Deicing method of airport deicing vehicle

Abstract

The embodiment of the invention provides a deicing method for an airport deicing vehicle, and the method comprises the steps: dynamically generating an optimized advancing path based on the current working state of the deicing vehicle and the geographic information of a to-be-deiced region; controlling the deicing vehicle to move to a specified position according to the advancing path; the deicing equipment is started, and deicing operation on the designated position is started; and the deicing effect is monitored in real time, and the deicing parameters are adjusted or the advancing path is re-planned according to the deicing condition. Through the scheme of the embodiment of the invention, the problem of how to optimize the traveling path of the vehicle so as to solve the problem of non-uniform coverage rate in deicing operation can be solved.

Description

Technical Field

[0001] The present application relates to the technical field of transportation equipment maintenance and service, and in particular to a deicing method for an airport deicing vehicle. Background Art

[0002] Airport de-icing vehicles spray de-icing fluid on snow-covered runways or remove snow and ice physically to ensure flight safety and flight punctuality. However, in actual operations, the vehicle's travel path selection has a significant impact on the de-icing effect. In particular, when the path planning is inappropriate, it is easy to have problems such as inadequate de-icing and uneven coverage in some areas, resulting in reduced de-icing efficiency and reduced safety. Summary of the invention

[0003] In view of this, an embodiment of the present disclosure provides a deicing method for an airport deicing vehicle, which at least partially solves the problems existing in the prior art.

[0004] A deicing method for an airport deicing vehicle of the present application comprises:

[0005] Dynamically generate an optimized travel path based on the current working status of the deicing vehicle and the geographic information of the area to be deiced;

[0006] Control the de-icing vehicle to move to the designated location according to the travel path;

[0007] Start the de-icing equipment and begin de-icing operations at the designated location;

[0008] Monitor the de-icing effect in real time, adjust de-icing parameters or re-plan the travel path according to the de-icing situation.

[0009] In a specific embodiment, the step of dynamically generating an optimized travel path based on the current working state of the deicing vehicle and the geographical information of the area to be deiced further includes:

[0010] Obtain the working status data of the de-icing vehicle and the geographic information of the area to be de-iced;

[0011] Analyzing density distribution of coverage paths based on the working status data and geographic information of the area to be de-iced;

[0012] Adjusted the path generation algorithm to increase path density in low-density areas;

[0013] If the coverage density D of a certain area is less than a preset density threshold T, the path is preferentially adjusted so that the area is covered multiple times, where D represents the area coverage density and T represents the preset density threshold.

[0014] In a specific embodiment, the step of analyzing the density distribution of the coverage path based on the working status data and the geographical information of the area to be deiced further includes:

[0015] Collect the coverage frequency of path points;

[0016] Draw a coverage density distribution map;

[0017] Identify high and low density regions based on the coverage density distribution map;

[0018] Among them, the coverage density D_i of a certain point i is N_i / A. When D_i < k, mark this point as a low density region, where N_i represents the coverage times of a certain point i, A represents the area of the region, D_i represents the coverage density of a certain point i, and k represents the preset minimum density standard.

[0019] In a specific embodiment, the step of identifying high and low density regions based on the coverage density distribution map further includes:

[0020] Divide the deicing area to be processed into multiple sub-regions;

[0021] Calculate the average coverage density of each sub-region;

[0022] Mark the sub-regions with an average coverage density lower than the threshold T as low density sub-regions;

[0023] The average coverage density D_j of a certain sub-region j is Σ(D_{ij}) / n. When D_j < k, mark this sub-region j as a low density sub-region, where Σ(D_{ij}) represents the sum of the coverage densities of all points in the region, n represents the number of points in the sub-region, D_j represents the average coverage density of the sub-region j, and k represents the preset density threshold.

[0024] In a specific embodiment, the step of adjusting the path generation algorithm to increase the path density in low density regions further includes:

[0025] Add additional paths for the identified low density regions;

[0026] Optimize the path generation algorithm to reduce duplicate paths in high density regions;

[0027] Consider the deicing time and efficiency and comprehensively adjust the path density;

[0028] If the total length L_total of the optimized path is less than or equal to e times the original path length L_original, it is considered that the path optimization is effective, where L_total represents the total length of the optimized path, L_original represents the total length of the original path, and e is a system preset threshold greater than 1.

[0029] In a specific embodiment, the step of adding additional paths for the identified low density regions further includes:

[0030] Determine the key points of the low density region;

[0031] Establish connecting paths between key points;

[0032] Check how much the new path overlaps the existing path;

[0033] If the ratio of the overlapping length S_cross of the new path and the existing path to the total length S_total of the new path r = S_cross / S_total is greater than f, the direction or position of the new path is adjusted to ensure effective coverage of the low-density area, where S_cross represents the overlapping length of the new path and the existing path, S_total represents the total length of the new path, r represents the overlapping ratio, and f is a predetermined threshold.

