An automatic activation control method of an intelligent anti-icing and snow-removal control system

Abstract

The embodiment of the disclosure provides an automatic activation control method of an intelligent anti-icing and snow-removing control system, comprising: dividing an anti-icing and snow-removing heating area based on real-time temperature and icing condition data of an aircraft surface; activating a heating device of a corresponding area when detecting that a specific area has an icing risk; dynamically adjusting the power of each heating area according to environmental conditions and flight states; recording and analyzing the working state of each heating area, and optimizing future partition strategies to improve the energy efficiency ratio. Through the scheme of the embodiment of the disclosure, the problem of how to intelligently partition the heating area to balance 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] Anti-icing and snow removal control systems utilize integrated sensors to monitor aircraft surface temperature, humidity, and ice and snow accumulation in real time. Based on this data, they automatically activate the heating system to effectively prevent frozen snow from impacting aircraft performance and safety. However, this approach also faces a key technical challenge: intelligently zoning the heating areas to achieve a balance between energy consumption and anti-icing efficiency. While ensuring that each critical area receives adequate heating for effective anti-icing, it is also necessary to avoid energy waste caused by excessive heating, placing higher demands on 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 application discloses 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 areas 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 area based on the real-time temperature data of each part of the aircraft surface further includes:

[0015] Get the real-time temperature T_i of each monitoring point on the aircraft surface, where i is 1 to n;

[0016] Calculate the average temperature in each area T_avg = (T_1 + T_2 + ... + T_n) / n;

[0017] Determine the anti-icing priority P of the area based on the average temperature in the area;

[0018] If P ≥ k, the area is marked as a high priority area, 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 the area based on the average temperature in the area further includes:

[0020] Analyze the average temperature T_avg and historical data to extract the historical average freezing temperature T_hist of the area;

[0021] Calculate the anti-icing priority factor F = |T_avg-T_hist|;

[0022] Based on the anti-icing priority factor F and the current ambient humidity H, calculate the comprehensive priority P = F × H;

[0023] If P>L and T_avg <T_threshold,增加该区域的加热功率P_heat,其中F表示防冰优先级因子,H表示当前环境湿度,P表示综合优先级,L表示设定的综合优先级下限,T_threshold表示设定的温度阈值,P_heat表示加热功率。

[0024] Preferably, the step of calculating the anti-icing priority factor F=|T_avg-T_hist| further comprises:

[0025] Get the historical average freezing temperature T_hist in the area;

[0026] Real-time monitoring of the average temperature T_avg in the current area;

[0027] Calculate the anti-icing priority factor F = |T_avg-T_hist|;

[0028] If F>G, adjust the heating start and stop frequency of the area f_heat=F / C, where G represents the set anti-icing priority threshold, f_heat represents the heating start and stop frequency, and C represents the constant coefficient.

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

[0030] monitoring the environmental humidity H in real time;

[0031] calculating the product P=F x H of the anti-icing priority factor F and the environmental humidity H;

[0032] adjusting the coverage R_coverage of the heating area based on the comprehensive priority P;

[0033] if P>S, expanding the coverage R_coverage of the area, wherein S represents a set comprehensive priority threshold, R_initial represents an initial coverage, and ΔR represents a coverage increment.

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

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

[0036] counting the icing frequency f_i=f_i / N of each area, N being the total number of monitoring times;

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

[0038] if f_i≥R, marking the icing risk level of the area as a high risk level G_high, otherwise marking it as a low risk level G_low, wherein 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 a set risk threshold R further comprises:

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

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

[0042] if R_f≥K, marking the icing risk level of the area as a high risk level G_high, otherwise marking it as a low risk level G_low, wherein R_f represents the risk factor and K represents the set risk factor threshold.

