Ice accretion prediction method, system, medium, and product based on observation data correction

Abstract

The application relates to the technical field of aviation data recognition and analysis, and in particular to an icing prediction method and system based on observation data correction, a medium and a product. The method comprises the following steps: obtaining indirect indexes in a flight area of an aircraft through observation of a stationary satellite device; calculating initial membership degrees according to cloud top temperature and a first membership degree function; correcting the initial membership degrees according to cloud top phase states to obtain corrected membership degrees; obtaining direct indexes in the flight area in a flight interval; calculating direct membership degrees according to the direct indexes and a corresponding second membership degree function; selecting a recommended prediction area according to flight information, wherein the flight information comprises meteorological information; and calculating icing potential of a plurality of point positions in the recommended prediction area according to the corrected membership degrees and the direct membership degrees. The application can greatly improve the calculation efficiency and reduce the calculation cost in a complex flight scene of multiple aircrafts.

Description

Technical Field

[0001] This application relates to the field of aviation data identification and analysis technology, and in particular to icing prediction methods, systems, media and products based on observation data correction. Background Technology

[0002] Aircraft icing can cause a series of serious technical problems. For example, icing on the wings and tail disrupts the aerodynamic shape, leading to decreased lift, increased drag, and potentially earlier stall angle of attack; icing in the engine intakes can cause intake distortion or ice debris ingestion that damages the blades; icing on sensors such as the pitot tube can distort flight data; and icing on the control surfaces can jam the control surfaces, affecting maneuverability. These changes significantly increase fuel consumption, reduce flight performance, and in severe cases, can lead to stall or loss of control, threatening flight safety.

[0003] Based on this, traditional technologies have also proposed some methods for predicting aircraft icing.

[0004] For example, patent application CN118245744A proposes a method and system for correcting aircraft icing forecasts based on geostationary meteorological satellite data. The method includes acquiring meteorological reanalysis data and real-time satellite observation data in the target area; performing grid matching on the meteorological reanalysis data and the real-time satellite observation data using a preprocessing method; applying a first correction rule to perform a first correction on the third grid point using the fused meteorological data; applying a second correction rule to perform a second correction on the fourth grid point using the fused meteorological data; and using a fuzzy logic algorithm to determine the membership function coefficients for icing forecasting using the fused meteorological data at different altitudes of the target grid point. Based on this, the scheme provides a comprehensive icing forecasting method that uses real-time satellite data and meteorological reanalysis data to perform dual correction on the key coefficient (cloud top temperature coefficient) in the membership function, effectively improving the accuracy of icing prediction.

[0005] For example, patent application CN117093953A proposes a method and system for rapid correction and prediction of aircraft icing based on multi-meteorological element fusion. The method includes the following steps: acquiring the object to be corrected and its flight data; searching a pre-built correction database for a first icing event matching the object to be corrected according to a first search rule; if a matching first icing event is found, obtaining a preset first recommended correction coefficient; otherwise, searching the correction database for a second icing event similar to the object to be corrected according to a second search rule; inputting the event altitude and geographical location of the retrieved second icing event into a coefficient correction model to obtain a second recommended correction coefficient, and inputting the corresponding recommended correction coefficient into the icing event correction model. This invention introduces limited non-meteorological element conditions for result correction to traditional multi-meteorological element fusion algorithms, which improves the reliability of prediction results while ensuring the timeliness of prediction data.

[0006] However, the applicant noted that these icing prediction methods still have room for improvement in adaptability when faced with complex and dynamically changing real-world icing scenarios. Summary of the Invention

[0007] The purpose of this invention is to provide a method, system, medium, and product for predicting ice accumulation based on observation data correction, which partially solves or alleviates the above-mentioned shortcomings in the prior art and can further improve the efficiency of ice accumulation prediction.

[0008] To solve the aforementioned technical problems, the present invention specifically adopts the following technical solution:

[0009] A first aspect of the present invention is to provide an ice accumulation prediction method based on observational data correction, comprising the steps of:

[0010] S201, Indirect indicators within the flight area of ​​the spacecraft are obtained through observation using geostationary satellite equipment, including cloud top temperature;

[0011] S202, calculate the initial membership degree based on the cloud top temperature and the first membership function; wherein, the first membership function is used to define the probability of icing at different cloud top temperatures;

[0012] S203, The initial membership degree is corrected according to the cloud top phase state to obtain the corrected membership degree;

[0013] S204, Obtain direct indicators within the flight area, the direct indicators including: temperature and relative humidity;

[0014] S205, calculate the direct membership degree based on the direct index and the corresponding second membership function;

[0015] S206, Select a recommended prediction area based on flight information, wherein the flight information includes meteorological information, and the meteorological information includes cloud cover quantity and precipitation.

[0016] S207, calculate the icing potential of multiple points in the recommended prediction area based on the modified membership degree and the direct membership degree.

[0017] In some embodiments, S203 includes the step of:

[0018] The corresponding correction coefficient is selected from the correction table according to the type of cloud top phase; wherein, the correction table includes the correction coefficients preset based on at least one type of cloud top phase;

[0019] The corrected membership degree is calculated based on the correction coefficient and the initial membership degree.

[0020] In some embodiments, S206 includes the step of:

[0021] S2061, determine whether the number of cloud layers is greater than or equal to the first preset number of layers;

[0022] If the result of S2061 is yes, then proceed to step S2062:

[0023] S2062, determine whether the precipitation is greater than or equal to the preset precipitation;

[0024] If so, the cloud top height of the lower cloud layer is identified, and the recommended prediction area is determined based on the cloud top height of the lower cloud layer and the ground position; wherein, the lower cloud layer includes N layers of clouds from bottom to top;

[0025] If not, then the cloud region of the lower cloud layer is selected as the recommended prediction region.

[0026] In some embodiments, S206 further includes the step of:

[0027] If the result of S2061 is negative, then proceed to step S2063:

[0028] S2063, determine whether the precipitation is greater than or equal to the preset precipitation;

[0029] If so, the recommended prediction area is determined based on the cloud top height and the ground location of the cloud layer;

[0030] If not, then select the cloud layer as the recommended prediction area.

[0031] In some embodiments, the steps further include:

[0032] S2064, determine whether the number of cloud layers is greater than or equal to the second preset number of layers;

[0033] If so, proceed with the following steps:

[0034] S2065, Obtain the flight path density of the aircraft during its flight period; wherein, the flight path density is the effective number of aircraft passing through a set flight area within a set flight cycle, and the cloud layer is located in or passes through the flight area;

[0035] S2066, Select the recommended value of N based on the route density.