[0034] In a specific embodiment, the step of adding additional paths for the identified low-density areas further comprises:

[0035] Identify critical path points within low-density areas;

[0036] Calculate the path optimization weight of each key path point;

[0037] Adjust the driving speed of the de-icing vehicle according to the path optimization weight;

[0038] If the path optimization weight W_i>W_0 of the critical path point i, the stay time T_i at this point is increased to improve the deicing effect, where W_i represents the path optimization weight of the critical path point i, W_0 represents the benchmark optimization weight, and T_i represents the stay time at the critical path point i.

[0039] In a specific embodiment, the step of calculating the path optimization weight of each key path point further includes:

[0040] Collect data on de-icing effectiveness in low-density areas;

[0041] Analyze data to determine the importance of each waypoint;

[0042] Combine deicing effect data and path density to calculate path optimization weights;

[0043] If the comprehensive score S = α × D_i + β × E_i of the coverage density D_i and the deicing effect E_i of the key path point i is greater than the threshold γ, the path optimization weight W_i of the key path point i is increased to ensure high-efficiency coverage, where α and β are trade-off coefficients.

[0044] In a specific embodiment, the calculation of the path optimization weight of each key path point also includes calculating the optimization weight factor of each path point based on the effect index, where the optimization weight factor W_i of path point i = (1+k×ΔE), ΔE is the effect improvement of the path point, and k represents the adjustment coefficient.

[0045] The disclosed embodiment provides a deicing method for an airport deicing vehicle, including: dynamically generating an optimized travel path based on the current working state of the deicing vehicle and the geographical information of the area to be deiced; controlling the deicing vehicle to move to a designated location according to the travel path; starting the deicing equipment to start deicing operations at the designated location; real-time monitoring of the deicing effect, adjusting deicing parameters or replanning the travel path according to the deicing situation. Through the solution of the disclosed embodiment, it is possible to solve how to optimize the vehicle's travel path to solve the problem of uneven coverage in deicing operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in the present application and should not be regarded as limiting the scope of the present application.

[0047] Figure 1 It is a flow chart of a method for de-icing an airport de-icing vehicle;

[0048] Figure 2 It is a flow chart for dynamically generating an optimized travel path based on the current working status of the de-icing vehicle and the geographic information of the area to be de-iced;

[0049] Figure 3 It is a flow chart for analyzing the density distribution of the coverage path based on the working status data and the geographic information of the area to be de-iced;

[0050] Figure 4 It is a flow chart for identifying high and low density areas based on the coverage density distribution map;

[0051] Figure 5 is a flow chart for adjusting the path generation algorithm to increase path density in low-density areas;

[0052] Figure 6 It is a flow chart for adding additional paths for the identified low-density areas;

[0053] Figure 7 It is a flow chart for adding additional paths for the identified low-density areas;

[0054] Figure 8 It is a flow chart for calculating the path optimization weight of each key path point. DETAILED DESCRIPTION

[0055] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0056] Next, a deicing method for an airport deicing vehicle according to the present invention will be described with reference to the accompanying drawings.

[0057] like Figure 1 As shown, the method of the present application includes:

[0058] S101: dynamically generating an optimized travel path based on the current working state of the deicing vehicle and the geographical information of the area to be deiced;

[0059] S102: Control the deicing vehicle to move to a designated location according to the travel path;

[0060] S103: Start the deicing equipment and start deicing operation at the designated location;

[0061] S104: Monitor the deicing effect in real time, and adjust deicing parameters or re-plan the travel path according to the deicing situation.

[0062] Dynamically generating an optimized travel path is the basic step of the entire deicing method. In this step, first, the current working status data of the deicing vehicle is collected, including fuel, power level, system health status, etc. At the same time, the geographical information of the area to be deiced is obtained, such as the area, terrain characteristics, snow thickness and distribution, etc. By comprehensively analyzing this information, the algorithm model is used to dynamically generate the most optimized travel path. This can ensure that the deicing operation is carried out efficiently, reduce invalid paths and duplication of work, and improve the overall deicing efficiency. For example, in one embodiment, assuming that the snow coverage area of ​​the runway and taxiway of a certain airport is large, the current position of the deicing vehicle is obtained through the GPS positioning system, and the snow thickness and density data collected by the laser radar and camera sensors are combined. The central processing unit calculates and generates an optimal path so that the deicing vehicle can complete the deicing task of the entire operating area in the shortest time.

[0063] According to the optimized travel path, the de-icing vehicle is controlled to move to the specified position. In this step, the control system accurately guides the de-icing vehicle to travel along the established route and reach each predetermined de-icing point according to the pre-generated path map. Specifically, the control system uses a variety of sensors and technical means, such as lidar, ultrasonic sensors, infrared cameras, etc., to detect the position of the vehicle and environmental changes in real time, and adjust the driving direction and speed in time to ensure safety and accuracy. For example, in actual operation, when the de-icing vehicle is traveling along the optimized path, if it encounters sudden obstacles (such as debris accidentally scattered on the runway), the central processing unit will respond quickly and redirect the vehicle back to the main path through an emergency avoidance program to ensure that the de-icing operation is not affected.

