Automatic activation control method of intelligent anti-icing and snow-removing control system

Abstract

The embodiment of the invention provides an automatic activation control method for an intelligent anti-icing and snow-removing control system, and the method comprises the steps: dividing an anti-icing and snow-removing heating region based on the real-time temperature and icing condition data of the surface of an airplane; when it is detected that the icing risk exists in the specific area, the heating device in the corresponding area is activated; dynamically adjusting the power of each heating area according to the environment condition and the flight state; and the working state of each heating area is recorded and analyzed, and the future partitioning strategy is optimized to improve the energy efficiency ratio. Through the scheme of the embodiment of the invention, the problem of how to intelligently partition the heating area to solve the balance problem between the energy consumption and the anti-icing efficiency can be solved.

Description

Technical Field

[0001] The present application relates to the technical field of avionics systems, and in particular to an automatic activation control method for an intelligent anti-icing and snow removal control system. Background Art

[0002] The anti-icing and snow removal control system can monitor the temperature, humidity and ice and snow accumulation on the aircraft surface in real time through integrated sensors, and automatically activate the heating system based on this data to effectively prevent the impact of frozen snow on aircraft performance and safety. However, this method also faces a key technical problem, that is, how to intelligently partition the heating area to achieve a balance between energy consumption and anti-icing efficiency. While ensuring that each key area can be properly heated to achieve an effective anti-icing effect, it is also necessary to avoid energy waste caused by excessive heating, which places higher requirements on the energy efficiency management and optimization of the entire system. Summary of the invention

[0003] In view of this, an embodiment of the present disclosure provides an automatic activation control method for an intelligent anti-icing and snow removal control system, which at least partially solves the problems existing in the prior art.

[0004] The present invention provides an automatic activation control method for an intelligent anti-icing and snow removal control system, comprising:

[0005] Based on the real-time temperature and icing data of the aircraft surface, anti-icing and snow removal heating areas are divided;

[0006] When a risk of icing is detected in a specific area, the heating device of the corresponding area is activated;

[0007] Dynamically adjust the power of each heating area according to environmental conditions and flight status;

[0008] Record and analyze the working status of each heating zone, and optimize future zoning strategies to improve energy efficiency.

[0009] Preferably, the step of dividing the anti-icing and snow removal heating area based on the real-time temperature and icing condition data of the aircraft surface further comprises:

[0010] Based on the real-time temperature data of each part of the aircraft surface, calculate the average temperature of each area;

[0011] Analyze historical icing data to determine the icing risk level in each area;

[0012] Divide the aircraft surface into multiple anti-icing and snow removal heating zones based on average temperature and icing risk level;

[0013] Adjust the heating activation threshold for each zone to optimize the balance between energy consumption and anti-icing efficiency.

[0014] Preferably, the step of calculating the average temperature of each region based on the real-time temperature data of each part of the aircraft surface further includes:

[0015] Obtain the real-time temperature \(T_i\) of each monitoring point on the aircraft surface, where \(i\) ranges from 1 to \(n\);

[0016] Calculate the average temperature within each region \(T_{avg}=(T_1 + T_2+\cdots+T_n) / n\);

[0017] Based on the average temperature within the region, determine the anti-icing priority \(P\) of this region;

[0018] If \(P\geq k\), mark this region as a high-priority region, where \(P\) represents the anti-icing priority and \(k\) represents the preset priority threshold.

[0019] Preferably, the step of determining the anti-icing priority \(P\) of this region based on the average temperature within the region further includes:

[0020] Analyze the average temperature \(T_{avg}\) and historical data, and extract the historical average icing temperature \(T_{hist}\) of this region;

[0021] Calculate the anti-icing priority factor \(F = |T_{avg}-T_{hist}|\);

[0022] Based on the anti-icing priority factor \(F\) and the current environmental humidity \(H\), calculate the comprehensive priority \(P = F\times H\);

[0023] If \(P > L\) and \(T_{avg}<T_{threshold}\), increase the heating power \(P_{heat}\) of this region, where \(F\) represents the anti-icing priority factor, \(H\) represents the current environmental humidity, \(P\) represents the comprehensive priority, \(L\) represents the set lower limit of the comprehensive priority, \(T_{threshold}\) represents the set temperature threshold, and \(P_{heat}\) represents the heating power.

[0024] Preferably, the step of calculating the anti-icing priority factor \(F = |T_{avg}-T_{hist}|\) further includes:

[0025] Obtain the historical average icing temperature \(T_{hist}\) within the region;

[0026] Real-time monitor the average temperature \(T_{avg}\) within the current region;

[0027] Calculate the anti-icing priority factor \(F = |T_{avg}-T_{hist}|\);

[0028] If \(F > G\), adjust the heating start-stop frequency \(f_{heat}=F / C\) of this region, where \(G\) represents the set anti-icing priority threshold, \(f_{heat}\) represents the heating start-stop frequency, and \(C\) represents the constant coefficient.

[0029] Preferably, the step of calculating the comprehensive priority P=F×H based on the anti-icing priority factor F and the current ambient humidity H further comprises:

[0030] Real-time monitoring of ambient humidity H;

[0031] Calculate the product of the anti-icing priority factor F and the ambient humidity H: P = F × H;

[0032] Based on the comprehensive priority P, adjust the coverage R_coverage of the heating area;

[0033] If P>S, expand the coverage of the area R_coverage=R_initial+ΔR, where S represents the set comprehensive priority threshold, R_initial represents the initial coverage, and ΔR represents the coverage increment.

[0034] Preferably, the step of analyzing historical icing data to determine the icing risk level of each area further comprises:

[0035] Collect historical icing data D_i of each area on the aircraft surface, where i is 1 to n;

[0036] Count the freezing frequency of each area f_i=D_i / N, where N is the total number of monitoring times;

[0037] Compare the icing frequency f_i of each area with the set risk threshold R;

[0038] If f_i ≥ R, the icing risk level of the area is calibrated as a high risk level G_high, otherwise it is calibrated as a low risk level G_low, where f_i represents the icing frequency of area i, and R represents the set risk threshold.