[0036] In some embodiments, S2065 includes the step of:

[0037] S20651, obtain at least one of the mission types and / or de-icing capabilities of the aircraft;

[0038] S20652, Generate an impact value for the aircraft based on the mission type and / or the de-icing capability;

[0039] S20653, determine the route density based on at least one of the said influence values.

[0040] In some embodiments, the steps further include:

[0041] Obtain the mission type and / or de-icing capability of the aircraft;

[0042] Based on the mission type and / or the de-icing capability, generate flight risks for the aircraft;

[0043] The recommended value for N is selected based on the aforementioned flight risk;

[0044] And / or, the task type includes at least one of the following: disaster relief task, exercise task, passenger transport task, freight transport task;

[0045] And / or, when N is greater than a preset threshold, the indirect indicators and / or the direct indicators of the lower cloud layer observed are retrieved from the backup detector;

[0046] And / or, the backup detector is a drone detector or a preceding aircraft.

[0047] A second aspect of the present invention is to provide an ice accumulation prediction system based on observation data correction, comprising:

[0048] The static acquisition module is used to acquire indirect indicators within the flight area of ​​the spacecraft through geostationary satellite equipment, including cloud top temperature.

[0049] The initial membership calculation module is used to calculate the initial membership degree based on the cloud top temperature and the first membership function; wherein, the first membership function is used to define the probability of icing at different cloud top temperatures;

[0050] The correction module is used to correct the initial membership degree according to the cloud top phase state to obtain the corrected membership degree;

[0051] A direct indicator acquisition module is used to acquire direct indicators within the flight area, including temperature and relative humidity.

[0052] The membership calculation module is used to calculate the direct membership degree based on the direct index and the corresponding second membership function.

[0053] The region selection module is used to select a recommended prediction region based on flight information, which includes meteorological information, including cloud cover quantity and precipitation conditions.

[0054] The potential calculation module is used to calculate the ice accumulation potential of multiple points in the recommended prediction area based on the modified membership degree and the direct membership degree.

[0055] A third aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the ice accumulation prediction method based on observation data correction as described in any embodiment of the present invention.

[0056] A fourth aspect of the present invention is to provide a computer program product comprising a computer program that, when executed by a processor, implements the ice accumulation prediction method based on observation data correction as described in any embodiment of the present invention.

[0057] Beneficial technical effects:

[0058] The icing potential calculation mechanism proposed in this invention can achieve a good balance between computational cost and aircraft scheduling safety. Specifically:

[0059] Firstly, this invention employs a two-factor fusion method for determining membership: first, the initial membership degree is determined based on the cloud top temperature, and then the initial membership degree is corrected through phase state adjustment. Furthermore, this two-factor fusion method can achieve continuous membership degree output.

[0060] For example, in this application, a continuous (non-discrete) initial membership degree can be determined from the temperature based on the cloud top temperature membership degree function, and then the initial membership degree can be corrected based on the cloud phase. That is to say, in this embodiment, relatively fine membership degree calculation can be performed (i.e., the membership degree can change relatively slowly and continuously in different scenarios).

[0061] Furthermore, based on the continuous output of membership degree based on cloud top temperature and then phase state in this embodiment, on the one hand, the membership degree can be accurately calculated directly based on static satellite observation data (i.e., a relatively single data source), thereby reducing the dependence on multi-source data.

[0062] On the other hand, the dual fusion recognition based on cloud top temperature and cloud phase also reduces the pressure to correct the number of clouds (i.e., there is no need to rely on the number of clouds to accurately distinguish the degree of membership).

[0063] Secondly, this invention selects recommended prediction areas by combining cloud cover quantity and precipitation, thereby controlling the cost of icing prediction while reserving relatively sufficient scheduling flexibility for aircraft.

[0064] Third, this invention determines the flight path density based on the effective number of aircraft, and then selects a recommended value for N based on the flight path density. This allows for the calculation of a flight path density that is more in line with the actual situation, thereby appropriately expanding the recommended prediction range (i.e., selecting a reasonable value for N), and thus controlling the investment cost of icing prediction while reserving relatively sufficient scheduling flexibility for aircraft.

[0065] Fourth, this invention constructs a restrictive scheduling mechanism for backup detectors. Specifically, this restrictive scheduling mechanism can refer to the selective use of backup detector types (such as UAV detectors or preceding aircraft), or it can refer to application in specific areas (such as scheduling backup detectors to conduct observations in the lower cloud layer). This approach can further achieve a better balance between cost and scheduling security. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. The elements or parts in the drawings are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0067] Figure 1 An example diagram of the first membership function provided in an embodiment of the present invention;

[0068] Figure 2 An example diagram of the temperature membership function provided in an embodiment of the present invention;

[0069] Figure 3 This is an example diagram of the relative humidity membership function provided in an embodiment of the present invention;

[0070] Figure 4 This is an example diagram of the membership function of liquid water content provided in an embodiment of the present invention;

[0071] Figure 5 An example diagram of the vertical velocity membership function provided in an embodiment of the present invention;

[0072] Figure 6 This is a flowchart illustrating an embodiment of the ice accumulation prediction method based on observation data correction provided by the present invention.

[0073] Figure 7 This is a schematic diagram of the structure of an ice accumulation prediction system based on observation data correction provided in an embodiment of the present invention;

[0074] Figure 8 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0075] 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0076] In this document, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" may be used interchangeably.

[0077] In this document, the terms "upper," "lower," "inner," "outer," "front," "rear," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the present invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0078] In this document, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0079] In this document, "and / or" includes any and all combinations of one or more of the listed related items.

[0080] In this article, "multiple" means two or more, that is, it includes two, three, four, five, etc.

[0081] As used in this specification, the term "about" typically means + / -5% of the value, more typically + / -4% of the value, more typically + / -3% of the value, more typically + / -2% of the value, even more typically + / -1% of the value, and even more typically + / -0.5% of the value.

[0082] In this specification, certain embodiments may be disclosed in a range-bound format. It should be understood that this "range-bound" description is merely for convenience and brevity and should not be construed as a rigid limitation on the disclosed range. Therefore, the description of a range should be considered as having specifically disclosed all possible subranges and the individual numerical values ​​within those ranges. For example, a description of the range 1-6 should be considered as having specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., and the individual numbers within those ranges, such as 1, 2, 3, 4, 5, and 6. This rule applies regardless of the breadth of the range.