[0064] Starting the de-icing equipment and performing de-icing operations at designated locations is the actual step of removing ice accumulation. In this process, the operator or the autopilot system turns on the de-icing equipment, such as high-pressure jets, snow blowers, chemical de-icing sprayers, etc., and sets and uses them accordingly according to the different conditions of the current location. Real-time monitoring of equipment performance indicators and operating results, such as wind speed, temperature, humidity and other parameters, ensures that the de-icing process is stable and effective. For example, in the cold winter, an international airport was suspended due to heavy snow. After receiving the start-up command, the de-icing team immediately went to the scene and used large de-icing vehicles equipped with automatic adjustment nozzles and thermal sensors to start clearing key flight routes. The central control system can fine-tune the angle and spray force of the nozzle in real time according to the detected data to achieve the best de-icing effect, thereby quickly restoring the normal operation order of the flight.

[0065] The steps of real-time monitoring of de-icing effects and timely adjustment of strategies or re-planning of routes require immediate verification of the processing results after each de-icing point is completed, and use video monitoring, thermal imaging scanning, etc. to check whether there is residual frost. If a problem is found, the background system will be notified immediately to re-evaluate the actual situation in the local area. It may be necessary to fine-tune the working parameters of the de-icing equipment or recalculate a more reasonable driving route to more comprehensively cover all areas to be de-iced. For example, in a specific use case, during a night de-icing operation, the technicians found that the de-icing effect was poor in some areas due to the special terrain. After multiple tests and verifications, it was decided to improve it by using low-altitude, low-speed and multiple coverage methods. At the same time, the connection relationship between all nodes in the area was updated to ensure that each branch can be effectively reached, and finally the expected coverage standard was achieved.

[0066] Through the effective implementation of the above four steps, not only can the efficiency of airport de-icing work be significantly improved, but also problems such as uneven coverage in traditional methods can be solved.

[0067] Next, refer to Figure 2, describing the steps of the present invention for dynamically generating an optimized travel path based on the current working status of the deicing vehicle and the geographical information of the area to be deiced.

[0068] S201: Acquire working status data of the deicing vehicle and geographic information of the area to be deiced;

[0069] S202: Analyzing density distribution of coverage paths based on the working status data and geographic information of the area to be de-iced;

[0070] S203: adjusting the path generation algorithm to increase the path density in the low-density area;

[0071] S204: If the coverage density D of a certain area is less than a preset density threshold T, the path is preferentially adjusted so that the area is covered multiple times, where D represents the area coverage density and T represents the preset density threshold.

[0072] The first step is to obtain the working status data of the de-icing vehicle and the geographical information of the area to be de-iced. This step involves collecting data from multiple sensors and map systems. Specifically, the working conditions of the de-icing vehicle, such as the amount of fuel, remaining spraying liquid, current speed, etc., are transmitted to the control system in real time through the vehicle's built-in sensors, while the airport map, runway, taxiway layout and other geographical information are obtained through high-precision map data. In one embodiment, assuming that the de-icing operation area of ​​an airport consists of multiple runways and taxiways, the system needs to ensure that the acquired data can fully reflect the conditions of these areas in order to provide an accurate basis for subsequent path planning.

[0073] Secondly, based on the working status data and the geographic information of the area to be de-iced, the density distribution of the coverage path is analyzed. This process is implemented through computer simulation or historical data analysis, with the aim of finding out the deficiencies in the current path plan, especially which areas are over- or under-covered. For example, when performing a de-icing mission, it may be found that some areas at the end of the taxiway are less covered due to their remote location, resulting in ice thickness exceeding the standard. At this point, it is necessary to record the specific location and scope of these low-density coverage areas to prepare for the next adjustment.

[0074] Secondly, adjust the path generation algorithm to increase the path density in low-density areas. This is the core step of the entire path optimization. By fine-tuning or replanning the original route locally, we strive to achieve the ideal deicing effect for each piece of ground to be processed. For example, in the above example, intelligent computing methods such as genetic algorithms or ant colony optimization algorithms can be used to dynamically adjust the path nodes so that the previously discovered weak areas can be visited and processed more times.

[0075] Finally, if the coverage density D of a certain area is less than the preset density threshold T, the path is preferentially adjusted to cover this area multiple times. Here, D and T respectively refer to the actual coverage density of the area and the desired minimum standard. The setting of T depends on specific factors such as climate conditions and ground types, and generally ranges from 1.5 to 3 times per square meter to ensure sufficient de-icing efficiency while avoiding resource waste. When D < T, it means that this area has not been sufficiently de-iced, so it is necessary to increase the number of passes or adjust the driving direction to increase the coverage density. Continuing with the above case, on a particular cold night, in the middle section of a relatively wide runway, the phenomenon of D < T occurred due to severe icing. For this reason, the system will automatically adjust the path so that it returns to this area for supplementary de-icing after completing other tasks.

