Intelligent snow removal planning method and system for large road snow removal vehicles

Abstract

The present invention discloses an intelligent snow removal planning method and system for large-scale road surface snow removal vehicles. Before the snow removal vehicles start to remove snow, the snow removal gear, driving speed and whether to perform formation operation of a single or multiple snow removal vehicles are planned according to the actual snow thickness and actual road parameters, and the time and fuel consumption required to complete the snow removal task are calculated. With the minimum operation time or the lowest economic cost as the optimization goal, an optimization algorithm is applied to realize intelligent snow removal planning, ensure that the snow removal vehicles complete the snow removal task efficiently, economically and effectively, reduce the use cost of the snow removal vehicles and improve the road surface utilization rate.

Description

Technical Field

[0001] The present invention belongs to the field of large-scale road snow removal vehicles, and in particular relates to an intelligent snow removal planning method and system for large-scale road snow removal vehicles. Background Art

[0002] At present, when snow removal is being carried out, snow removal is still carried out in the form of snow removal assignment scheme or snow removal operation plan. In this method, the snow removal commander temporarily adjusts the snow removal operation mode according to the actual snow removal situation on site to achieve the purpose of increasing snow removal speed and improving resource utilization. However, this traditional snow removal method is easily affected by human factors, such as unreasonable snow removal planning and untimely adjustment of the plan, which can easily lead to low snow removal efficiency and unsatisfactory snow removal effect. With the continuous growth of the number of airports put into operation in my country, de-icing and snow removal operations have become the key guarantee work to ensure the normal operation of airports in winter. Safely, quickly, high-quality and efficient completion of de-icing and snow removal guarantee tasks is the key research direction of various airports and snow removal equipment design companies at home and abroad.

[0003] At present, with the continuous supplementation and updating of the new generation of snow removal equipment, the continuous expansion of the snow removal support team, and the gradual improvement of snow removal efficiency, the snow removal command method that relies solely on walkie-talkies or telephone communications is no longer suitable for large-scale rapid snow removal operations. This traditional snow removal command mode not only fails to track the progress of snow removal operations in a timely manner and accurately plan snow removal plans, but also increases the difficulty of snow removal command and dispatch, greatly affecting the overall efficiency of snow removal work. At the same time, it is also difficult to form a complete snow removal record, which brings great inconvenience to the later snow removal data statistics. Therefore, establishing a new intelligent snow removal planning method, improving the efficiency of snow removal and ice removal operations, saving resource consumption, and shortening flight delays are important means to ensure the normal operation of airports in winter.

[0004] The existing snowplows in China are divided into mechanical snowplows and turbojet snowplows, but these snowplows are not equipped with intelligent snowplow planning methods and technologies. The technical research related to snowplow planning mainly includes:

[0005] 1. Research on the assignment problem of snow removal equipment group. This type of problem mainly takes traditional snow removal vehicles and other snow removal equipment groups as the research objects. According to the snow removal operation priority and snow removal operation capacity of each equipment in the equipment group, the assignment scheme of each snow removal equipment in the snow removal equipment group is designed. However, this method is not suitable for large road surface snow removal vehicles.

[0006] 2. To optimize the snow removal management model in a certain area. This method is mainly based on the specific snow removal work practice, combined with the experience and theory of snow removal management organizational models at home and abroad, and studies are conducted from the perspectives of snow removal organizational structure design, management methods, and management processes, striving to propose a relatively reasonable and applicable basic theory of snow removal management.

[0007] 2-1. This type of method is mainly used to assign snow removal tasks to snow removal equipment groups, and is suitable for the assignment of snow pushers, snow throwers, and snow spreaders. When snow removal operations are performed using turbofan engine hot-blow snow removal vehicles, this type of snow removal equipment group assignment method is not of reference value.

[0008] 2-2. This method is a study on snow removal command and management system, and its purpose is to improve snow removal efficiency by continuously improving the snow removal command and management model. However, this type of research does not involve the snow removal operation capability of snow removal equipment, and this type of research is also a snow removal management method for a group of snow removal equipment, which is not suitable for scenarios where a single device is used for snow removal operations.