[0083] Example 1:

[0084] It is important to note that the technical approach adopted in patent application CN118245744A is to correct the cloud top temperature coefficient through a dual correction mechanism to solve the problem of "misjudgment of cloud parameters leading to inaccurate key coefficients". That is, this patent first corrects the number of cloud layers, and then corrects the cloud top temperature coefficient (i.e., cloud top temperature membership degree), thereby improving the accuracy of membership degree calculation through coefficient optimization.

[0085] The technical means employed in this patent application are as follows:

[0086] 1) Select different cloud top temperature coefficient determination schemes based on the number of cloud layers. For example, when the number of cloud layers is no more than 1, satellite data (such as cloud top temperature or phase) is used as the basis for judgment. If the number of cloud layers is greater than 1, meteorological reanalysis data is used as the basis for judgment.

[0087] 2) When using satellite data (such as cloud top temperature or cloud phase) as the basis for judgment, a dual mechanism is employed to correct the cloud top temperature coefficient. Specifically, when the cloud phase is determined, the cloud top temperature coefficient is selected based on the cloud phase; or, when the cloud phase is unclear, it is directly determined by looking up the cloud top temperature in a table. In other words, the membership degree is determined based on either cloud phase or cloud top temperature as a single factor, and the final determined cloud top temperature coefficient (i.e., membership degree) is discontinuous and discrete. For example, when the cloud phase is supercooled water or warm water, the membership degree is directly set to 1; when the cloud phase is an unclear mixed phase, the membership degree is determined again based on temperature. Therefore, in different cloud phase scenarios, the membership degree is a non-continuous, jumping value.

[0088] In other words, this patent application attempts to use multiple observational data (such as satellite data and meteorological reanalysis data) to perform relatively accurate calculations and analyses of membership degrees. However, this method of determining multiple observational data also requires higher data acquisition costs to some extent. Furthermore, the jump-like, discrete membership degree prediction based on a single factor places extremely high demands on the accuracy of the original data acquisition (especially cloud phase products). Therefore, this patent application may face certain pressures regarding the accuracy and cost of data acquisition.

[0089] In stark contrast, this invention employs a two-factor fusion method for determining membership: first, the initial membership degree is determined based on cloud top temperature, and then the initial membership degree is corrected through phase state adjustment. Furthermore, this two-factor fusion method can achieve continuous membership degree output.

[0090] For example, in this application, a continuous (non-discrete) initial membership degree can be determined from the temperature based on the cloud top temperature membership degree function, and then the initial membership degree can be corrected based on the cloud phase. That is to say, in this embodiment, relatively fine membership degree calculation can be performed (i.e., the membership degree can change relatively slowly and continuously in different scenarios).

[0091] Furthermore, based on the continuous output of membership degree based on cloud top temperature and then phase state in this embodiment, on the one hand, the membership degree can be accurately calculated directly based on static satellite observation data (i.e., a relatively single data source), thereby reducing the dependence on multi-source data.

[0092] On the other hand, the dual fusion recognition based on cloud top temperature and cloud phase also reduces the pressure to correct the number of clouds (i.e., there is no need to rely on the number of clouds to accurately distinguish the degree of membership).

[0093] It is also worth noting that in this embodiment, cloud top temperature is selected as the first determination factor, that is, the initial membership degree is selected based on cloud top temperature. The accuracy of cloud top temperature can be used to determine the approximate range of the initial membership degree in the early stage with relative reliability. Subsequently, the cloud phase state inferred from cloud top temperature is used to fine-tune the initial membership degree, which can achieve more refined membership degree analysis.

[0094] In some embodiments, see Figure 6 This invention proposes an ice accumulation prediction method based on observation data correction, comprising the following steps:

[0095] S201, Indirect indicators within the flight area of ​​the spacecraft are obtained through observation using geostationary satellite equipment, including cloud top temperature;

[0096] S202, calculate the initial membership degree based on the cloud top temperature and the first membership function; wherein, the first membership function is used to define the probability of icing at different cloud top temperatures;

[0097] S203, The initial membership degree is corrected according to the cloud top phase state to obtain the corrected membership degree;

[0098] S204, Obtain direct indicators within the flight area, the direct indicators including: temperature and relative humidity;

[0099] S205, calculate the direct membership degree based on the direct index and the corresponding second membership function;

[0100] S206, Select a recommended prediction area based on flight information, wherein the flight information includes meteorological information, and the meteorological information includes cloud cover quantity and precipitation.

[0101] S207, calculate the icing potential of multiple points in the recommended prediction area based on the modified membership degree and the direct membership degree.

[0102] In some embodiments, cloud top temperature is the average effective cloud top radiation temperature over a horizontal spatial range when a region of the Earth's surface is covered by clouds. Exemplarily, it can be measured by inversion using satellite remote sensing technology.

[0103] In some embodiments, the flight area refers to a pre-defined or actual flight path and the surrounding specific spatial range.

[0104] The applicant noted that the phase of cloud top condensate influences the composition of the underlying cloud layer. Relatively warm cloud top temperatures mean that the cloud layer is likely predominantly composed of liquid water. Conversely, if the cloud top temperature is low enough to produce ice crystals, these crystals will grow and fall into the underlying cloud layer, sometimes causing the cloud to become completely ice-crystallized. Geresdi et al. (2005) observed that as cloud top temperatures decrease, precipitation undergoes a gradual transition from liquid-dominated to ice-crystallized. To assess the probability of a cloud layer containing liquid water rather than being completely ice-crystallized at a given cloud top temperature, a cloud top temperature membership function (i.e., the first membership function) can be plotted.

[0105] In some embodiments, see Figure 1 When the cloud top temperature (CTT) is ≥ -12°C, the first membership function reaches its peak, as liquid water dominates in these warmer clouds. As CTT decreases, the value gradually decays but never reaches zero. Although low-temperature cloud tops typically indicate ice crystal dominance, liquid water may still be present in these clouds if the rate of liquid water formation exceeds the rate of consumption. The first membership function gradually decays with decreasing cloud top temperature in the range of -12°C to approximately -30°C, then levels off.

[0106] In some embodiments, the icing probability refers to the number of icing events that occur at a certain cloud top temperature / the number of all historical flight data at the corresponding cloud top temperature (or the sum of the number of icing events and the number of non-icing events).

[0107] In some embodiments, the membership value corresponding to the cloud top temperature value in a pre-constructed first membership function can be used as the initial membership degree.

[0108] In some embodiments, the second membership function may include a temperature membership function and a relative humidity membership function.