[0076] Through the combined effect of the above steps, it is finally possible to quickly and efficiently formulate and execute a reasonable de-icing plan in a complex environment, thus ensuring flight safety and airport operation efficiency.

[0077] Next, refer to Figure 3 , and describe the steps of analyzing the density distribution of the coverage path based on the working state data and the geographical information of the area to be de-iced of the present invention.

[0078] S301: Collect the coverage frequency of path points. This step means that during the de-icing task execution, the system needs to record the number of times the de-icing vehicle stops and passes through each specific geographical point. The position information of these points is provided by on-vehicle GPS devices or ground monitoring systems to ensure the accuracy and timeliness of the coverage points. For example, during the de-icing operation of an airport runway, the de-icing vehicle will make multiple round trips at different positions to spray de-icing fluid. By recording the points and the number of times each vehicle passes through, a detailed coverage rate database can be constructed.

[0079] S302: Draw a coverage density distribution map. After completing the statistics of the coverage frequency of path points, the next step is to visualize these data to generate a coverage density distribution map. This can be achieved through a GIS geographical information system, using different colors or symbols to represent the coverage density of each area. Specifically, after a certain de-icing task is completed, the operator can see the coverage points on the airport runway and taxiway from the data visualization interface of the system, and the color of each point changes according to the different coverage densities, thus intuitively showing the coverage situation of the de-icing work.

[0080] S303: Identify high- and low-density areas based on the coverage density distribution map. On the density distribution map, different density areas are distinguished by a preset threshold k.

[0081] S304: If the coverage density D_i of a point i is less than k, the area where the point is located is marked as a low-density area. This identification helps to find the insufficient or excessive coverage, which is convenient for optimizing the subsequent work path planning. For example, after several de-icing operations at an airport in winter, the staff found that there was an obvious low-density area in a certain taxiway through the coverage density distribution map, which means that this area may not have received enough de-icing coverage and needs additional attention and adjustment.

[0082] The coverage density D_i of a point i is N_i / A, where N_i represents the number of times a point i is covered, A represents the area of ​​the area, D_i represents the coverage density of a point i, and k represents the preset minimum density standard. In this formula, N_i is a positive integer that reflects the number of times the deicing vehicle passes through the same location; the unit of A is square meters, which is usually the effective coverage area within a certain radius around the path point. The selection of the optimal value depends on the needs of the specific application scenario. The formula is designed to obtain an indicator that can reflect the uniformity of deicing work by calculating the relationship between the actual coverage frequency of each location and its area. For example, assuming k = 0.8, this means that each unit area should be passed by the deicing vehicle at least 0.8 times before the area can be considered to be fully covered by deicing. The purpose of this setting is to ensure the effectiveness and reliability of deicing work, while reducing repetitive and unnecessary operations and improving overall work efficiency.

[0083] Next, refer to Figure 4 , further describing the steps of identifying high-density and low-density areas based on the coverage density distribution map of the present invention. These steps are intended to divide the area to be de-iced into multiple sub-areas, and identify high-density and low-density areas by calculating the average coverage density of each sub-area.

[0084] S401: Divide the area to be de-iced into multiple sub-areas. This step involves subdividing the area to be de-iced into several smaller sub-areas to achieve more detailed analysis. The size of the sub-area can be adjusted according to actual needs, but it should generally be ensured that the coverage rate within the sub-area does not change too much, thereby improving the accuracy of subsequent calculations. For example, in one embodiment, the area to be de-iced is divided into 10x10 meter square sub-areas to ensure that each sub-area is sufficiently representative.

[0085] S402: Calculate the average coverage density of each sub-region. This process requires measuring or estimating the coverage density in each sub-region. Specifically, the average coverage density of each sub-region can be calculated by the following formula: D_j = Σ(D_{ij}) / n. Wherein, D_j represents the average coverage density of sub-region j, Σ(D_{ij}) represents the sum of the coverage density of all points i in sub-region j, and n is the number of points in sub-region j. The range of D_{ij} in the formula is usually between 0 and 1, indicating the coverage density of a single point; the value range of n depends on the size of the sub-region and the resolution of the density point, but is usually a positive integer. This formula is used to evaluate the average coverage of each point in the sub-region to ensure that the calculation results are accurate and reliable. For example, specifically, if a sub-region contains 100 points and the coverage density of each point is 0.8, then the average coverage density D_j of the sub-region is 0.8.

[0086] S403: Mark the sub-area with average coverage density lower than threshold T as a low-density sub-area. This step involves comparing the calculated average coverage density with the preset threshold T. When the average coverage density D_j of a sub-area j is lower than the threshold T, it is marked as a low-density sub-area. The selection of threshold T should be based on the needs of the actual deicing task, usually ranging from 0 to 1, and the optimal value depends on the specific environmental conditions and deicing standards. For example, in one embodiment, the preset threshold T is 0.6. If the average coverage density of a sub-area is 0.5, the sub-area is marked as a low-density sub-area and requires special attention and processing.