[0039] Preferably, the step of comparing the icing frequency f_i of each area with the set risk threshold R further comprises:

[0040] Check the icing frequency f_i of each area;

[0041] Calculate the risk factor R_f = (f_i-f_min) / (f_max-f_min), where f_min is the minimum icing frequency and f_max is the maximum icing frequency;

[0042] If R_f ≥ K, the icing risk level of the area is calibrated as a high risk level G_high, otherwise it is calibrated as a low risk level G_low, where R_f represents the risk factor and K represents the set risk factor threshold.

[0043] Preferably, the step of calculating the risk factor R_f=(f_i-f_min) / (f_max-f_min) further comprises:

[0044] Determine the icing frequency f_i of each area;

[0045] Find the minimum freezing frequency f_min and the maximum freezing frequency f_max among all monitoring points;

[0046] Calculate the risk factor R_f = (f_i-f_min) / (f_max-f_min);

[0047] If R_f>J, the area is marked as the focus area A_focus, where J represents the set risk factor threshold.

[0048] Preferably, the step of marking the area as a focus area A_focus based on the risk factor R_f>J further comprises:

[0049] Determine the risk factor R_f for each region;

[0050] If R_f>J, the area is marked as the focus area A_focus that requires additional monitoring;

[0051] Displaying icons and warning information of the key focus area on the system control interface;

[0052] If the number of focus areas exceeds the threshold N_focus, an additional alarm mechanism is triggered, where J represents the set risk factor threshold and N_focus represents the threshold for the number of focus areas.

[0053] The disclosed embodiment provides an automatic activation control method for an intelligent anti-icing and snow removal control system, including: dividing the anti-icing and snow removal heating area based on the real-time temperature and icing data of the aircraft surface; activating the heating device of the corresponding area when the risk of icing in a specific area is detected; dynamically adjusting the power of each heating area according to environmental conditions and flight status; recording and analyzing the working status of each heating area, and optimizing the future zoning strategy to improve the energy efficiency ratio. Through the solution of the disclosed embodiment, it is possible to solve the problem of how to intelligently partition the heating area to solve the balance between energy consumption and anti-icing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the exemplary implementation methods of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the embodiments of the present disclosure and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1It is a flow chart of an automatic activation control method of an intelligent anti-icing and snow removal control system of the present application;

[0056] Figure 2 It is a flowchart for dividing the anti-icing and snow removal heating areas based on the real-time temperature and icing data of the aircraft surface;

[0057] Figure 3 It is a flow chart for calculating the average temperature of each area based on the real-time temperature data of each part of the aircraft surface;

[0058] Figure 4 is a flow chart for determining the anti-icing priority P of an area based on the average temperature in the area;

[0059] Figure 5 is a flow chart for calculating anti-icing priority factors;

[0060] Figure 6 is a flow chart of an automatic activation control method of an intelligent anti-icing and snow removal control system according to another embodiment of the present application;

[0061] Figure 7 It is a flow chart for analyzing historical icing data and determining the icing risk level of each area;

[0062] Figure 8 is a flow chart of an automatic activation control method of an intelligent anti-icing and snow removal control system according to another embodiment of the present application;

[0063] Fig. 9 is a flow chart of an automatic activation control method of an intelligent anti-icing and snow removal control system according to another embodiment of the present application;

[0064] Fig.10 It is a flow chart of an automatic activation control method of an intelligent anti-icing and snow removal control system according to another embodiment of the present application. DETAILED DESCRIPTION

[0065] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0066] Next, referring to the accompanying drawings, an automatic activation control method of an intelligent anti-icing and snow removal control system of the present invention is described.

[0067] like Figure 1 As shown, the method of the present application mainly includes four main steps:

[0068] S101: Based on the real-time temperature and icing data of the aircraft surface, the anti-icing and snow removal heating areas are divided. Specifically, the control system collects data from sensors distributed throughout the aircraft surface, which can monitor the temperature changes and icing conditions of the surface in real time. The system analyzes this data through algorithms and determines which areas may be at risk of icing based on preset rules or models. Each area is divided into independent control units, which can achieve more accurate energy management. For example, in a specific embodiment, assuming that the sensor detects that the temperature of the leading edge of the wing and the trailing edge of the tail is lower than the icing threshold, and these areas have shown slight signs of icing, the system marks these two areas as high-risk areas and further subdivides them into several small control units.

[0069] S102: When a risk of icing is detected in a specific area, the heating device of the corresponding area is activated. Once an area is identified as having an icing risk, the control system will immediately trigger the heating device in that area. This process is achieved through a closed-loop control system, that is, the sensor continuously monitors the effect of heating to ensure that the temperature in the area reaches the minimum level required for anti-icing. If the temperature continues to drop or the icing phenomenon is not alleviated, the system will increase power or start more heating devices. In one embodiment, when a small control unit at the leading edge of the wing detects that the temperature drops below -5°C and there are signs of icing, the system will automatically activate the heating wire of the unit and keep heating until the temperature returns to above 0°C and the icing disappears.

[0070] S103: Dynamically adjust the power of each heating area according to environmental conditions and flight status. The control system will not only consider the real-time data of the sensor, but also comprehensively consider the environmental conditions and the flight status of the aircraft to adjust the power of the heating device. For example, when flying at high altitudes, the air is drier and a higher heating power may be required to prevent icing; when flying close to the ground and the humidity is high, the power needs to be adjusted to prevent excessive heating and waste of energy. In a specific embodiment, assuming that the aircraft is undergoing a transition from a high-altitude, low-humidity environment to a high-humidity environment on the ground, the system will dynamically calculate the optimal heating power distribution through a built-in algorithm. For example, at high altitudes, the system may increase the power of all heating units to the maximum value to prevent rapid icing; when approaching the ground, the power is gradually reduced to reduce energy consumption while maintaining an effective anti-icing effect.