[0109] In some embodiments, calculating the direct membership degree based on the direct index and the corresponding second membership function may include: determining the corresponding temperature membership degree based on the temperature value and the corresponding temperature membership function; and determining the corresponding relative humidity membership degree based on the relative humidity value and the corresponding relative humidity membership function.

[0110] In some embodiments, atmospheric temperature data from historical flight probes can be preprocessed to construct the temperature membership function. See also... Figure 2 The temperature range was discretized into several continuous intervals with 1℃ intervals. The frequency of icing events in each temperature interval was statistically analyzed. The ratio of the frequency of icing events in each interval to the total number of flight detection records in that interval was calculated and normalized to obtain the temperature normalization curve (i.e., the temperature membership function) under icing conditions. This normalization curve, except for a significant peak near 0℃, exhibits slight fluctuations in the range of -14℃ to -6℃, showing a rapid decreasing trend from the peak to both ends. The peak near 0℃ reflects that the atmospheric conditions in this temperature range are prone to inducing icing; when the temperature is higher than this peak range, the probability of icing decreases rapidly. Based on the normalization results above, the membership function of ice accumulation (as shown by the yellow curve in the figure) exhibits a piecewise characteristic. In the range of -28℃ to -14℃, the closer the temperature is to -14℃, the higher the membership value. In the range of -14℃ to -1℃, the membership function value is stable at 1, indicating that the probability of meeting the ice accumulation condition is highest in this temperature range. Thereafter, as the temperature increases, the membership function shows a rapid decreasing trend.

[0111] In some embodiments, to construct the relative humidity membership function under icing conditions, this invention first preprocesses the atmospheric relative humidity data from historical flight probes. See also... Figure 3The relative humidity range was discretized into several continuous intervals with 5% increments. The frequency of icing events within each relative humidity interval was statistically analyzed. The ratio of the frequency of icing events in each interval to the total number of flight detection records in that interval (i.e., the icing probability) was calculated and normalized to obtain the relative humidity normalization curve (i.e., the relative humidity membership function) under icing conditions. Referring to the above normalization results, the membership function of icing (as shown by the yellow curve in the figure) exhibits a piecewise characteristic. Within the relative humidity range of 60% to 90%, the closer the relative humidity is to 90%, the higher the membership value. When the relative humidity is greater than 90%, the membership function value stabilizes at 1, indicating that the relative humidity range has the highest probability of meeting the icing conditions.

[0112] In some embodiments, direct membership includes temperature membership and relative humidity membership.

[0113] In some embodiments, a decision tree model (as shown in Table 1) can be used to calculate the initial ice accumulation potential.

[0114] In some embodiments, different vertical layers can be applied to calculate the initial icing potential value under different weather scenarios. For example, different vertical layers can be selected to calculate the initial icing potential value under single-layer cloud precipitation, multi-layer cloud precipitation, and no precipitation. For example, the existing weather scenario can be determined first, and different vertical layers can be applied to calculate the initial icing potential (InitialCIP) under different scenarios. For example, four different scenarios can be set up: clear sky, snowfall, single-layer cloud, and multi-layer cloud, and the aircraft icing situation under single-layer cloud and multi-layer cloud precipitation and no precipitation under specific scenarios is discussed. The equations used are shown in Table 1.

[0115] Among them, M T This refers to the temperature membership function, M. RH This refers to the relative humidity membership function, M. CTT This refers to the cloud top temperature membership function.

[0116] Table 1

[0117]

[0118] a. Clear sky

[0119] When no clouds are detected in a grid area (i.e., clear sky), the initial aircraft icing potential for that area is determined to be 0. This is because in clear sky conditions, there is a lack of supercooled water droplets and cloud droplets in the atmosphere, and there are no material carriers that can collide with the aircraft surface and cause icing. Furthermore, it is difficult to reach the saturated vapor pressure threshold that allows gaseous water vapor to condense directly into liquid water on the aircraft surface. Therefore, the initial icing potential for that area is determined to be 0.

[0120] b. Single-layer cloud

[0121] When the relative humidity is continuous throughout the entire vertical height of the cloud layer, it is considered that a single-layer cloud exists. First, the cloud top temperature membership function (M...) is used... CTT A preliminary assessment of the cloud phase is made, followed by consideration of the temperature membership function (M) at each altitude level. T ) and relative humidity membership function (M RH Calculate the initial icing potential. If liquid precipitation is observed below the cloud layer, the aircraft icing environment becomes more complex. At this point, when liquid precipitation is superimposed with a warm cloud top temperature (CTT > -15℃), it indicates that the collision-coalescence process of water droplets within the cloud is active. Therefore, if the ambient temperature is within the suitable icing range (-20-0℃), the risk of aircraft icing exists not only within the lowest cloud layer but also in the area below the cloud bottom. From the ground upwards, the icing potential extends at least to the cloud base and can further penetrate into the cloud interior; its initial icing potential is defined as:

[0122]

[0123] When ground meteorological observations record snowfall, it can be determined that all liquid water droplets in the clouds and the path of falling snow particles have been completely transformed into solid ice and snow particles. At this time, the aircraft icing potential in the area is determined to be 0.

[0124] c. Multi-layered cloud

[0125] When multiple cloud layers exist in the vertical structure of the atmosphere, the icing potential of each cloud layer needs to be determined independently. Specifically, the cloud top position of the upper cloud layer can be directly identified through satellite observation, so its icing potential can be calculated based on satellite remote sensing data. However, due to the obstruction of the upper cloud layer, direct observation of the lower cloud layer is limited, and numerical model data is needed to help determine its spatial location and cloud top height. In this model, if a dry layer exists, the simulated relative humidity threshold for this layer is below 84%, which is used to distinguish the vertical boundaries of the upper and lower cloud layers. Based on this, the lower cloud layer has independent cloud top temperature characteristics, and the model will calculate the icing layer thickness and icing potential separately for this layer to avoid interference between the meteorological parameters of the upper and lower cloud layers. In addition, when precipitation is observed at the ground, it is only applied to the lowest cloud layer and the elevation range below it, and does not extend upward to the middle and upper cloud layers. This is because the formation of ground precipitation is directly related to the collision-coalescence and melting of water droplets in the lowest cloud layer, and has a weaker correlation with the icing environment of the middle and upper cloud layers. Its initial icing potential is defined as:

[0126]

[0127] When ground meteorological observations record snowfall, it can be determined that all liquid water droplets in the clouds and the path of falling snow particles have been completely transformed into solid ice and snow particles. At this time, the icing potential of aircraft at all vertical heights in the area is determined to be 0.