[0087] Through the above steps, high-density and low-density areas can be effectively identified in the complex de-icing area, thus providing an important basis for optimizing the path planning and resource allocation of de-icing vehicles. This method not only improves de-icing efficiency, but also reduces resource waste, ensuring the safety and efficiency of airport operations.

[0088] Next, refer to Figure 5 , describing the steps of adjusting the path generation algorithm of the present invention to increase the path density in low-density areas.

[0089] S501: Add additional paths for identified low-density areas. Specifically, low-density areas refer to areas in a certain area on an airport runway or apron where the coverage rate of de-icing vehicles is low. The purpose of adding additional paths is to ensure that these areas are adequately covered to improve the overall de-icing effect. For example, in one embodiment, if it is detected by drones or ground sensors that some areas are not thoroughly de-iced, these areas will be marked as low-density areas. The system will automatically generate new paths for these areas and insert them into the existing de-icing routes to ensure that each low-density area is adequately processed.

[0090] S502: Optimize the path generation algorithm to reduce repeated paths in high-density areas. This step is intended to avoid waste of resources and improve work efficiency. High-density areas refer to certain specific locations where the same area is passed through multiple times due to previous path design. This not only reduces efficiency but may also lead to the use of excessive deicing materials. To achieve this goal, the shortest path algorithm in graph theory (such as the Dijkstra algorithm or the A× algorithm) can be used to minimize repeated paths while ensuring that all necessary areas are covered. In one embodiment, by analyzing historical data and current environmental parameters, the algorithm can automatically detect high-density areas and replan paths to reduce invalid operations. For example, when it is detected that a certain area has reached the required deicing standard, the algorithm will remove or merge subsequent paths passing through the area to optimize the overall path.

[0091] S503: Consider deicing time and efficiency, and comprehensively adjust the path density. The goal of this step is to maximize the deicing efficiency while ensuring safety and effectiveness. Specifically, it is necessary to comprehensively consider factors such as the travel speed, operation time, fuel consumption, etc. of the deicing vehicle, and reasonably arrange the workload of each path. For example, in one embodiment, assume that the airport runway is 3 kilometers long, and the average travel speed of the deicing vehicle is 20 kilometers per hour, and the maximum endurance time of a single operation is 2 hours. Based on these parameters, the system will calculate the optimal path arrangement so that the deicing vehicle can complete the deicing work of the entire area in the shortest time. Specifically, the system will add time weights when generating paths, giving priority to path solutions with shorter time and larger coverage areas.

[0092] S504: If the total length of the optimized path L_total is less than or equal to e times the original path length L_original, the path optimization is considered to be effective, where L_total represents the total length of the optimized path, L_original represents the total length of the original path, and e is a system predetermined threshold greater than 1. As described above, in the present application, the total path may become longer due to the addition of additional paths for the identified low-density areas. The purpose of this formula is to ensure the actual effect of the optimized path and avoid excessive complexity without substantial improvement. The value of the parameter e is usually determined according to the specific airport size and deicing requirements, generally between 1.1 and 1.5, and the optimal value is about 1.3. For example, if the total length of the original path of an airport is 10 kilometers, and the total length of the path after the system is optimized is 11 kilometers, and e=1.3 is set, then because 11<10×1.3=13, the path optimization is considered to be effective. This setting is to retain a certain degree of path optimization without being too complicated, resulting in increased operating costs.

[0093] Next, refer to Figure 6 , describing the steps of the present invention for adding additional paths for the identified low-density areas.

[0094] S601: Identify key points in low-density areas. This step aims to find the most representative or key locations in low-density areas, which are usually the key points that must be passed in path planning. By identifying these key points, it can be ensured that the setting of new paths is more targeted and efficient. For example, in an airport de-icing scenario, low-density areas may be areas that are not adequately covered by de-icing vehicles. Key points in these areas may include the edge of the runway, the intersection of the taxiway, or special locations on the apron.

[0095] S602: Establish connection paths between key points. This step forms a new path or path network by connecting the key points determined in the previous step, ensuring that each key point in the low-density area is effectively covered. Specifically, in one embodiment, a path planning algorithm (such as Dijkstra algorithm or A× algorithm) can be used to calculate the optimal path to ensure that the path is short and efficient.

[0096] S603: Check the degree of overlap between the new path and the existing path. After the new path is established, it is necessary to evaluate the relationship between this path and the existing path to avoid redundancy or unnecessary waste of resources. Specifically, by calculating the overlapping length S_cross between the new path and the existing path and the total length S_total of the new path, the overlap ratio r = S_cross / S_total can be obtained. Here, S_cross is the length of the overlapping part of the new path and the existing path, S_total is the total length of the new path, and the overlap ratio r is used to measure the degree of overlap.