[0009] Based on this, the technical problems to be solved by the present invention are: 1) construct a snow removal width data set under different snow removal gears, different driving speeds, and different snowfall thicknesses; 2) design an intelligent snow removal planning method that can be applied to large-scale road snow removal vehicles; 3) provide an intelligent snow removal planning device for snow removal vehicles. Summary of the invention

[0010] In order to overcome the technical defects in the background technology, the present invention aims to provide an intelligent snow removal planning method and system for large-scale road snowplows. Before the snowplow starts snow removal, the snow removal gear, driving speed and whether to perform formation operation of a single snowplow are planned according to the thickness of the snow accumulation and the actual road parameters, and the time and fuel consumption required for the snowplow to complete the snow removal task are calculated to ensure that the snowplow completes the snow removal task efficiently and safely, thereby reducing the use cost of the snowplow and improving the economic benefits.

[0011] In order to solve the technical problem, the technical solution of the present invention is:

[0012] An intelligent snow removal planning method for a large road surface snow removal vehicle, the method comprising:

[0013] S1: Determine the snow removal gear of the snow removal vehicle;

[0014] S2: Determine the effective snow removal speed range of the snow removal vehicle;

[0015] S3: According to different snow removal gears and different effective snow removal speeds, determine the snow removal width of the snow removal vehicle under different snowfall thicknesses and construct a snow removal width dataset;

[0016] S4: Determine the length and width parameters of the airport runway to be cleared;

[0017] S5: Determine the maximum permissible operation time for the airport runway to be cleared;

[0018] S6: Using an intelligent target optimization algorithm, the snow removal width data set is processed according to the thickness of the snow to be removed, the length and width parameters of the airport road to be removed, and the maximum allowable operating time of the airport road to be removed, so as to obtain the optimal strategy for snow removal planning and basic information for snow removal planning.

[0019] Furthermore, the optimal strategy for snow removal planning includes: optimal gear strategy for snow removal vehicles and optimal driving speed strategy, and the basic information for snow removal planning includes: snow removal time information, fuel consumption information and formation information.

[0020] Further, the S1 specifically includes:

[0021] Based on the engine rotor speed, the snow removal gear of the snowplow is set to several different gears from low to high according to the engine operating conditions, which correspond to different output powers of the snowplow.

[0022] Further, the S2 specifically includes:

[0023] The effective snow removal speed range of the snowplow is determined based on the dynamic snow removal test results of the snowplow. As the effective snow removal speed of the snowplow increases, the snow blowing width of the snowplow gradually decreases. The effective snow removal speed of the snowplow is 5Km / h-40Km / h.

[0024] Furthermore, according to the actual conditions of the corresponding runways at different airports, the length and width parameters of the runways to be removed are determined.

[0025] Furthermore, a snow removal width dataset is constructed based on the snow removal widths under different snow removal gears, different effective snow removal speeds and different snow thicknesses of the snow removal vehicle; each row of data in the snow removal width dataset contains four parameters, namely: the snow removal gear parameter of the snow removal vehicle, the effective snow removal speed parameter of the snow removal vehicle, the snow thickness parameter and the snow removal width parameter.

[0026] Furthermore, the maximum permissible operating time is: the runway idle time set by the airport according to the flight travel plan, and the permissible operating time of the runway to be cleared is collected in the snow removal command system.

[0027] Further, the S6 specifically includes:

[0028] S61: performing an optimal search for all data in the snow removal width data set according to the thickness of the snow to be removed and the width parameter of the airport road to be removed, searching for a corresponding relationship data set of all different snow removal gears that can be used for snow removal by snow removal vehicles under the condition of the thickness of the snow to be removed, different effective snow removal speeds, and snow removal widths under the snow thickness, and sorting the corresponding relationship data set from small to large according to the snow removal gears;

[0029] The purpose is to give priority to using small gears for snow removal to protect the engine. Time and economic cost are the most important constraints of the intelligent target optimization algorithm. The intelligent snow removal planning method should first meet the operating time requirements allowed by the airport;

[0030] S62: Based on the time constraint, the corresponding relationship data set is looped and calculated. According to the length and width parameters of the airport runway to be cleared, whether the planned operation time of the current data is less than the maximum allowable operation time of the airport runway to be cleared is calculated. When the planned operation time is less than the maximum allowable operation time of the airport runway to be cleared, the loop is exited. The fuel consumption of the snow removal work is calculated by combining the fuel consumption under different standard gears with the snow removal time, and finally the optimal strategy for snow removal planning and basic information of snow removal planning are output.