[0128] In some embodiments, S206 includes the step of:

[0129] S2061, determine whether the number of cloud layers is greater than or equal to the first preset number of layers;

[0130] If the result of S2061 is yes, then proceed to step S2062:

[0131] S2062, determine whether the precipitation is greater than or equal to the preset precipitation;

[0132] If so, the cloud top height of the lower cloud layer is identified, and the recommended prediction area is determined based on the cloud top height of the lower cloud layer and the ground position; wherein, the lower cloud layer includes N layers of clouds from bottom to top;

[0133] If not, then the cloud region of the lower cloud layer is selected as the recommended prediction region.

[0134] In this embodiment, the present invention screens recommended prediction areas by combining cloud cover quantity and precipitation, thereby controlling the cost of icing prediction while reserving relatively sufficient scheduling flexibility for aircraft.

[0135] For example, if there are many clouds and the precipitation is also high, this embodiment preferably determines the recommended prediction area based on the cloud top height of the lower clouds and the ground location. For example, the icing potential of multiple vertical layers from the lower clouds to the ground can be calculated. In this embodiment, expanding the recommended prediction area within this specific interval can reserve relatively sufficient scheduling space for the aircraft.

[0136] It should be understood that by calculating the icing potential at multiple vertical levels, the scope of icing potential calculation can be expanded, thereby avoiding uncontrollable emergencies in situations with high flight density. For example, when multiple aircraft operate at close range and at multiple altitudes, an emergency evasive maneuver by a single aircraft may trigger a conflict; or, busy air route networks are often saturated, making it difficult to find safe alternative routes for multiple aircraft in a short period of time.

[0137] In some embodiments, if there are many clouds and the precipitation is low, this embodiment preferably uses the cloud region of the lower cloud layer as the recommended prediction region, that is, calculates the icing potential of the lower cloud layer. This reduces computational costs and also minimizes scheduling risks.

[0138] Furthermore, in some embodiments, the final icing potential at a given point can be calculated based on the initial icing potential at that point:

[0139] (1).

[0140] in, The initial icing potential, which includes temperature, relative humidity, and cloud top temperature, comprehensively indicates the magnitude of the target cloud's icing potential for aircraft. However, it is insufficient to provide guidance for aircraft icing forecasting. The project team used CLW (liquid water content in clouds) and W (vertical velocity) to adjust the initial icing potential. Membership function for liquid water content (see [link]). Figure 4 ); For the vertical velocity membership function (see [link]), please refer to [link] for details. Figure 5 ). Figure 4 In the diagram, the horizontal axis corresponds to different cloud liquid water contents, and the vertical axis corresponds to different cloud liquid water content membership degrees. Figure 5 In the diagram, the horizontal axis corresponds to different vertical velocities, and the vertical axis corresponds to different membership degrees of the vertical velocity. When there is upward motion, the adjustment factor is defined as... When there is a downward vertical velocity, the adjustment factor is defined as , where a and b are the influence coefficients of vertical velocity and liquid water content on ice accumulation potential, respectively, a=0.6 and b=0.4.

[0141] In this embodiment, the ice accumulation potential calculation mechanism proposed in this invention can achieve a good balance between computational difficulty and computational cost.

[0142] Furthermore, in this embodiment, the present invention proposes a hierarchical prediction mechanism for icing potential. This hierarchical prediction mechanism refers to selecting different processing methods for different data types (such as temperature, relative humidity, and cloud top temperature). Specifically, for indirect indicators (i.e., cloud top temperature), the initial membership degree is corrected using the cloud top phase state to obtain the corrected membership degree; for direct indicators (i.e., temperature and relative humidity), the direct membership degree can be directly obtained based on the corresponding membership function. This approach greatly simplifies the calculation, allowing for more targeted calculation of icing potential from large amounts of flight data, thereby reducing the execution difficulty of the computer system.

[0143] Furthermore, based on this, the present invention can improve the flexibility and safety of aircraft scheduling, and is particularly suitable for complex scenarios involving multiple aircraft (such as airborne real-time forecasting and large-scale flight path monitoring). Specifically, the present invention can select recommended prediction areas based on flight information, that is, dynamically adjust the range of the prediction area according to the real-time acquired cloud cover and precipitation, thereby expanding the recommended prediction area while controlling computational costs.

[0144] In some embodiments, S203 includes the step of:

[0145] The corresponding correction coefficient is selected from the correction table according to the type of cloud top phase; wherein, the correction table includes the correction coefficients preset based on at least one type of cloud top phase;

[0146] The corrected membership degree is calculated based on the correction coefficient and the initial membership degree.

[0147] In some embodiments, the cloud top phase can refer to the macroscopic thermodynamic state of water substances in the top cloud layer, mainly including three types: ice cloud, water cloud, and mixed cloud.

[0148] In some embodiments, the cloud top phase can be classified into four types: liquid water, supercooled water, mixed phase, and ice phase.

[0149] In some embodiments, cloud top temperature membership is defined as:

[0150] .

[0151] The original cloud top temperature membership degree can also be referred to as the initial membership degree. CCLP refers to the cloud top phase membership coefficient (i.e., correction coefficient), which can be obtained from a preset correction table. The cloud top temperature membership degree can thus refer to the corrected membership degree.

[0152] For example, see Table 2 for a correction table.

[0153] Table 2

[0154]

[0155] In some embodiments, the cloud top phase membership coefficient C CLP It can be built based on data from the Fengyun-4 (FY-4) meteorological satellite.

[0156] The applicant noted that the top of clouds in the liquid water phase consists of liquid water droplets with a temperature above 0°C. Although these droplets are not supercooled, the surface of an aircraft often remains below 0°C due to the low-temperature environment at high altitudes (especially in high-altitude airspace). When these liquid water droplets collide with the low-temperature surface of the aircraft, they rapidly cool to below freezing point through heat conduction, transforming into supercooled droplets that immediately freeze. This results in a high rate of icing and a high risk of icing. Therefore, a correction factor of 1 can be set.

[0157] Alternatively, the core of the supercooled water phase is that the water droplets remain liquid even when their temperature is below 0°C, which is the most critical cause of aircraft icing. Supercooled water droplets have poor thermodynamic stability, and once they come into contact with the aircraft body (low-temperature surface), they will freeze instantly and adhere firmly to the surface of the body without any additional cooling process, resulting in a high risk of icing. Therefore, a correction factor of 1 can be set.