[0097] S604: If the overlap ratio r between the new path and the existing path is greater than the predetermined threshold f, the direction or position of the new path is adjusted to ensure effective coverage of the low-density area. The purpose of this step is to optimize path planning, reduce resource waste and improve efficiency. The threshold f is usually set between 0 and 1, and the optimal value can be adjusted according to actual needs, generally between 0.2 and 0.4. Specifically, if in the airport deicing scenario, the overlap ratio between the new path and the existing path reaches 0.3, and the preset threshold f is 0.2, the new path needs to be readjusted to better cover the low-density area.

[0098] Here, if the overlap ratio r is too large, it indicates that there is a lot of overlap between the new path and the original path, which means that the new path has more similarities with the original path, and the change effect on the low-density area may not be obvious enough, because the previous path has already led to a low-density area, and similar paths may also lead to similar results. Therefore, in this application, if the overlap ratio r is greater than the predetermined threshold f, the direction or position of the new path is adjusted.

[0099] Through the above steps, the operating efficiency and coverage of the airport de-icing vehicle can be effectively improved to ensure that each key area can be effectively handled.

[0100] Next, refer to Figure 7 , describing the steps of the present invention for adding additional paths for the identified low-density areas.

[0101] S701: Determine key path points in low-density areas. The purpose of this step is to find key locations in low-density areas that may require special attention to ensure effective de-icing. For example, in one embodiment, areas with low snow thickness can be identified through sensors or image processing technology, and key path points in these areas are where additional processing is required.

[0102] S702: Calculate the path optimization weight of each key path point. The path optimization weight is a numerical value used to measure the importance of a specific path point, and its value range is [0,1]. The closer the value is to 1, the more important it is. In this process, the weight value can be calculated by analyzing the environmental conditions around the key path point (such as snow thickness, ground friction, etc.). In one embodiment, the calculation formula for the path optimization weight is as follows: W_i = (S_i + F_i) / M, where S_i is the snow thickness, F_i is the ground friction, and M is a normalized constant. The significance of the formula is that it comprehensively considers the impact of snow thickness and ground friction on the deicing effect, so that the weight value can more accurately reflect the importance of the key path point.

[0103] S703: Adjust the driving speed of the de-icing vehicle according to the path optimization weight. In this step, the driving speed of the de-icing vehicle will be dynamically adjusted according to the path optimization weight of the key path point. If the importance of the key path point is high (that is, the weight value is large), the driving speed is appropriately reduced to ensure sufficient deicing. Conversely, if the importance of the key path point is low, the driving speed can be increased to improve work efficiency. Specifically, in one embodiment, the driving speed V can be calculated according to the following formula: V = V_0 × (1-αW_i), where V_0 is the initial speed, α is the adjustment coefficient, the value range is (0,1), and the optimal value is about 0.5. The function of the adjustment coefficient is to control the adjustment range of the driving speed to prevent excessive changes in speed from affecting work safety and efficiency.

[0104] S704: If the path optimization weight W_i of the critical path point i is greater than the benchmark optimization weight W_0, increase the dwell time T_i at the point to improve the deicing effect. In this step, the value of the benchmark optimization weight W_0 is usually set to 0.7. When the path optimization weight of a critical path point exceeds this threshold, it means that the snow accumulation at the point is serious or the ground friction is large, and more dwell time is required for effective deicing operations. The specific dwell time can be calculated according to the following formula: T_i = T_0 × (W_i-W_0), where T_0 is the benchmark dwell time, which is 1 minute, and the optimal value is also 1 minute. The purpose of increasing the dwell time is to ensure that the snow at the critical path points can be completely cleared, thereby ensuring the normal operation of the airport.

[0105] In a specific embodiment, assume that there are several key path points in a low-density area, namely A, B, and C, and their snow thickness and ground friction are (1 cm, 0.2), (3 cm, 0.4), and (2 cm, 0.3), respectively. According to the calculation formula of the path optimization weight, W_A = 0.3, W_B = 0.7, and W_C = 0.5. The initial speed V_0 = 5 km / h, the adjustment coefficient α = 0.5, then the adjusted speeds are V_A = 5 × (1-0.5 × 0.3) = 4.25 km / h, V_B = 5 × (1-0.5 × 0.7) = 3.25 km / h, and V_C = 5 × (1-0.5 × 0.5) = 3.75 km / h. Since W_B>W_0 = 0.5, the dwell time is increased at the key path point B, and the specific dwell time is T_B = 1 × (0.7-0.5) = 0.2 minutes.

[0106] The above process demonstrates how to improve the de-icing effect of airport de-icing vehicles in low-density areas through a systematic approach, ensuring the normal operation of the airport under adverse weather conditions.

[0107] Next, refer to Figure 8 , describing the steps of calculating the path optimization weight of each key path point of the present invention.

[0108] S801: De-icing effectiveness data needs to be collected in low-density areas. Specifically, this means collecting de-icing effectiveness information for de-icing vehicles in areas of the airport where there is less traffic or less frequent flights. This data includes, but is not limited to, temperature, humidity, de-icing agent type, amount used, and actual de-icing time.