[0031] S63: When the planned operation time is greater than the maximum allowable operation time of the airport runway to be cleared, automatically search downward to find a higher output gear and a faster effective snow removal speed that meet the time requirement, then calculate the final fuel consumption, and output the optimal strategy and basic information of snow removal planning;

[0032] S64: When the planned operation time calculated by the snowplow at all gears and all effective snow removal speeds is greater than the maximum allowable operation time of the airport's road to be cleared, the algorithm will recommend snow removal in a snow removal formation mode; at this time, the snow removal time of the snowplow formation to complete the snow removal operation will be approximately equal to 1 / 2 of the single-vehicle operation, and the snow removal task can be completed more quickly; the snow removal gear, driving speed, snow removal time and fuel consumption parameters in the double formation case are recalculated using the corresponding relationship data set in step S62. When the snow removal time meets the maximum allowable operation time of the airport's road to be cleared, the loop is exited and the optimal strategy for snow removal planning and basic information for snow removal planning are output;

[0033] S65: When snowplows are working in a snowplow formation and are unable to complete the snowplowing operation within the specified time, or the corresponding snow thickness cannot be found in the effective snowplowing width, it indicates that the snow on the snowplowing road at the current airport is too thick to complete the snowplowing operation in a short time. It is recommended to use a snowplow equipment group to carry out the snowplowing operation.

[0034] Furthermore, the processing of S6 using the intelligent target optimization algorithm specifically includes: first, constructing a snow removal width data set X = {x1; x2; ...; xn}∈R n×4 There are n types of samples in the data set, each of which contains 4 parameters, namely, snow removal gear, driving speed, snow thickness and snow removal width obtained in the experiment. The i-th type of sample can be recorded as X i ={x i1 , x i2 , x i3 , x i4}, i = 1, 2, ..., n;

[0035] Then, it is determined whether the snow thickness is too thick. If the input snow thickness is too thick (b = 0), a group of snow removal equipment can be used for joint operation or the snow removal operation time can be extended. If the snow thickness is within the normal range (b = 1), the snow thickness d is used to search all the data in the database, and all the snow removal width data that meet the current snow thickness are recorded as

[0036]

[0037] Sort the data subset X1 in order of speed from small to large. The objective function of the algorithm is defined as:

[0038]

[0039] Among them, V i is the speed of the snowplow, L j The length of the snow-clearing road at the airport;

[0040] Repeat formula (2) to solve the objective function and determine the relationship between the objective function and the airport's allowed operating time T. The expressions of the snow removal mode are shown in formula (3). When the objective function meets the time requirement, the loop is exited. When the time requirement is not met, formula (3) is executed repeatedly. Where t1 is the time difference between the two snow removal vehicles when the snow removal formation is used for snow removal.

[0041]

[0042] According to the corresponding snow removal mode, calculate the fuel consumption O of the snow removal vehicle when completing the snow removal operation in each mode. Fuel is the fuel consumption per unit time when the snow removal vehicle works in different snow removal gears. According to the different snow removal gears, fuel has four different fuel consumptions. Fuel1 is the fuel consumption when the engine starts to the working state. When F=1, the fuel consumption is O1, when F=2, the fuel consumption is O2, and when F=3, the fuel consumption is divided into two cases. O3 corresponds to the fuel consumption of a single formation, and O4 corresponds to the fuel consumption of a double formation.

[0043]

[0044] Finally, the optimal strategy for snow removal planning and basic information of snow removal planning are output.

[0045] An intelligent snow removal planning system for a large-scale road snow removal vehicle, the system comprising:

[0046] An input bar module and an output bar module. The input bar module is used to input information, including an airport permitted operation time selection button, a field and track parameter selection knob, and a snow thickness parameter selection cursor. The output bar module is used to output and display results, including five parameters: snow removal gear, driving speed, formation, snow removal time, and fuel consumption.