[0158] Alternatively, in a mixed-phase cloud top, liquid water droplets, supercooled water droplets, and ice crystals coexist, with icing potential falling between the supercooled water (liquid water) phase and the ice phase. On the one hand, ice crystals themselves cannot provide additional water for icing and will occupy part of the cloud space, resulting in a lower number and distribution density of supercooled water droplets than in the pure supercooled water phase. On the other hand, the mixed distribution of ice crystals and water droplets will hinder the effective contact between some supercooled water droplets and the aircraft body. The icing speed and severity are weaker than those of liquid water and supercooled water phases, but there is still a clear risk of icing. Therefore, a correction factor of 0.8 can be set.

[0159] Alternatively, when the cloud top is in a pure ice phase, due to the influence of vertical motion, most water droplets will freeze into ice after reaching the cloud top with the updraft. The entire cloud layer lacks supercooled water droplets, so it cannot provide the necessary environment for aircraft icing, that is, the risk of icing is low. Therefore, the correction factor can be set to 0.

[0160] Please refer to Table 2 for details. In some embodiments, the cloud phase state can be pre-coded and the corresponding cloud phase state membership coefficient can be set. When the cloud phase state code is 1, the cloud phase state membership coefficient of the pixel is determined to be 1; when the cloud phase state code is 2, the cloud phase state membership coefficient of the pixel is determined to be 1; when the cloud phase state code is 3, the cloud phase state membership coefficient of the pixel is determined to be 0.8; when the cloud phase state code is 4, the cloud phase state membership coefficient of the pixel is determined to be 0.

[0161] As an exemplary embodiment, when the cloud top is in the icy phase, the cloud quantity is a single layer, and there is ground precipitation, the correction factor is 0.2.

[0162] It should be understood that the present invention preferably sets the cloud top phase membership coefficient corresponding to the mixed phase state to a value between high and low coefficients. This can, to a certain extent, avoid misjudging the risk of icing, reduce unnecessary flight detours and de-icing operations, and reduce the waste of airspace resources. Specifically, the mixed phase state still contains supercooled water droplets. If the correction coefficient is too low, the actual risk of icing will be underestimated. The preferred correction coefficient of 0.8 can more accurately warn of potential hazards where there is a clear possibility of icing but the icing risk has not reached its peak. This allows pilots or decision-making systems to take targeted monitoring and warning measures (rather than ignoring or over-responding), achieving a better balance between accuracy and efficiency.

[0163] In this embodiment, the present invention enables the correction mechanism to limit the computational cost to a certain extent when the recommended prediction area (or computational range) expands by setting the correction coefficient in a hierarchical manner (here, hierarchical setting refers to setting the corresponding correction coefficient for different cloud top phases).

[0164] In some embodiments, S206 further includes the step of:

[0165] If the result of S2061 is negative, then proceed to step S2063:

[0166] S2063, determine whether the precipitation is greater than or equal to the preset precipitation;

[0167] If so, the recommended prediction area is determined based on the cloud top height and the ground location of the cloud layer;

[0168] If not, then select the cloud layer as the recommended prediction area.

[0169] In this embodiment, the present invention selects recommended prediction areas by combining cloud cover quantity and precipitation, thereby controlling the cost of icing prediction while reserving relatively sufficient scheduling flexibility for aircraft.

[0170] For example, if the number of clouds is small and the precipitation is large, this embodiment preferably determines the recommended prediction area based on the cloud top height and ground location. For example, the icing potential of multiple vertical layers from the cloud top to the ground can be calculated. In this embodiment, expanding the recommended prediction area under this special condition allows for relatively sufficient scheduling space for the aircraft.

[0171] For example, if there are many clouds and the precipitation is low, this embodiment preferably uses the cloud area as the recommended prediction area.

[0172] In other words, this invention proposes a restricted selection mechanism for the recommended prediction region, which allows for the expansion of the recommended prediction region under specific circumstances. This improves the flexibility of aircraft scheduling while controlling computational costs. Alternatively, this restricted selection mechanism achieves a good balance between security and cost.

[0173] In this embodiment, the present invention proposes a mechanism for selecting recommended prediction regions based on ice accumulation probability, specifically:

[0174] In some embodiments, when the probability of icing is high (i.e., when rainfall is heavy), the present invention preferably sacrifices some computational efficiency to allow for more flexible scheduling of aircraft, i.e., calculating multiple vertical layers from the cloud layer to the ground and capturing vertical changes in temperature, humidity, and supercooled water content, in order to further improve the flexibility of multi-aircraft scheduling strategies.

[0175] Alternatively, by conducting calculations on a broader scale, focusing not only on the current flight altitude but also on the icing potential of other vertical regions, a basis for decision-making regarding emergency climbs or descents can be provided, thereby improving the safety of the aircraft during flight.

[0176] In some embodiments, when the probability of icing is low (i.e., when the rainfall is low), the present invention preferably improves the computational efficiency on the basis of basically correct calculation, that is, it assumes that the risk of icing comes from supercooled water in the clouds, and directly uses the cloud area as the recommended prediction area, thereby greatly reducing the consumption of computing resources and response time.

[0177] In some embodiments, the steps further include:

[0178] S2064, determine whether the number of cloud layers is greater than or equal to the second preset number of layers;

[0179] If so, proceed with the following steps:

[0180] S2065, Obtain the flight path density of the aircraft during its flight period; wherein, the flight path density is the effective number of aircraft passing through a set flight area within a set flight cycle, and the cloud layer is located in or passes through the flight area;

[0181] S2066, Select the recommended value of N based on the route density.

[0182] In some embodiments, the absolute number of aircraft can be weighted to obtain an effective number, thereby enabling the route density to reflect the degree of concentration of aircraft susceptible to icing in the actual flight area, and thus to classify icing risk levels in a more realistic manner.

[0183] In this embodiment, the present invention determines the flight path density based on the effective number of aircraft, and then selects a recommended value for N based on the flight path density. This method can calculate a flight path density that is more in line with the actual situation, thereby appropriately expanding the recommended prediction range (i.e., selecting a reasonable value for N), and thus controlling the investment cost of icing prediction while reserving relatively sufficient scheduling flexibility for aircraft.

[0184] For example, the higher the flight route density, the more preferably a larger N value is selected in this embodiment to expand the recommended prediction area to a greater extent. Alternatively, the lower the flight route density, the more preferably a smaller N value is selected in this embodiment to expand the recommended prediction area to a smaller extent.

