an overlap between the geographical location indicated by the spatial relationship and the predicted meteorological disaster coverage. or, When the spatial relationship indicates that there is no overlap between the geographical location and the predicted meteorological disaster coverage area, the risk correction coefficient is determined based on the distance between the geographical location and the predicted meteorological disaster coverage area.

[0106] In one possible implementation, the device also includes a simulation module for dynamically simulating the evolution trend of meteorological disasters in the region based on predicted meteorological data and regional spatial distribution data.

[0107] The weather warning device provided in this embodiment of the invention can execute the weather warning method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0108] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0109] The following is a detailed reference. Figure 4 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0110] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0111] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the weather warning method of the embodiments of the present invention.

[0112] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0113] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the weather warning method shown in the above embodiments is implemented.

[0114] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0115] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A weather early warning method, characterized in that, The method includes: The system obtains the predicted meteorological disaster coverage and predicted meteorological data of the area where the warning device is located within a preset future time period based on the current time, as well as the geographical location of the warning device. Based on the predicted meteorological disaster coverage area and the geographical location, determine the spatial relationship between the geographical location and the predicted meteorological disaster coverage area; Based on the spatial relationship and the predicted meteorological data, an early warning is issued for the meteorological risk of the equipment to be warned, and an early warning result is obtained.

2. The method according to claim 1, characterized in that, The method of issuing a weather risk warning for the equipment to be warned based on the spatial relationship and the predicted meteorological data, and obtaining the warning result, includes: Based on the predicted meteorological data and preset meteorological thresholds, an initial risk coefficient is determined, wherein the initial risk coefficient is used to indicate the degree of risk of meteorological disasters occurring to the equipment to be warned; Based on the aforementioned spatial relationships, a risk correction coefficient is determined; Based on the risk correction coefficient, the initial risk coefficient is corrected to obtain the final risk coefficient, wherein the final risk coefficient is used to indicate the early warning result.

3. The method according to claim 2, characterized in that, The determination of the initial risk coefficient based on the predicted meteorological data and preset meteorological thresholds includes: Obtain the device information of the device to be warned; Based on the device information, the preset meteorological threshold is corrected to obtain the corrected threshold; The initial risk coefficient is determined based on the predicted meteorological data and the corrected threshold.

4. The method according to claim 2, characterized in that, Before determining the initial risk coefficient based on the predicted meteorological data and preset meteorological thresholds, the method further includes: Obtain historical actual meteorological data of the area where the device to be warned is located, as well as historical predicted meteorological data corresponding to the historical actual meteorological data; Based on the historical actual meteorological data and the historical predicted meteorological data, the predicted meteorological data is corrected to obtain corrected predicted meteorological data, which is used to determine the initial risk coefficient based on the corrected predicted meteorological data and the preset meteorological threshold.

5. The method according to claim 2, characterized in that, The determination of the risk correction coefficient based on the spatial relationship includes: Obtain the terrain information of the location of the device to be warned; The risk correction coefficient is determined based on the terrain information and the spatial relationship.

6. The method according to claim 2, characterized in that, The determination of the risk correction coefficient based on the spatial relationship includes: When the spatial relationship indicates that the geographical location overlaps with the predicted meteorological disaster coverage area, the risk correction coefficient is determined based on the overlapping range corresponding to the spatial relationship; or, When the spatial relationship indicates that the geographical location does not overlap with the predicted meteorological disaster coverage area, the risk correction coefficient is determined based on the distance between the geographical location and the predicted meteorological disaster coverage area.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: Based on the predicted meteorological data and the spatial distribution data of the region, the evolution trend of meteorological disasters in the region is dynamically simulated.

8. A weather warning device, characterized in that, The device includes: The acquisition module is used to acquire the predicted meteorological disaster coverage area and predicted meteorological data of the area where the warning device is located within a preset future time period based on the current time, as well as the geographical location of the warning device; The determination module is used to determine the spatial relationship between the predicted meteorological disaster coverage area and the predicted meteorological disaster coverage area based on the predicted meteorological disaster coverage area and the geographical location; The early warning module is used to provide early warning of the meteorological risk of the equipment to be warned based on the spatial relationship and the predicted meteorological data, and obtain the early warning result.

9. An electronic device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the weather warning method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the weather warning method according to any one of claims 1 to 7.

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