[0071] S104: Record and analyze the working status of each heating area, and optimize future partitioning strategies to improve energy efficiency. In order to continuously improve system performance, the control system will record the working data of each heating area during each operation, including activation time, power setting, temperature change and ice elimination. Through big data analysis and machine learning algorithms, the system can identify which partitioning schemes and power settings are most effective, and then optimize future anti-icing strategies. In one embodiment, suppose that the system records the temperature changes and ice formation of a control unit during multiple flights. The analysis results show that under specific environmental conditions, low-power operation of a partition can effectively prevent ice formation with low energy consumption. The system will use these empirical data for future partition optimization, such as adjusting the size, location or power level of the partition to achieve better energy efficiency.

[0072] In addition, the system has adopted several key technologies on how to intelligently partition the heating area to solve the balance between energy consumption and anti-icing efficiency. First, when dynamically dividing the heating area, the system takes into account the sensitivity and importance of different parts. For example, the leading edge of the wing is critical to flight safety, so more small but low-power control units are set up in this area to achieve refined control. Secondly, through machine learning algorithms, the system can predict the icing risks of various areas under different flight phases and environmental conditions, and respond in advance. In this way, unnecessary energy waste can be avoided while ensuring the anti-icing effect. In short, through the above methods and technologies, the system can automatically activate and effectively manage the anti-icing and snow removal heating areas in complex and changing environments to achieve the best energy efficiency balance.

[0073] Next, refer to Figure 2 , describing the steps of dividing the anti-icing and snow removal heating area based on the real-time temperature and icing condition data of the aircraft surface of the present invention:

[0074] S201: Calculate the average temperature of each area based on the real-time temperature data of each part of the aircraft surface;

[0075] S202: Analyze historical icing data to determine the icing risk level of each area;

[0076] S203: Divide the aircraft surface into multiple anti-icing and snow removal heating areas based on average temperature and icing risk level;

[0077] S204: Adjust the heating start threshold of each area to optimize the balance between energy consumption and anti-icing efficiency.

[0078] First, based on the real-time temperature data of each part of the aircraft surface, the average temperature of each area is calculated. This process involves collecting temperature data from different parts of the aircraft surface through a sensor network, for example, temperature sensors are arranged in key parts such as the fuselage, wings and tail. When calculating the average temperature, the following formula is used:

[0079]

[0080] Where Tavg is the average temperature of a region, Ti is the temperature measured by the ith sensor, and n is the number of sensors in the region. The purpose of this formula is to provide an overall temperature reference value for evaluating the overall temperature condition of the region. For example, in one embodiment, if there are 10 sensors in the wing region and the temperatures measured by them are -5°C, -7°C, -6°C, -8°C, -5°C, -6°C, -4°C, -7°C, -6°C and -5°C, respectively, then the average temperature of the region is:

[0081] Tavg=(-5-7-6-8-5-6-4-7-6-5) / 10=-6.1℃

[0082] Secondly, analyze historical icing data to determine the icing risk level of each area. By collecting and analyzing historical flight data, the icing risk of different areas under specific meteorological conditions is assessed. The risk level is usually divided into three levels: low, medium, and high. For example, through data analysis, it is found that the front end of the wing is more prone to icing under low temperature and high humidity conditions, so it is rated as a high-risk area.

[0083] Based on the average temperature and icing risk level, the aircraft surface is divided into multiple anti-icing and snow removal heating areas. This step involves taking into account the average temperature and icing risk level of each area and formulating a heating strategy. For example, if the average temperature of a wing area is -6.1℃ and the icing risk level is high, this area will be designated as an area that requires higher priority heating. Another tail area has an average temperature of -3℃ and a medium icing risk level, which will be designated as a lower priority heating area.

[0084] Finally, adjust the heating start threshold of each area to optimize the balance between energy consumption and anti-icing efficiency. Set different start thresholds based on actual operating experience and experimental data. For example, for high-risk areas, the heating start threshold is set to -4°C, and heating is started when the temperature is lower than this value; for medium-risk areas, the start threshold can be set to -5°C. The specific start threshold setting is shown in the following formula:

[0085] Tthreshold=Tmin+k×Trisk, where Tthreshold is the heating start threshold, Tmin is the minimum safe temperature, usually around -10℃, Trisk is the correction factor corresponding to the icing risk level, ranging from 0 to 1, with a value close to 1 in high-risk areas and a value close to 0 in low-risk areas, and k is an empirical adjustment factor, generally around 0.5. This setting is to reduce unnecessary energy consumption while ensuring safety. For example, assuming the minimum safe temperature is -10℃, for high-risk areas Trisk=0.9, then the heating start threshold is:

[0086] Tthreshold=-10+0.5×0.9=-9.55

[0087] In summary, through these four steps, the intelligent anti-icing and snow removal control system can effectively divide and manage the heating area to ensure flight safety and energy efficiency.

[0088] Next, refer to Figure 3 , describing the steps of calculating the average temperature of each area based on the real-time temperature data of each part of the aircraft surface of the present invention.

[0089] S301: Get the real-time temperature T_i of each monitoring point on the aircraft surface, where i is 1 to n. This step refers to installing temperature sensors at different locations on the surface of the aircraft to collect real-time temperature data at each monitoring point. These monitoring points are distributed in various important areas of the fuselage, such as wings, tail fins, and engine air intakes. In this way, detailed temperature information of various parts of the aircraft surface can be obtained, thereby providing basic data for subsequent calculations. For example, assuming that there are 5 monitoring points in a certain area, the real-time temperatures of these monitoring points are T_1 = -10°C, T_2 = -8°C, T_3 = -12°C, T_4 = -9°C, and T_5 = -7°C.