[0043] Preferably, the step of calculating a 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 includes:

[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] Display icons and warning information of the key focus areas 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 embodiments provide a method for automatically activating an intelligent anti-icing and snow removal control system. The method includes: dividing anti-icing and snow removal heating zones based on real-time aircraft surface temperature and icing data; activating the corresponding heating device when icing risk is detected in a specific area; dynamically adjusting the power of each heating zone based on environmental conditions and flight status; and recording and analyzing the operating status of each heating zone to optimize future zoning strategies for improved energy efficiency. This disclosed embodiment addresses the issue of intelligently zoning heating zones to balance 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 following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate 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 relevant drawings can be obtained based on these drawings without 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 that divides 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] Figure 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] Figure 10 It is a flowchart 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 document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0066] Next, an automatic activation control method of an intelligent anti-icing and snow removal control system according to the present invention will be described with reference to the accompanying drawings.

[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, anti-icing and snow removal heating areas are divided. Specifically, the control system collects data from sensors distributed throughout the aircraft surface. These sensors 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 an independent control unit, 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 has fallen below 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 it is detected that there is an icing risk 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 of 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 the power or start more heating devices. In one embodiment, when a small control unit on 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 wasting 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 to prevent rapid icing; and 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 zoning 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 zoning schemes and power settings are most effective, and then optimize future anti-icing strategies. In one embodiment, suppose the system records the temperature changes and ice conditions of a control unit during multiple flights. The analysis results show that under specific environmental conditions, low-power operation of a certain partition can effectively prevent ice and has lower 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 to intelligently partition heating areas to achieve a balance between energy consumption and anti-icing efficiency. First, when dynamically dividing heating areas, 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, lower-power control units are set up in this area to achieve refined control. Second, through machine learning algorithms, the system can predict the icing risk 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 anti-icing and snow removal heating areas in complex and changing environments, achieving the best energy efficiency balance.

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

[0074] S201: Calculating 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 zones based on average temperature and icing risk level;

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

[0078] First, based on real-time temperature data from various parts 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 placed in key areas such as the fuselage, wings, and tail. The following formula is used to calculate the average temperature:

[0079]

[0080] Where Tavg is the average temperature of a region, Ti is the temperature measured by the i-th 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 conditions of the region. For example, in one embodiment, if there are 10 sensors in the wing region and the temperatures they measure 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, historical icing data is analyzed to determine the icing risk level for each area. By collecting and analyzing historical flight data, the icing risk in different areas under specific meteorological conditions is assessed. Risk levels are generally categorized as low, medium, and high. For example, data analysis revealed that the front end of the wing is more susceptible to icing in low temperatures and high humidity, thus being assessed as a high-risk area.

[0083] The aircraft's surfaces are divided into several anti-icing and snow removal heating zones based on average temperature and icing risk. This step involves developing a heating strategy based on the average temperature and icing risk level for each zone. For example, a wing zone with an average temperature of -6.1°C and a high icing risk level would be designated as a high-priority heating zone. Another tail zone with an average temperature of -3°C and a medium icing risk level would be designated as a low-priority heating zone.

[0084] Finally, adjust the heating activation threshold for each area to optimize the balance between energy consumption and anti-icing efficiency. Different activation thresholds are set based on actual operating experience and experimental data. For example, for high-risk areas, the heating activation threshold is set to -4°C, and heating is activated when the temperature drops below this value; while for medium-risk areas, the activation threshold can be set to -5°C. The specific activation threshold settings are shown in the following formula:

[0085] Tthreshold = Tmin + k × Trisk, where Tthreshold is the threshold for heating activation, Tmin is the minimum safe temperature, typically around -10°C, Trisk is the correction factor corresponding to the icing risk level, ranging from 0 to 1, with values ​​close to 1 in high-risk areas and close to 0 in low-risk areas. k is an empirical adjustment factor, generally around 0.5. This setting is intended to reduce unnecessary energy consumption while ensuring safety. For example, assuming the minimum safe temperature is -10°C and Trisk = 0.9 for high-risk areas, the heating activation 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 heating areas 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: Obtain the real-time temperature T_i of each monitoring point on the aircraft surface, where i ranges from 1 to n. This step involves installing temperature sensors at different locations on the aircraft surface to collect real-time temperature data from each monitoring point. These monitoring points are distributed across key areas of the fuselage, such as the wings, tail, and engine air intakes. In this way, detailed temperature information can be obtained for each part of the aircraft surface, providing basic data for subsequent calculations. For example, suppose there are five monitoring points in a certain area, and the real-time temperatures of these 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 to obtain the average temperature of the area by summing the temperatures of all monitoring points in the area and dividing it by the number of monitoring points, so as to 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 according to 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, then 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 value preset by the system and 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 calculation, the 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 within the region.