[0109] S802: Analyze the collected data to determine the importance of each path point. Through data analysis methods, such as statistical analysis, machine learning models, etc., evaluate the deicing effect and demand situation at each key path point, so as to determine its importance in the entire path planning.

[0110] S803: Combine deicing effect data and path density to calculate path optimization weights. This step combines the deicing effect with the path density to generate a weight value that reflects the overall optimization requirements of each key path point. The weight value is calculated using the formula S = α × D_i + β × E_i, where S is the comprehensive score, D_i represents the coverage density of key path point i, and E_i represents the deicing effect of the path point. α and β are weight coefficients used to adjust the relative importance between the two. Normally, 0 < α, β < 1, and α + β = 1. The optimal ratio depends on the actual needs and experimental results in a specific environment. The formula is designed to balance the relationship between path density and deicing effect to ensure that work efficiency can be effectively improved while maintaining good deicing quality.

[0111] S804: If the comprehensive score S of the key path point i exceeds the set threshold γ, then its path optimization weight W_i will be increased. The threshold γ is a standard predefined according to the actual situation. When the comprehensive score of a key path point is higher than this standard, it means that it has a greater impact on the overall deicing process, so it needs to be given a higher weight to ensure that it is given priority when planning the route. The specific strategy for increasing the weight can be adjusted according to the actual operation flexibility to achieve the best optimization goal.

[0112] In one embodiment, it is assumed that the area near the end of a runway at an airport is a low-density area, but there is still a high demand for deicing in the case of heavy snowfall in winter. After the data of the deicing vehicle operating in this area is recorded and analyzed in detail, it is found that although there are few vehicles and pedestrians at this location on weekdays, due to its special geographical location, its deicing effect directly affects the safe takeoff and landing of flights. Therefore, when calculating the S value, the proportion of β is appropriately increased to emphasize the importance of deicing quality. If the S value exceeds γ, the proportion of this location in the global route optimization is increased to ensure that the area can receive timely and effective deicing services.

[0113] In addition, in the present invention, calculating the path optimization weight of each key path point also includes calculating the optimization weight factor of each path point based on the effect index, where the optimization weight factor W_i of path point i = (1+k×ΔE), ΔE is the effect improvement of the path point, and k represents the adjustment coefficient.

[0114] First, identify each critical path point. These critical path points are important nodes in the de-icing operation, such as the various wing surfaces and fuselage of the aircraft that need to be processed first. Specifically, the airport's ground control center can dynamically adjust these critical path points through real-time monitoring data.

[0115] Secondly, define the effect improvement ΔE. This parameter is used to measure the degree of improvement of the effect of a certain path point after a specific operation (such as spraying deicing fluid) is performed on the point relative to when the operation is not performed. Usually, the value range of ΔE is [0,1], where 0 indicates no improvement and 1 indicates maximum improvement. In one embodiment, ΔE can be calculated by comparing the actual deicing effect with the theoretical value, for example, by detecting the change in ice thickness after deicing through an image analysis algorithm.

[0116] Next, set the adjustment coefficient k. This parameter is used to adjust the sensitivity of the optimization weight factor Wi to the effect improvement. The value range of k is usually [0,2], 0 means that the weight does not change with the effect, and 2 means that the weight is very sensitive to the effect change. The optimal value depends on the specific application scenario and requirements. For example, in the deicing process, in order to ensure that each key part of the aircraft can achieve the optimal deicing effect, a higher k value, such as 1.5, may be selected.

[0117] Finally, calculate the optimization weight factor Wi of path point i. According to the formula Wi = (1 + k × ΔE), substitute ΔE and k into the calculation to get it. The setting of this formula ensures that Wi is always greater than or equal to 1. When ΔE is large, the value of Wi will also increase accordingly, thereby increasing the optimization weight of the path point. Such a setting helps to effectively allocate resources, so that key deicing paths receive more attention and optimization.

[0118] In a specific embodiment, assume that the de-icing effect improvement ΔE at a certain aircraft wing surface is 0.8, and the selected adjustment coefficient k is 1.5. Then, the optimization weight factor W_i at the wing surface is 1+1.5×0.8=2.2. This means that when planning the path, the wing surface will obtain a higher optimization weight than other path points, ensuring that the de-icing operation is more efficient and reliable. In this way, the efficiency and quality of the entire de-icing process can be significantly improved.

[0119] A deicing method for an airport deicing vehicle of the present invention comprises dynamically generating an optimized travel path based on the current working state of the deicing vehicle and the geographical information of the area to be deiced; controlling the deicing vehicle to move to a designated position according to the travel path; starting the deicing equipment to start deicing operations at the designated position; and monitoring the deicing effect in real time, adjusting the deicing parameters or replanning the travel path according to the deicing situation.

[0120] This technical solution effectively solves the problem of uneven coverage in traditional deicing methods. Through real-time data collection and analysis, the system can continuously optimize the travel path and deicing parameters to ensure that each area is treated evenly, avoiding omissions or repeated operations, and significantly improving the overall deicing efficiency and quality. In addition, this method also reduces unnecessary resource consumption and improves the economy and environmental friendliness of deicing work.