[0090] S302: Calculate the average temperature in each area T_avg = (T_1+T_2+...+T_n) / n. Here, T_avg refers to the average temperature in the area, T_1, T_2,...T_n respectively represent the temperature of each monitoring point in the area, and n represents the number of monitoring points in the area. The significance of this formula is that by summing the temperatures of all monitoring points in the area and dividing by the number of monitoring points, the average temperature of the area is obtained, which can more accurately reflect the overall temperature conditions of the area. Continuing with the above example, the average temperature of the 5 monitoring points in the area is T_avg = {-10+(-8)+(-12)+(-9)+(-7)} / 5 = -9.2℃.

[0091] S303: Determine the anti-icing priority P of the area based on the average temperature in the area. In this step, the system determines the anti-icing demand priority of the area based on the calculated average temperature. The priority is usually determined based on a preset rule, for example, the lower the temperature, the higher the priority. Specifically, a priority function P(T_avg) can be set, where the lower T_avg, the larger the value of P. For example, if P = -0.5×T_avg+5 is set, the average temperature T_avg = -9.2°C in the above example corresponds to a priority of P = -0.5×(-9.2)+5 = 9.6.

[0092] S304: If P≥k, mark the area as a high priority area, where P represents the anti-icing priority and k represents the preset priority threshold. Here k is a numerical value preset by the system, which is used to distinguish between high priority and normal priority areas. For example, assuming that the system preset high priority threshold k=9, then according to the above calculated priority P=9.6 is greater than k, so the area will be marked as a high priority area, and the corresponding anti-icing measures will be triggered. This setting is to ensure that anti-icing measures can be activated first in areas with extremely low temperatures, thereby improving flight safety.

[0093] Next, refer to Figure 4 , describing the steps of the present invention for determining the anti-icing priority P of a region based on the average temperature in the region.

[0094] S401: Analyze the temperature average value T_avg and historical data to extract the historical average freezing temperature T_hist of the area. The purpose of this step is to obtain a reference temperature standard by analyzing the current temperature average value in the area and the historical freezing conditions in the area, so as to more accurately evaluate the anti-icing needs of the current area. The historical average freezing temperature T_hist is usually calculated by collecting meteorological data for many years, and the range is generally between -5℃ and 0℃. This step helps to distinguish the different needs of different geographical areas in anti-icing treatment.

[0095] S402: Calculate the anti-icing priority factor F = |T_avg-T_hist|. In this step, the absolute value of the difference between the current temperature average value T_avg and the historical average freezing temperature T_hist is used as the anti-icing priority factor F. The range of F value is 0 to infinity, but it is usually limited to 0 to 10 in practical applications. The setting of this formula is intended to quantify the difference between the current temperature condition and the historical freezing temperature. The larger the difference, the higher the icing risk in the current area, so the priority of anti-icing is also increased accordingly.

[0096] S403: Based on the anti-icing priority factor F and the current ambient humidity H, calculate the comprehensive priority P = F × H. This step combines the temperature difference and the current ambient humidity because frost is more likely to form in a high-humidity environment, further increasing the urgency of anti-icing. The ambient humidity H usually varies between 0 and 100%. The range of the P value is also from 0 to infinity, but in practical applications, it is usually set between 0 and 100. The significance of this formula is to obtain a more comprehensive and accurate anti-icing priority index by comprehensively considering the anti-icing priority factor F and the current ambient humidity H.

[0097] S404: If P > L and T_avg < T_threshold, increase the heating power P_heat of this area. Here, L represents the set lower limit of the comprehensive priority, usually set to about 20; T_threshold represents the set temperature threshold, usually set to about -2°C. The range of the heating power P_heat can be adjusted according to the specific equipment and environmental conditions. If the current comprehensive priority P exceeds the set lower limit L and the current average temperature is lower than the set temperature threshold T_threshold, it indicates that the icing risk in the current area is very high, and the system should immediately take measures to increase the heating power to prevent icing.

[0098] For example, in an embodiment, assume that an aircraft is flying at high altitude in winter, the average temperature T_avg of a specific area on the aircraft is -3°C, and the historical average icing temperature T_hist of this area is -1°C. First, calculate the anti-icing priority factor F = |-3 - (-1)| = 2. The current ambient humidity H is 80%, then the comprehensive priority P = F × H = 2 × 80% = 1.6. If the set lower limit L of the comprehensive priority is 2, and the set temperature threshold T_threshold is -2°C, then P = 1.6 < L = 2 and T_avg = -3 < T_threshold = -2, which does not meet the heating condition, so the heating power is not adjusted temporarily.

[0099] Specifically, assume that in another scenario, the average temperature T_avg of the same area of the same aircraft is -4°C, the historical average icing temperature T_hist is still -1°C, and the current ambient humidity H is 90%. Calculate the anti-icing priority factor F = |-4 - (-1)| = 3, and the comprehensive priority P = F × H = 3 × 90% = 2.7. Since P = 2.7 > L = 2 and T_avg = -4 < T_threshold = -2, which meets the heating condition, the system will automatically increase the heating power P_heat of this area to prevent possible icing problems and ensure flight safety.

[0100] Through these detailed steps and examples, it is demonstrated how to effectively determine the anti-icing priority based on the temperature average in the area, thereby optimizing the aircraft's intelligent anti-icing and snow removal control system.

[0101] Next, refer to Figure 5 , describing the steps of calculating the anti-icing priority factor F=|T_avg-T_hist| of the present invention.

[0102] S501: Obtain the historical average freezing temperature T_hist in the area. The purpose of this step is to determine the historical average freezing temperature of the area as a reference point for anti-icing control. Historical data usually comes from long-term observation data recorded by weather stations or the aircraft itself to ensure the reliability and accuracy of the data. Specifically, in one embodiment, assuming that the historical freezing temperature record of a certain aircraft model on a specific flight path shows that the historical average freezing temperature T_hist of the area is -5°C.