[0094] S401: Analyze the average temperature T_avg with historical data to extract the historical average freezing temperature T_hist for the region. This step aims to derive a reference temperature standard by analyzing the current average temperature in the region and its historical freezing conditions, thereby more accurately assessing the current region's anti-icing needs. The historical average freezing temperature T_hist is typically calculated using years of meteorological data and typically ranges from -5°C to 0°C. This step helps differentiate the anti-icing needs of different geographic regions.

[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 average temperature T_avg and the historical average freezing temperature T_hist is used as the anti-icing priority factor F. The value of F ranges from 0 to infinity, but in practice is typically limited to between 0 and 10. This formula is designed to quantify the difference between current temperature conditions and historical freezing temperatures. A larger difference indicates a higher icing risk in the current area, and therefore a higher anti-icing priority.

[0096] S403: Based on the anti-icing priority factor F and the current ambient humidity H, calculate the overall priority P = F × H. This step incorporates the temperature difference and the current ambient humidity, as frost is more likely to form in high-humidity environments, further increasing the urgency of anti-icing. Ambient humidity H typically ranges from 0 to 100%. The P value also ranges from 0 to infinity, but in practical applications, it is typically set between 0 and 100. The significance of this formula is that by comprehensively considering the anti-icing priority factor F and the current ambient humidity H, a more comprehensive and accurate anti-icing priority indicator is obtained.

[0097] S404: If P>L and T_avg <T_threshold,增加该区域的加热功率P_heat。这里的L表示设定的综合优先级下限,通常设定为20左右;T_threshold表示设定的温度阈值,通常设定为-2℃左右。加热功率P_heat的范围可以根据具体设备和环境情况进行调整。如果当前的综合优先级P超过了设定的下限L并且当前的温度平均值低于设定的温度阈值T_threshold,说明当前区域的结冰风险非常高,系统应立即采取措施,增加加热功率以预防结冰。

[0098] For example, in one embodiment, assume that an aircraft is flying at high altitude in winter, and the average temperature T_avg of a specific area on the aircraft is -3°C, while the historical average freezing temperature T_hist of the 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 comprehensive priority lower limit L is 2, and the set temperature threshold T_threshold is -2°C, then P = 1.6 <L=2且T_avg=-3<T_threshold=-2,不满足加热条件,因此暂时不调整加热功率。

[0099] Specifically, assume that in another scenario, the average temperature T_avg in 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,满足加热条件,系统会自动增加该区域的加热功率P_heat,以预防可能出现的结冰问题,确保飞行安全。

[0100] Through these detailed steps and examples, it is demonstrated how to effectively determine anti-icing priorities based on the average temperature 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 for the region. This step aims to determine the historical average freezing temperature for the region, which serves as a reference point for anti-icing control. Historical data typically comes from long-term observations recorded by weather stations or the aircraft itself, ensuring data reliability and accuracy. Specifically, in one embodiment, assume that historical freezing temperature records for a certain aircraft model along a specific flight path show that the historical average freezing temperature T_hist for the region is -5°C.