[0185] In some embodiments, S2065 includes the step of:

[0186] S20651, obtain at least one of the mission types and / or de-icing capabilities of the aircraft;

[0187] S20652, Generate an impact value for the aircraft based on the mission type and / or the de-icing capability;

[0188] S20653, determine the route density based on at least one of the said influence values.

[0189] For example, the task type includes at least one of the following: disaster relief task, exercise task, passenger transport task, and freight transport task. It should be understood that the priority of the aforementioned task types decreases in that order.

[0190] In some embodiments, priorities can be set for different task types, with different priorities corresponding to different impact values ​​(or weights). For example, task types with higher priorities have larger impact values ​​and greater weights when calculating flight route density. For instance, the impact value can be used as a weight when calculating flight route density.

[0191] For example, task A has a priority of level one, task B has a priority of level two, and task C has a priority of level three (level one has a higher priority than level two, and level two has a higher priority than level three; and the impact value corresponding to different task priorities can be preset).

[0192] The impact value for the aircraft performing mission A is 1.3, the impact value for the aircraft performing mission B is 1.0, and the impact value for the aircraft performing mission C is 0.9. There are 10 aircraft performing mission A, 10 aircraft performing mission B, and 10 aircraft performing mission C.

[0193] The final flight path density is the weighted sum of the absolute number of aircraft performing each mission type and their corresponding impact values. For example, flight path density = absolute number of aircraft performing mission A × impact value of mission A + absolute number of aircraft performing mission B × impact value of mission B + absolute number of aircraft performing mission C × impact value of mission C. In some embodiments, de-icing capability levels corresponding to different de-icing capabilities can be preset in advance. For example, the stronger the de-icing capability and the higher the de-icing capability level, the more de-icing devices and / or de-icing methods the corresponding aircraft may be equipped with (such as hot air de-icing, electric heating de-icing, chemical de-icing, etc.).

[0194] In some embodiments, aircraft with stronger de-icing capabilities are better adapted to icing environments, and their corresponding impact values ​​are smaller.

[0195] For example, different influence values ​​can be preset for each de-icing capability level. Flight path density = weighted sum of the absolute number of aircraft at each de-icing capability level and the corresponding influence value.

[0196] In some embodiments, generating impact values ​​based on task type and de-icing capability may include:

[0197] Determine the first impact value based on the task type;

[0198] The second impact value is determined based on the de-icing capacity;

[0199] Calculate the average of the first influence value and the second influence value, and use the average value as the influence value.

[0200] In some embodiments, the steps further include:

[0201] Obtain the mission type and / or de-icing capability of the aircraft;

[0202] Based on the mission type and / or the de-icing capability, generate flight risks for the aircraft;

[0203] The recommended value for N is selected based on the aforementioned flight risk.

[0204] In some embodiments, the higher the priority of the mission type, the higher the flight risk level; the stronger the de-icing capability, the lower the flight risk level.

[0205] In some embodiments, flight risk levels corresponding to different mission types and different de-icing capabilities can be preset.

[0206] In some embodiments, generating flight risks for the aircraft based on the mission type and the de-icing capability may include:

[0207] Determine the corresponding first risk level based on the task type;

[0208] The corresponding second risk level is determined based on the de-icing capability;

[0209] The average of the first risk level and the second risk level is taken as the flight risk level.

[0210] In some embodiments, the flight risk level is used to characterize the magnitude of flight risk. For example, the higher the flight risk level, the greater the flight risk.

[0211] In some embodiments, the greater the flight risk, the larger the recommended value of N; the lower the flight risk, the smaller the recommended value of N.

[0212] In this embodiment, the present invention proposes two methods for determining the recommended value of N. One method is to determine the impact value based on the mission type and / or de-icing capability, determine the flight path density based on the impact value, and determine the recommended value of N based on the flight path density. The other method is to determine the flight risk based on the mission type and / or de-icing capability, and determine the recommended value of N based on the flight risk.

[0213] In other words, Method 1 (determining the recommended value of N based on flight route density) focuses more on meeting the resource optimization needs of dense flight route areas, while Method 2 (determining the recommended value of N based on flight risk) focuses more on meeting the safety needs of individual aircraft. In some embodiments, both Method 1 and Method 2 can obtain a computing resource allocation method that fits the actual needs (e.g., the higher the flight route density, the larger the value of N, and the more cloud layers are calculated).

[0214] In some embodiments, the steps further include:

[0215] When N is greater than a preset threshold, the indirect indicators and / or direct indicators of the lower cloud layer observed are retrieved from the backup detector.

[0216] In some embodiments, the backup detector may be a drone detector or a preceding aircraft.

[0217] It should be understood that backup detectors can directly collect key parameters such as temperature, relative humidity, and cloud top temperature in actual flight environments (such as within clouds), avoiding errors and uncertainties that may occur during satellite data collection. In other words, backup detectors acquire more direct and reliable data. However, using backup detectors to observe indirect and / or direct cloud indicators incurs higher costs (such as deployment, maintenance, and data transmission costs, and introducing new backup detectors further complicates the flight scenario).

[0218] Therefore, in this embodiment, the present invention constructs a restrictive scheduling mechanism for backup detectors. Specifically, this restrictive scheduling mechanism can refer to the selective use of backup detector types (such as UAV detectors or preceding aircraft), or it can refer to application in specific areas (such as scheduling backup detectors to conduct observations in the lower cloud layer). This achieves a better balance between cost and scheduling security.

[0219] For example, when the flight risk is high, it is preferable to call upon preceding aircraft and drone detectors to observe the target cloud layer (or a specific cloud layer in the recommended prediction area); when the flight risk is low, preceding aircraft can be called upon.

[0220] In other words, this embodiment focuses on restricting the use of backup detection resources in the lower cloud region, which can improve security while controlling scheduling costs.

[0221] For example, for areas other than the lower cloud layer, such as the area between the lower cloud layer and the ground, conventional detection methods (such as satellites) can still be used to obtain the data.

[0222] In some embodiments, a preceding aircraft may refer to a preceding flight.

[0223] In some embodiments, see Figure 7 The present invention also proposes an ice accumulation prediction system based on observation data correction, comprising:

[0224] The static acquisition module is used to acquire indirect indicators within the flight area of ​​the spacecraft through geostationary satellite equipment, including cloud top temperature.

[0225] The initial membership calculation module is used to calculate the initial membership degree based on the cloud top temperature and the first membership function; wherein, the first membership function is used to define the probability of icing at different cloud top temperatures;

[0226] The correction module is used to correct the initial membership degree according to the cloud top phase state to obtain the corrected membership degree;

[0227] A direct indicator acquisition module is used to acquire direct indicators within the flight area, including temperature and relative humidity.