[0103] S502: Real-time monitoring of the average temperature T_avg in the current area. This process is achieved through multiple temperature sensors installed on the surface of the aircraft to ensure the comprehensiveness and real-time nature of the data. The average temperature T_avg refers to the average value of the data collected by all sensors over a period of time, which is used to reflect the true conditions of the current environment. For example, during the flight, five temperature sensors are installed on the lower part of the wing, and the temperatures collected at a certain moment are -3°C, -4°C, -4°C, -2°C and -3°C, respectively. Then T_avg = (-3-4-4-2-3) / 5 = -3.2°C.

[0104] S503: Calculate the anti-icing priority factor F = |T_avg-T_hist|. This formula is used to evaluate the difference between the current temperature and the historical average freezing temperature. The larger the difference, the easier it is for ice to form in the current environment, and the more aggressive the anti-icing measures should be. In this formula, T_avg is the average temperature in the current area, and T_hist is the historical average freezing temperature. The absolute value is taken to ensure that F is always non-negative. Assuming that T_avg = -3.2°C and T_hist = -5°C in the first two steps, then F = |-3.2-(-5)| = 1.8°C.

[0105] S504: If F>G, adjust the heating start and stop frequency f_heat=F / C in this area. Here G represents the set anti-icing priority threshold, which is a preset value used to determine whether anti-icing measures need to be strengthened. When F exceeds G, more frequent heating is triggered to avoid the risk of icing. f_heat represents the heating start and stop frequency, which determines the operating frequency of the heater. C is a constant coefficient used to adjust the sensitivity of the heating frequency. For example, set G=1.5℃ and C=0.5. If the calculated F=1.8℃, F>G is satisfied, and the heating frequency f_heat=1.8 / 0.5=3.6Hz needs to be adjusted. This means that the heater starts 3.6 times per second to ensure adequate anti-icing. The purpose of setting G and C is to balance the anti-icing effect and energy consumption to make the system safe and efficient.

[0106] Next, refer to Figure 6 , describing the steps of calculating the comprehensive priority P=F×H based on the anti-icing priority factor F and the current ambient humidity H of the present invention.

[0107] S601: Real-time monitoring of ambient humidity H;

[0108] S602: Calculate the product of the anti-icing priority factor F and the ambient humidity H, P=F×H;

[0109] S603: Based on the comprehensive priority P, adjust the coverage R_coverage of the heating area;

[0110] S604: If P>S, expand the coverage of the area R_coverage=R_initial+ΔR, where S represents the set comprehensive priority threshold, R_initial represents the initial coverage, and ΔR represents the coverage increment.

[0111] Real-time monitoring of ambient humidity H refers to the continuous acquisition and recording of the humidity value of the external environment in the aircraft's anti-icing control system. Ambient humidity H is usually expressed as a percentage, ranging from 0% to 100%. In typical application scenarios, the optimal measurement range of ambient humidity is usually 20% to 90%. This is because when the ambient humidity is within this range, the possibility of icing is the highest and the demand for the anti-icing system is the greatest.

[0112] After the current environmental humidity H is monitored in real time, the next step is to calculate the product P = F×H of the anti-icing priority factor F and the environmental humidity H. The anti-icing priority factor F is a value comprehensively evaluated based on the current state of the aircraft and the flight environment, and its range is generally from 0 to 1. When the F value is close to 1, it indicates a high icing risk for the aircraft in the current environment, and the priority of anti-icing measures should be increased. The formula P = F×H is designed to more accurately reflect the urgency of anti-icing requirements. By multiplying F and H, the actual moisture content in the current environment can be added while considering the possibility of icing, enabling the system to make a more accurate judgment and response.

[0113] Next, the coverage range R_coverage of the heating area is adjusted based on the comprehensive priority P. This means dynamically adjusting which parts of the aircraft surface should be heated and the size of the heating area according to the calculated level of the comprehensive priority P. Specifically, if P > S, where S is a preset comprehensive priority threshold, usually set to around 0.7, then the heating area will be expanded, R_coverage = R_initial + ΔR. Here, R_initial represents the initial coverage range under standard or minimum conditions, and ΔR is the increased range dynamically determined based on the difference between P and S, aiming to ensure sufficient energy allocation for anti-icing treatment in high-risk situations.

[0114] In one embodiment, assume that the current anti-icing priority factor F is 0.85 and the environmentally humidity H monitored in real time is 60%. Then the comprehensive priority P is calculated as 0.85×60% = 0.51. If the preset threshold S is 0.65, then P < S, so there is no need to adjust the heating area. However, assume that the environmental humidity suddenly rises to 80%. The new P = 0.85×80% = 0.68, then P > S, and at this time the system will expand the heating area and increase the heating intensity to prevent icing.

[0115] Through the above steps, the present invention provides a method capable of automatically adjusting the anti-icing and snow-removing strategy, which not only ensures the safety of the aircraft but also effectively utilizes energy and improves flight efficiency.

[0116] Next, refer to Figure 7 , and describe the steps of analyzing historical icing data of the present invention to determine the icing risk level of each area.

[0117] S701: Collect historical icing data D_i for each area on the aircraft surface, where i ranges from 1 to n. Here, D_i represents the number of times icing occurred in area i during historical monitoring. i ranges from 1 to n, and n represents the number of different areas monitored on the aircraft surface. These data can be obtained from historical flight records, meteorological data, or on-board sensors. The purpose of collecting historical icing data is to understand the icing occurrence in each area.

[0118] S702: Count the freezing frequency of each area f_i=D_i / N, where N is the total number of monitoring times. Here, f_i represents the freezing frequency of area i in all monitoring times, and N is the total number of monitoring times. By calculating the freezing frequency of each area, the probability of freezing in each area can be quantified. For example, in one embodiment, if a certain area freezes 150 times in a total of 1000 monitoring times, the freezing frequency of the area f_i=150 / 1000=0.15.