[0103] S502: Real-time monitoring of the average temperature T_avg within the current area. This process is implemented using multiple temperature sensors installed on the aircraft's surface to ensure comprehensive and real-time data. The average temperature T_avg is the average of the data collected by all sensors over a period of time, reflecting the actual current environmental conditions. For example, during flight, five temperature sensors are installed on the underside of the wing. The temperatures collected at a given 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 evaluates the difference between the current temperature and the historical average freezing temperature. A larger difference indicates a more favorable environment for ice formation and more aggressive anti-icing measures should be taken. In this formula, T_avg is the average temperature in the current area, and T_hist is the historical average freezing temperature. Taking the absolute value ensures that F is always non-negative. Assuming T_avg = -3.2°C and T_hist = -5°C in the previous 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℃, then 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 both 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, involves continuously acquiring and recording ambient humidity values ​​within the aircraft's anti-icing control system. Ambient humidity, H, is typically expressed as a percentage, ranging from 0% to 100%. In typical applications, the optimal measurement range for ambient humidity is typically between 20% and 90%. This is because when the ambient humidity falls within this range, the likelihood of icing is highest, placing the greatest demands on the anti-icing system.

[0112] After real-time monitoring of the current ambient humidity H, the next step is to calculate the product of the anti-icing priority factor F and the ambient humidity H, P = F × H. The anti-icing priority factor F is a value based on a comprehensive assessment of the aircraft's current state and flight environment, and its range is generally 0 to 1. When the F value is close to 1, it means that the aircraft has a high icing risk 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 needs. By multiplying F and H, the actual moisture content in the current environment can be added while considering the possibility of icing, allowing the system to make more accurate judgments and responses.

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

[0114] In one embodiment, assuming that the current anti-icing priority factor F is 0.85 and the real-time monitored ambient humidity H is 60%, the comprehensive priority P is calculated as 0.85×60%=0.51. If the preset threshold S is 0.65, then P <S,因此不需要调整加热区域。但是,假设环境湿度突然上升至80%,新的P=0.85×80%=0.68,则P> S, then the system will expand the heating area and increase the heating intensity to prevent ice from forming.

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

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

[0117] S701: Collect historical icing data D_i for each aircraft surface region, where i ranges from 1 to n. D_i represents the number of icing events in region i during historical monitoring, with i ranging from 1 to n, and n representing the number of monitored aircraft surface regions. This data can be obtained from historical flight records, meteorological data, or onboard sensors. The purpose of collecting historical icing data is to understand the occurrence of icing in each region.

[0118] S702: Calculate the icing frequency of each region, f_i = D_i / N, where N is the total number of monitoring times. Here, f_i represents the icing frequency of region i across all monitoring times, and N is the total number of monitoring times. By calculating the icing frequency of each region, the probability of icing in each region can be quantified. For example, in one embodiment, if a region experiences icing 150 times out of a total of 1000 monitoring times, the icing frequency for that region, 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 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 subsequent formulation of anti-icing measures. For example, R can be set to 0.1, indicating 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 determine which areas are more prone to icing during flight by comparing the icing frequency of each area with the risk threshold, so that corresponding preventive measures can be taken. 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 in each area. This step is to obtain the actual icing frequency in different areas. The icing frequency refers to the number of times icing occurs in a certain period of time. These frequencies can be obtained through 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 for 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 in different areas for subsequent comparison. The value range of R_f is from 0 to 1, which represents 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 a preset risk factor threshold used to determine whether an area belongs to 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 one specific embodiment, assume that during a flight mission, multiple sensors are distributed across key locations, such as the wings, fuselage, and engine air intakes, to monitor the icing frequency in these areas. Data analysis reveals that the icing frequency f_1 for the wings is 0.3 times / hour, the icing frequency f_2 for the fuselage is 0.1 times / hour, and the icing frequency f_3 for the engine air intakes is 0.5 times / hour. Furthermore, the minimum icing frequency f_min is 0.1 times / hour, and the maximum icing frequency f_max is 0.5 times / hour. The risk factor R_f for each region is calculated using the following formula: 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, and engine air intake R_f3 = (0.5-0.1) / (0.5-0.1) = 1. Assuming the risk factor threshold K = 0.8, the engine air inlet area is assigned a high-risk level (G_high) due to R_f3 ≥ 0.8, while the wing and fuselage areas are assigned a low-risk level (G_low). Based on this result, the system prioritizes anti-icing and snow removal measures for the engine air inlet to ensure flight safety.