[0228] The membership calculation module is used to calculate the direct membership degree based on the direct index and the corresponding second membership function.

[0229] The region selection module is used to select a recommended prediction region based on flight information, which includes meteorological information, including cloud cover quantity and precipitation conditions.

[0230] The potential calculation module is used to calculate the ice accumulation potential of multiple points in the recommended prediction area based on the modified membership degree and the direct membership degree.

[0231] It should be understood that the ice accumulation prediction system based on observation data correction is used to implement the steps described in any embodiment of the present invention.

[0232] In some embodiments, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in any embodiment of the present invention.

[0233] In some embodiments, the present invention also provides a computer program product comprising a computer program that, when executed by a processor, implements the steps described in any embodiment of the present invention.

[0234] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0235] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a computer terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0236] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

[0237] In some embodiments, this application also provides a schematic block diagram of the structure of a computer device, please see... Figure 8 Computer programs can be used in situations such as Figure 8 It runs on the computer device shown. Figure 8 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The memory may include non-volatile storage media and internal memory. The non-volatile storage media may store an operating system and computer programs. The computer programs include program instructions that, when executed, cause the processor to perform arbitrary methods. The processor provides computational and control capabilities to support the operation of the entire computer device. The internal memory provides an environment for the execution of the computer programs in the non-volatile storage media; when executed by the processor, these programs cause the processor to perform arbitrary methods. The network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 8The structures shown are merely block diagrams of a portion of the structure related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. It should be understood that the processor may be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

Claims

1. An ice accumulation prediction method based on observational data correction, characterized in that, Including the following steps: S201, Indirect indicators within the flight area of ​​the spacecraft are obtained through observation using geostationary satellite equipment, including cloud top temperature; S202, calculate the initial membership degree based on the cloud top temperature and the first membership function; wherein, the first membership function is used to define the probability of icing at different cloud top temperatures; S203, The initial membership degree is corrected according to the cloud top phase state to obtain the corrected membership degree; S204, Obtain direct indicators within the flight area, the direct indicators including: temperature and relative humidity; S205, calculate the direct membership degree based on the direct index and the corresponding second membership function; S206, Selecting a recommended forecast area based on flight information, wherein the flight information includes meteorological information, which includes cloud cover quantity and precipitation; wherein, S206 further includes: S2061, determine whether the number of cloud layers is greater than or equal to the first preset number of layers; If the result of S2061 is yes, then proceed to step S2062: S2062, determine whether the precipitation is greater than or equal to the preset precipitation; If so, the cloud top height of the lower cloud layer is identified, and the recommended prediction area is determined based on the cloud top height of the lower cloud layer and the ground position; wherein, the lower cloud layer includes N layers of clouds from bottom to top; If not, then the cloud region of the lower cloud layer is selected as the recommended prediction region; S207, calculate the icing potential of multiple points in the recommended prediction area based on the modified membership degree and the direct membership degree.

2. The method according to claim 1, characterized in that, S203 includes the following steps: The corresponding correction coefficient is selected from the correction table according to the type of cloud top phase; wherein, the correction table includes the correction coefficients preset based on at least one type of cloud top phase; The corrected membership degree is calculated based on the correction coefficient and the initial membership degree.

3. The method according to claim 1, characterized in that, S206 also includes the following steps: If the result of S2061 is negative, then proceed to step S2063: S2063, determine whether the precipitation is greater than or equal to the preset precipitation; If so, the recommended prediction area is determined based on the cloud top height and the ground location of the cloud layer; If not, then select the cloud layer as the recommended prediction area.

4. The method according to claim 1 or 3, characterized in that, It also includes the following steps: S2064, determine whether the number of cloud layers is greater than or equal to the second preset number of layers; If so, proceed with the following steps: S2065, Obtain the flight path density of the aircraft during its flight period; wherein, the flight path density is the effective number of aircraft passing through a set flight area within a set flight cycle, and the cloud layer is located in or passes through the flight area; S2066, Select a recommended value for N based on the route density.

5. The method according to claim 4, characterized in that, S2065 includes the following steps: S20651, obtain at least one of the mission types and / or de-icing capabilities of the aircraft; S20652, Generate an impact value for the aircraft based on the mission type and / or the de-icing capability; S20653, determine the route density based on at least one of the said influence values.

6. The method according to claim 1 or 3, characterized in that, It also includes the following steps: Obtain the mission type and / or de-icing capability of the aircraft; Based on the mission type and / or the de-icing capability, generate flight risks for the aircraft; The recommended value for N is selected based on the aforementioned flight risk; And / or, the task type includes at least one of the following: disaster relief task, exercise task, passenger transport task, freight transport task; And / or, when N is greater than a preset threshold, the indirect indicators and / or the direct indicators of the lower cloud layer observed are retrieved from the backup detector; And / or, the backup detector is a drone detector or a preceding aircraft.

7. An ice accumulation prediction system based on observation data correction, characterized in that, include: The static acquisition module is used to acquire indirect indicators within the flight area of ​​the spacecraft through geostationary satellite equipment, including cloud top temperature. The initial membership calculation module is used to calculate the initial membership degree based on the cloud top temperature and the first membership function; wherein, the first membership function is used to define the probability of icing at different cloud top temperatures; The correction module is used to correct the initial membership degree according to the cloud top phase state to obtain the corrected membership degree; A direct indicator acquisition module is used to acquire direct indicators within the flight area, including temperature and relative humidity. The membership calculation module is used to calculate the direct membership degree based on the direct index and the corresponding second membership function. The region selection module is used to select a recommended prediction region based on flight information, which includes meteorological information, including cloud cover number and precipitation conditions. The region selection module is also used to determine whether the cloud cover number is greater than or equal to a first preset number of layers. If the result is yes, then proceed as follows: determine whether the precipitation is greater than or equal to the preset precipitation; if the result is yes, identify the cloud top height of the lower cloud layer in the cloud layer, and determine the recommended prediction area based on the cloud top height of the lower cloud layer and the ground position; wherein, the lower cloud layer includes N layers of clouds from bottom to top; If not, then the cloud region of the lower cloud layer is selected as the recommended prediction region; The potential calculation module is used to calculate the ice accumulation potential of multiple points in the recommended prediction area based on the modified membership degree and the direct membership degree.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the ice accumulation prediction method based on observation data correction as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the ice accumulation prediction method based on observation data correction as described in any one of claims 1 to 6.

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