[0119] S703: Compare the icing frequency f_i of each area with the set risk threshold R. The risk threshold R here is a pre-set critical value, which is used to determine whether the icing frequency of a certain area is within the dangerous range. The value range of R is usually between 0 and 1, and the optimal value can be determined based on actual flight conditions and safety standards. The purpose of setting a risk threshold is to convert different icing frequencies into risk levels to facilitate the formulation of subsequent anti-icing measures. For example, R can be set to 0.1, which means that if the icing frequency of a certain area exceeds 10%, it is considered a high-risk area.

[0120] S704: If f_i≥R, the icing risk level of the area is calibrated as a high risk level G_high, otherwise it is calibrated as a low risk level G_low. Here, f_i represents the icing frequency of area i, and R represents the set risk threshold. The purpose of this step is to clarify which areas are more prone to icing during flight by comparing the icing frequency of each area with the risk threshold, so as to take corresponding preventive measures. For example, if the icing frequency of a certain area f_i=0.15 exceeds the preset risk threshold R=0.1, the area will be calibrated as a high risk level G_high, and the intelligent anti-icing and snow removal control system will focus on monitoring and processing this area to ensure flight safety.

[0121] Next, refer to Figure 8 , describes the step of comparing the icing frequency f_i of each area with the set risk threshold R of the present invention. This step mainly includes the following parts:

[0122] S801: Check the icing frequency f_i of each area. This step is to obtain the actual icing frequency in different areas. The icing frequency refers to the number of times the area is frozen in a certain period of time. These frequencies can be obtained through the analysis of sensors and historical data.

[0123] S802: Calculate the risk factor R_f = (f_i-f_min) / (f_max-f_min). The key to this step is to calculate the risk factor R_f of each area based on the known maximum icing frequency f_max and minimum icing frequency f_min. Here, f_i is the icing frequency of a specific area, f_min is the minimum icing frequency in all detection areas, and f_max is the maximum icing frequency in all detection areas. The purpose of this formula is to standardize the icing frequencies of different areas for subsequent comparison. The value range of R_f is from 0 to 1, which indicates the proportion of the icing conditions in the current area relative to the extreme conditions (f_min and f_max) in the entire detection range. Through this standardization, the icing risks between different areas can be compared more intuitively.

[0124] S803: If R_f≥K, the icing risk level of the area is calibrated as a high risk level G_high, otherwise it is calibrated as a low risk level G_low. This step is based on the risk factor R_f to determine the icing risk level. The closer R_f is to 1, the closer the icing frequency in the area is to the maximum value, and therefore the higher the risk. K is the preset risk factor threshold, which is used to determine whether an area is at a high risk level. Usually, the value range of K is between 0.7 and 0.9, and the specific optimal value can be adjusted according to actual conditions. In this way, the system can more accurately identify high-risk areas and take corresponding anti-icing measures.

[0125] For example, in a specific embodiment, suppose that in a certain flight mission, multiple sensors are distributed in key parts such as wings, fuselage and engine air intake to monitor the icing frequency of these areas. Through data analysis, it is found that the icing frequency of the wing area f_1 = 0.3 times / hour, the icing frequency of the fuselage area f_2 = 0.1 times / hour, and the icing frequency of the engine air intake f_3 = 0.5 times / hour. At the same time, the minimum icing frequency f_min = 0.1 times / hour, and the maximum icing frequency f_max = 0.5 times / hour. According to the above formula, the risk factor R_f of each area is calculated: wing R_f1 = (0.3-0.1) / (0.5-0.1) = 0.5, fuselage R_f2 = (0.1-0.1) / (0.5-0.1) = 0, engine air intake R_f3 = (0.5-0.1) / (0.5-0.1) = 1. Assuming that the risk factor threshold K is set to 0.8, according to the judgment rule, the engine air inlet area is marked as a high risk level G_high due to R_f3 ≥ 0.8, while the wing and fuselage area is marked as a low risk level G_low. Based on this result, the system can take anti-icing and snow removal measures for the engine air inlet first to ensure flight safety.

[0126] Through the above steps, the present invention can effectively quantify the icing frequency of each area, and judge the risk level in combination with a preset risk threshold, thereby realizing automatic control of the aircraft intelligent anti-icing and snow removal system.

[0127] Next, refer to Fig. 9 , describing the steps of calculating the risk factor R_f=(f_i-f_min) / (f_max-f_min) of the present invention.

[0128] S901: Determine the icing frequency f_i of each area. In the intelligent anti-icing and snow removal control system, the icing conditions of different areas are monitored through a sensor network, and the number of icing events per unit time in each area is collected. The value of f_i represents the icing frequency in the i-th area, which is usually expressed as the number of icing events per hour or per flight.

[0129] S902: Find the minimum icing frequency f_min and the maximum icing frequency f_max of all monitoring points. In this step, the icing frequencies monitored in all areas are compared to select the minimum value f_min and the maximum value f_max. These values ​​are used for subsequent standardization to ensure that the risk factors of each area are comparable.

[0130] S903: Calculate the risk factor R_f = (f_i-f_min) / (f_max-f_min). This step is to standardize the icing frequency f_i of each area and calculate its relative position among all monitoring points. The value of R_f is between 0 and 1. When f_i of a certain area is close to f_max, R_f is close to 1, indicating that the icing risk of the area is high; when f_i is close to f_min, R_f is close to 0, indicating that the icing risk of the area is low. The design of this formula makes the assessment of icing risk more intuitive and accurate, which is conducive to subsequent control decisions.

[0131] S904: If R_f>J, mark the area as a focus area A_focus, where J represents the set risk factor threshold. Here, J is a critical value set according to system requirements and safety standards, generally between 0 and 1. Specifically, the optimal value of J needs to be determined through experiments and historical data statistics to ensure that high-risk areas can be identified in a timely manner without frequent false alarms. For example, when J is set to 0.7, any area with an R_f value greater than 0.7 will be marked as A_focus so that more stringent anti-icing measures can be taken.