[0126] Through the above steps, the present invention can effectively quantify the icing frequency of each area and determine 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 Figure 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 for each zone. In an intelligent anti-icing and snow removal control system, a sensor network monitors icing conditions in different zones and collects the number of icing events per unit time in each zone. The value f_i represents the icing frequency in zone i, typically expressed as the number of icing events per hour or per flight.

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

[0130] S903: Calculate the risk factor R_f = (f_i - f_min) / (f_max - f_min). This step normalizes the icing frequency f_i for each region and calculates its relative position among all monitoring points. The value of R_f ranges between 0 and 1. When f_i for a region is close to f_max, R_f is close to 1, indicating a high icing risk. When f_i is close to f_min, R_f is close to 0, indicating a low icing risk. This formula makes icing risk assessment more intuitive and accurate, facilitating 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 based on system requirements and safety standards, typically between 0 and 1. Specifically, the optimal value of J needs to be determined through experimentation and historical data statistics to ensure timely identification of high-risk areas while minimizing 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, allowing for more stringent anti-icing measures to be implemented.

[0132] In one embodiment, assume that there are three primary monitoring areas on an aircraft, designated A, B, and C. Monitoring data shows that the icing frequencies in these three areas are: f_A = 3 times / hour, f_B = 1 time / hour, and f_C = 5 times / hour. Therefore, f_min = 1 and f_max = 5. The risk factor for each area is calculated using 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. Assuming J = 0.7, then R_C > 0.7, and area C will be marked as A_focus. The system will then increase the intensity and frequency of anti-icing and snow removal measures for this area.

[0133] Next, refer to Figure 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 for each area. This factor is calculated by comprehensively evaluating multiple parameters, including temperature, humidity, wind speed, and other meteorological data detected by sensors within each area, as well as the ice thickness on the aircraft surface. These parameters include temperature T (range -40°C to 20°C), 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, and d are weighting coefficients for these parameters. The values ​​of a, b, c, and d typically range from [0 to 1], with optimal values ​​of 0.4, 0.3, 0.2, and 0.1, respectively. These weights are set to prioritize the impact of temperature and humidity. The formula aims to derive a quantitative value through weighted calculations, used to assess whether an area has a high risk of ice or snow accumulation.

[0135] S1002: If R_f>J, the area is designated as a focus area A_focus requiring additional monitoring. Here, J is the preset risk factor threshold, for example, J = 70. This value is set to filter out high-risk areas, ensuring that these areas receive timely attention and action, thereby reducing security risks. A value of J that is too high can cause many low-risk areas to be misclassified as high-risk, increasing unnecessary monitoring burdens. A value that is too low can cause high-risk areas to be overlooked, impacting security.

[0136] S1003: Display an icon and warning message for the focus area on the system control interface. For example, the icon for the corresponding area on the control interface will turn red and flash, and a warning message will pop up: Risk factor R_f of area A_focus = X, please strengthen monitoring. This can intuitively alert operators and facilitate quick response and decision-making.

[0137] S1004: If the number of focus areas exceeds a 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 indicates that multiple areas are simultaneously at high risk, and additional resources may be required to address the potential risk, such as activating additional heating devices or dispatching more personnel for inspections. The purpose of triggering the alarm mechanism is to ensure the efficient 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 and R_f = 73 in area 5, while the R_f values ​​of other areas are all below 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 subsequently exceeds 70, 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 present invention provides an automatic activation control method for an intelligent anti-icing and snow removal control system, comprising: dividing anti-icing and snow removal heating zones based on real-time temperature and icing condition data on the aircraft surface; activating the heating device in the corresponding zone when an icing risk is detected in a specific zone; dynamically adjusting the power of each heating zone according to environmental conditions and flight status; recording and analyzing the operating status of each heating zone, and optimizing future zoning strategies to improve energy efficiency.