[0121] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A deicing method for an airport deicing vehicle, characterized in that: Including: Dynamically generate an optimized travel path based on the current working state of the de-icing vehicle and the geographical information of the area to be de-iced; Control the de-icing vehicle to move to the specified position according to the travel path; Start the de-icing equipment and begin the de-icing operation on the specified position; Monitor the de-icing effect in real time and adjust the de-icing parameters or re-plan the travel path according to the de-icing situation.

2. The deicing method for an airport deicing vehicle according to claim 1, characterized in that: The step of dynamically generating an optimized travel path based on the current working state of the de-icing vehicle and the geographical information of the area to be de-iced further includes: Obtain the working state data of the de-icing vehicle and the geographical information of the area to be de-iced; Analyze the density distribution of the coverage path based on the working state data and the geographical information of the area to be de-iced; Adjust the path generation algorithm to increase the path density in the low-density area; If the coverage density D of a certain area is less than the preset density threshold T, then preferentially adjust the path so that the area is covered multiple times, where D represents the area coverage density and T represents the preset density threshold.

3. The deicing method for an airport deicing vehicle according to claim 2, characterized in that: The step of analyzing the density distribution of the coverage path based on the working state data and the geographical information of the area to be de-iced further includes: Collect the coverage frequency of path points; Draw a coverage density distribution map; Identify high- and low-density areas based on the coverage density distribution map; The coverage density D_i of a certain point i is N_i / A. When D_i < k, mark this point as a low-density area, where N_i represents the number of coverage times of a certain point i, A represents the area of the region, D_i represents the coverage density of a certain point i, and k represents the preset minimum density standard.

4. The deicing method for an airport deicing vehicle according to claim 3, characterized in that: The step of identifying high- and low-density areas based on the coverage density distribution map further includes: Divide the area to be de-iced into multiple sub-regions; Calculate the average coverage density of each sub-region; Mark the sub-regions with an average coverage density lower than the threshold T as low-density sub-regions; The average coverage density D_j of a certain sub-region j is Σ(D_{ij}) / n. When D_j < k, mark this sub-region j as a low-density sub-region, where Σ(D_{ij}) represents the sum of the coverage densities of all points in the region, n represents the number of points in the sub-region, D_j represents the average coverage density of the sub-region j, and k represents the preset density threshold.

5. The deicing method for an airport deicing vehicle according to claim 2, characterized in that: The step of adjusting the path generation algorithm to increase the path density in the low-density area further includes: Add additional paths for the identified low-density areas; Optimize the path generation algorithm to reduce the repeated paths in the high-density area; Consider the de-icing time and efficiency and comprehensively adjust the path density; If the total length L_total of the optimized path is less than or equal to e times the original path length L_original, then the path optimization is considered effective, where L_total represents the total length of the optimized path, L_original represents the original path length, and e is a system predetermined threshold greater than 1.

6. The deicing method for an airport deicing vehicle according to claim 5, characterized in that: The step of adding additional paths for the identified low-density areas further includes: Determine the key points of the low-density area; Establish connection paths between the key points; Check the overlap degree of the new path and the existing path; If the ratio of the overlapping length S_cross of the new path and the existing path to the total length S_total of the new path r = S_cross / S_total is greater than f, the direction or position of the new path is adjusted to ensure effective coverage of the low-density area, where S_cross represents the overlapping length of the new path and the existing path, S_total represents the total length of the new path, r represents the overlapping ratio, and f is a predetermined threshold.

7. The deicing method for an airport deicing vehicle according to claim 5, characterized in that: The step of adding additional paths for the identified low-density area further comprises: Identify critical path points within low-density areas; Calculate the path optimization weight of each key path point; Adjust the driving speed of the de-icing vehicle according to the path optimization weight; If the path optimization weight W_i>W_0 of the critical path point i, the stay time T_i at this point is increased to improve the deicing effect, where W_i represents the path optimization weight of the critical path point i, W_0 represents the benchmark optimization weight, and T_i represents the stay time at the critical path point i.

8. The deicing method for an airport deicing vehicle according to claim 7, characterized in that: The step of calculating the path optimization weight of each key path point further includes: Collect data on de-icing effectiveness in low-density areas; Analyze data to determine the importance of each waypoint; Combine deicing effect data and path density to calculate path optimization weights; If the comprehensive score S = α × D_i + β × E_i of the coverage density D_i and the deicing effect E_i of the key path point i is greater than the threshold γ, the path optimization weight W_i of the key path point i is increased to ensure high-efficiency coverage, where α and β are trade-off coefficients.

9. The deicing method for an airport deicing vehicle according to claim 8, characterized in that: The calculation of the path optimization weight of each key path point also includes calculating the optimization weight factor of each path point according to the effect index, wherein the optimization weight factor W_i of path point i = (1+k×ΔE), ΔE is the effect improvement of the path point, and k represents the adjustment coefficient.

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