[0132] In one embodiment, assume that there are three main monitoring areas on an aircraft, denoted as A, B, and C. Monitoring data show that the icing frequencies of these three areas are: f_A = 3 times / hour, f_B = 1 time / hour, and f_C = 5 times / hour. Therefore, f_min = 1, f_max = 5. The risk factor of each area is calculated according to the above formula, R_A = (3-1) / (5-1) = 0.5, R_B = (1-1) / (5-1) = 0, and R_C = (5-1) / (5-1) = 1. Assume that J = 0.7, then R_C>0.7, area C will be marked as A_focus, and the system will increase the intensity and frequency of anti-icing and snow removal measures for this area.

[0133] Next, refer to Fig.10 , describing the steps of marking the area as a focus area A_focus based on the risk factor R_f>J of the present invention.

[0134] S1001: Determine the risk factor R_f of each area. The risk factor R_f can be comprehensively evaluated by calculating the temperature, humidity, wind speed and other meteorological data detected by sensors in each area and the thickness of ice on the aircraft surface. The parameters include temperature T (range -40℃ to 20℃), humidity H (range 0% to 100%), wind speed V (range 0m / s to 30m / s) and ice thickness D (range 0mm to 5mm). R_f = aT + bH + cV + dD, where a, b, c, d are the weight coefficients of these parameters, and the values ​​of a, b, c, d are usually in the range of [0, 1], and the optimal values ​​are 0.4, 0.3, 0.2, and 0.1, respectively. These weights are set to make the model pay more attention to the impact of temperature and humidity. The formula is intended to obtain a quantitative value through weighted calculation to assess whether the area has a high risk of freezing or snow accumulation.

[0135] S1002: If R_f>J, mark the area as a focus area A_focus that requires additional monitoring. Here, J is a preset risk factor threshold, for example, J=70. This value is set to filter out high-risk areas to ensure that these areas can receive timely attention and treatment to reduce security risks. If the J value is too high, many low-risk areas will be misjudged as high-risk areas, increasing unnecessary monitoring burden; if the J value is too low, high-risk areas may be ignored, affecting security.

[0136] S1003: Display the icon and warning information of the focus area on the system control interface. For example, the icon of the corresponding area on the control interface will turn red, accompanied by a flashing prompt, and a warning message will pop up: A_focus area risk factor R_f = X, please strengthen monitoring. This can intuitively remind the operator to pay attention and facilitate quick response and decision-making.

[0137] S1004: If the number of focus areas exceeds the threshold value N_focus, an additional alarm mechanism is triggered. N_focus is a configurable parameter in the system, for example, N_focus=5. When the number of focus areas exceeds this threshold, it means that there are multiple areas at high risk at the same time, and more resources may need to be called to deal with potential risks, such as starting more heating devices or sending more personnel for inspection. The purpose of triggering the alarm mechanism is to ensure the effective allocation and utilization of resources, avoid missing any important information, and thus improve the overall security of the system.

[0138] In a specific embodiment, assume that the aircraft apron of an airport is divided into 10 monitoring areas. The meteorological sensor detection data of each area shows that R_f=75 in area 3, R_f=73 in area 5, and R_f values ​​of other areas are all lower than 70. Therefore, according to the above rules, areas 3 and 5 are marked as key focus areas A_focus. The system interface will show that the icons of these areas turn red and are accompanied by a warning message: Focus on areas 3 and 5, please strengthen monitoring. If N_focus is set to 3, and the risk factor of the third area exceeds 70 later, an additional alarm mechanism will be triggered to activate more heating equipment and monitoring resources to ensure that de-icing and snow removal work in these areas is effectively carried out.

[0139] The automatic activation control method of an intelligent anti-icing and snow removal control system of the present invention comprises: dividing the anti-icing and snow removal heating areas based on the real-time temperature and icing condition data of the aircraft surface; activating the heating device of the corresponding area when the risk of icing in a specific area is detected; dynamically adjusting the power of each heating area according to environmental conditions and flight status; recording and analyzing the working status of each heating area, and optimizing the future zoning strategy to improve the energy efficiency ratio.

[0140] By implementing the above steps, the present invention effectively solves the problem of how to intelligently partition the heating area and achieves a balance between energy consumption and anti-icing efficiency. Traditional anti-icing and snow removal methods often use global heating or preset partitioning methods, which easily lead to energy waste and are difficult to adapt to complex environmental changes. The present invention uses real-time data monitoring, intelligent partitioning and dynamic power adjustment to enable the heating of each area to more accurately match actual needs, thereby maximizing energy efficiency while ensuring flight safety.

[0141] The methods, programs, systems, devices, etc. of the embodiments of the present invention may be executed or implemented in a single or multiple networked computers, or may be practiced in a distributed computing environment. In the embodiments of this specification, in these distributed computing environments, tasks may be performed by remote processing devices connected via a communication network.

[0142] Those skilled in the art should understand that the embodiments of the present specification may be provided as methods, systems or computer program products. Therefore, those skilled in the art may imagine that the functional modules / units or controllers and related method steps described in the above embodiments may be implemented in software, hardware or a combination of software / hardware.