[0140] By implementing the above steps, the present invention effectively solves the problem of intelligently zoning heating zones, achieving a balance between energy consumption and anti-icing efficiency. Traditional anti-icing and snow removal methods often use global heating or pre-defined zones, which can lead to energy waste and difficulty adapting to complex environmental changes. However, through real-time data monitoring, intelligent zoning, and dynamic power adjustment, the present invention enables heating in each zone to more precisely match actual needs, thereby maximizing energy efficiency while ensuring flight safety.

[0141] The methods, programs, systems, and apparatuses 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 will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, those skilled in the art will appreciate that the functional modules / units or controllers and related method steps described in the above embodiments may be implemented using software, hardware, or a combination of software / hardware.

[0143] Unless explicitly stated, the actions or steps of the methods, procedures, and methods described in accordance with the 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 document, 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 document, "one embodiment", "some embodiments", "example", "specific example", or "some examples" are intended to apply 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. Those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually contradictory.

[0145] While the exemplary systems and methods of the present invention have been specifically shown and described with reference to the foregoing embodiments, these are merely examples of the best modes for implementing the present systems and methods. Those skilled in the art will appreciate 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: include: Based on the real-time temperature and icing data of the aircraft surface, anti-icing and snow removal heating areas are divided; When it is detected that there is a risk of icing in the anti-icing and snow removal heating area, activating the heating device of the corresponding area; Dynamically adjust the power of each heating area according to environmental conditions and flight status; Record and analyze the working status of each heating zone and optimize future zoning strategies to improve energy efficiency; 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 includes: Based on the real-time temperature data of each part of the aircraft surface, calculate the average temperature of each area; Analyze historical icing data to determine the icing risk level in each area; Divide the aircraft surface into multiple anti-icing and snow removal heating zones based on average temperature and icing risk level; Adjust the heating activation threshold for each zone to optimize the balance between energy consumption and anti-icing efficiency; 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 icing 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.

2. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 1, 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: Get the real-time temperature T_i of each monitoring point on the aircraft surface, where i is 1 to n; Calculate the average temperature in each area T_avg = (T_1 + T_2 + ... + T_n) / n; Determine the anti-icing priority P of the area based on the average temperature in the area; If P ≥ k, the area is marked as a high priority area, where P represents the anti-icing priority and k represents the preset priority threshold.

3. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 2, characterized in that: The step of determining the anti-icing priority P of the area based on the average temperature in the area further includes: Analyze the average temperature T_avg and historical data to extract the historical average freezing temperature T_hist of the area; Calculate the anti-icing priority factor F = |T_avg-T_hist|; Based on the anti-icing priority factor F and the current ambient humidity H, calculate the anti-icing priority P = F × H; If P > L and T_avg < T_threshold, increase the heating power P_heat of the area, where F is the anti-icing priority factor, H is the current ambient humidity, P is the anti-icing priority, L is the set lower limit of the comprehensive priority, T_threshold is the set temperature threshold, and P_heat is the heating power.

4. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 3, characterized in that: The step of calculating the anti-icing priority factor F = |T_avg-T_hist| further includes: Get the historical average freezing temperature T_hist in the area; Real-time monitoring of the average temperature T_avg in the current area; Calculate the anti-icing priority factor F = |T_avg-T_hist|; If F > G, adjust the heating start and stop frequency of the area f_heat = F / C, where G represents the set anti-icing priority threshold, f_heat represents the heating start and stop frequency, and C represents the constant coefficient.

5. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 3, characterized in that: The step of calculating the anti-icing priority P = F × H based on the anti-icing priority factor F and the current ambient humidity H further includes: Real-time monitoring of ambient humidity H; Calculate the product of the anti-icing priority factor F and the ambient humidity H: P = F × H; Based on the anti-icing priority P, adjust the coverage R_coverage of the heating area; If P > S, expand the coverage of the area by 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.

6. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 1, 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.

7. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 6, 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 icing frequency f_min and the maximum icing 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.

8. The automatic activation control method of an intelligent anti-icing and snow removal control system according to claim 7, characterized in that: If R_f>J, the step of marking the area as the focus area A_focus 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; Display icons and warning information of the key focus areas 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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