[0143] Unless explicitly stated, the actions or steps of the methods, programs, and embodiments of the present invention do not have to be performed in a specific order and can still achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0144] In this article, multiple embodiments of the present invention are described, but for the sake of brevity, the description of each embodiment is not exhaustive, and the same or similar features or parts between the embodiments may be omitted. In this article, "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" are meant to be applicable to at least one embodiment or example according to the present invention, but not all embodiments. The above terms do not necessarily mean to refer to the same embodiment or example. In the absence of contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0145] The exemplary systems and methods of the present invention have been specifically shown and described with reference to the above embodiments, which are merely examples of the best modes for implementing the present systems and methods. It will be appreciated by those skilled in the art that various changes may be made to the embodiments of the systems and methods described herein when implementing the present systems and / or methods without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. An automatic activation control method for an intelligent anti-icing and snow removal control system, characterized in that: Comprising: Dividing the anti-icing and snow-removing heating areas based on the real-time temperature and icing condition data of the aircraft surface; Activating the heating devices in the corresponding areas when icing risks are detected in specific areas; Dynamically adjusting the power of each heating area according to environmental conditions and flight states; Recording and analyzing the working states of each heating area, and optimizing the future zoning strategy to improve the energy efficiency ratio.

2. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 1 is characterized in that: The step of dividing the anti-icing and snow-removing heating areas based on the real-time temperature and icing condition data of the aircraft surface further includes: Calculating the average temperature of each area based on the real-time temperature data of each part of the aircraft surface; Analyzing historical icing data to determine the icing risk level of each area; Dividing the aircraft surface into multiple anti-icing and snow-removing heating areas based on the average temperature and icing risk level; Adjusting the heating start threshold of each area to optimize the balance between energy consumption and anti-icing efficiency.

3. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 2 is characterized in that: The step of calculating the average temperature of each area based on the real-time temperature data of each part of the aircraft surface further includes: Obtaining the real-time temperature \(T_i\) of each monitoring point on the aircraft surface, where \(i\) ranges from 1 to \(n\); Calculating the average temperature \(T_{avg}=(T_1 + T_2+\cdots+T_n) / n\) within each area; Determining the anti-icing priority \(P\) of the area based on the average temperature within the area; If \(P\geq k\), marking the area as a high-priority area, where \(P\) represents the anti-icing priority and \(k\) represents the preset priority threshold.

4. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 3 is characterized in that: The step of determining the anti-icing priority \(P\) of the area based on the average temperature within the area further includes: Analyzing the average temperature \(T_{avg}\) and historical data, and extracting the historical average icing temperature \(T_{hist}\) of the area; Calculating the anti-icing priority factor \(F = |T_{avg}-T_{hist}|\); Calculating the comprehensive priority \(P = F\times H\) based on the anti-icing priority factor \(F\) and the current environmental humidity \(H\); If \(P > L\) and \(T_{avg}<T_{threshold}\), increasing the heating power \(P_{heat}\) of the area, where \(F\) represents the anti-icing priority factor, \(H\) represents the current environmental humidity, \(P\) represents the comprehensive priority, \(L\) represents the set lower limit of the comprehensive priority, \(T_{threshold}\) represents the set temperature threshold, and \(P_{heat}\) represents the heating power.

5. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 4 is characterized in that: The step of calculating the anti-icing priority factor \(F = |T_{avg}-T_{hist}|\) further includes: Obtaining the historical average icing temperature \(T_{hist}\) within the area; Real-time monitoring of the average temperature \(T_{avg}\) within the current area; Calculating the anti-icing priority factor \(F = |T_{avg}-T_{hist}|\); If \(F > G\), adjusting the heating start / stop frequency \(f_{heat}=F / C\), where \(G\) represents the set anti-icing priority threshold, \(f_{heat}\) represents the heating start / stop frequency, and \(C\) represents the constant coefficient.

6. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 4, characterized in that: The step of calculating the comprehensive priority \(P = F\times H\) based on the anti-icing priority factor \(F\) and the current environmental humidity \(H\) further includes: Real-time monitoring of the environmental humidity \(H\); Calculating the product \(P = F\times H\) of the anti-icing priority factor \(F\) and the environmental humidity \(H\); Adjusting the coverage range \(R_{coverage}\) of the heating area based on the comprehensive priority \(P\); If P>S, expand the coverage of the area R_coverage=R_initial+ΔR, where S represents the set comprehensive priority threshold, R_initial represents the initial coverage, and ΔR represents the coverage increment.

7. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 2 is characterized in that: The step of analyzing historical icing data to determine the icing risk level of each area further includes: Collect historical icing data D_i of each area on the aircraft surface, where i is 1 to n; Count the freezing frequency of each area f_i=D_i / N, where N is the total number of monitoring times; Compare the icing frequency f_i of each area with the set risk threshold R; If f_i ≥ R, the icing risk level of the area is calibrated as a high risk level G_high, otherwise it is calibrated as a low risk level G_low, where f_i represents the icing frequency of area i, and R represents the set risk threshold.

8. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 7, characterized in that: The step of comparing the icing frequency f_i of each area with the set risk threshold R further includes: Check the icing frequency f_i of each area; Calculate the risk factor R_f = (f_i-f_min) / (f_max-f_min), where f_min is the minimum icing frequency and f_max is the maximum icing frequency; If R_f ≥ K, the icing risk level of the area is calibrated as a high risk level G_high, otherwise it is calibrated as a low risk level G_low, where R_f represents the risk factor and K represents the set risk factor threshold.

9. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 8, characterized in that: The step of calculating the risk factor R_f=(f_i-f_min) / (f_max-f_min) further includes: Determine the icing frequency f_i of each area; Find the minimum freezing frequency f_min and the maximum freezing frequency f_max among all monitoring points; Calculate the risk factor R_f = (f_i-f_min) / (f_max-f_min); If R_f>J, the area is marked as the focus area A_focus, where J represents the set risk factor threshold.

10. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 9, characterized in that: The step of marking the area as a focus area A_focus based on the risk factor R_f>J further includes: Determine the risk factor R_f for each region; If R_f>J, the area is marked as the focus area A_focus that requires additional monitoring; Displaying icons and warning information of the key focus area on the system control interface; If the number of focus areas exceeds the threshold N_focus, an additional alarm mechanism is triggered, where J represents the set risk factor threshold and N_focus represents the threshold for the number of focus areas.

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