[0047] one or more processors;

[0048] A memory for storing one or more programs;

[0049] When the one or more programs are executed by the one or more processors, the one or more processors execute any of the above-mentioned intelligent snow removal planning methods for large-scale road snow removal vehicles.

[0050] Compared with the prior art, the advantages of the present invention are:

[0051] An intelligent snow removal planning method and system for large-scale road snowplows. The purpose of the present invention is to provide an applicable intelligent snow removal planning method for large-scale road snowplows. The method aims to solve the problems of slow snow removal speed and high economic resource cost when snowplows are still carried out in the traditional snow removal command mode without intelligent snow removal planning method during snow removal. The method focuses on solving the problems of poor snow removal effect and low snow removal utilization rate caused by the mismatch between the snow removal gear and the driving speed of a single or multiple snowplows under different field roads, different snow thicknesses and different operation time window restrictions. The method can use field road parameters, snow thickness, time limit window and other parameters to intelligently plan the collaborative operation mode of snowplows, command the snow removal gear, driving speed and snow removal formation of a single or multiple snowplows, and output the fuel consumption and snow removal time for completing the snow removal task. With the purpose of operation guidance planning with the shortest snow removal time and the lowest economic cost, the operating cost of snowplows is reduced and the utilization rate of road surfaces is improved.

[0052] Taking the turbofan engine hot-blowing snowplow as the object, the snow removal width of the snowplow at different snow removal gears and different driving speeds on the field roads with different snow thickness is first collected, and the snow removal width data set is constructed based on this data. Then, an intelligent snow removal planning algorithm for hot-blowing snowplow is designed. The algorithm can use parameters such as snow thickness, field road parameters, and airport allowed operation time, combined with the effective snow removal width data set to calculate the optimal snow removal gear, driving speed, snow removal time, fuel consumption, and whether to form a team for the snowplow. This ensures that the snowplow can perform snow removal operations quickly and effectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 , a specific flow chart of the present invention;

[0054] Figure 2 , a diagram of the snow removal planning calculation method of the present invention;

[0055] Figure 3 , the system interface of the intelligent snow removal planning module of the present invention. DETAILED DESCRIPTION

[0056] The specific implementation mode of the present invention is described below in conjunction with embodiments:

[0057] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to match the contents disclosed in the specification so that people familiar with this technology can understand and read them, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0058] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" cited in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0059] Embodiment 1:

[0060] As an intelligent and efficient snow removal operation implementation method for snowplows, the intelligent snow removal planning method is very different from the research methods of traditional snowplow snow removal planning. This method is based on the actual snow removal test measurement results, and is based on the snow removal gear, driving speed and snow thickness corresponding to the snow removal width during the actual snow removal test. Under the premise of considering resource consumption, time constraints and ensuring a certain service level, combined with parameters such as snow thickness, runway parameters, airport allowed operating time and engine fuel consumption, it provides the snowplow with reasonable snow removal gear, driving speed, snow removal time and fuel consumption information, ensuring the shortest snow removal time, highest snow removal efficiency and lowest fuel consumption.

[0061] This embodiment solves the problem that snowplows still follow the traditional snowplow command mode during the snowplow process and there is no intelligent snowplow planning method. An intelligent snowplow planning method and system for large-scale road snowplows is proposed. The intelligent target optimization algorithm is used to calculate the snowplow gear, driving speed, snowplow time, fuel consumption and formation parameters that are in line with the current snow conditions and the current road, such as Figure 1 shown.

[0062] Step 1: Determine the snow removal gear of the snowplow: Determine the reasonable snow removal gear of the snowplow according to the snow removal effect measured by the snow removal test of the turbofan engine hot-blow snowplow.

[0063] Step 2: Determine the snow removal speed of the snowplow: Based on the dynamic snow removal test results of the snowplow, determine the snow removal speed that can meet the snow removal requirements of the airport runway.

[0064] Step 3: Construct a snow removal width dataset: Determine the snow removal width of the snowplow at different snow thicknesses under different snow removal gears and different driving speeds.

[0065] Step 4: Determine the runway parameters: Determine the length and width parameters of the runway to be cleared.

[0066] Step 5: Determine the thickness of snow on the road: Determine the thickness of snow on the road to be cleared.

[0067] Step 6: Determine the maximum allowable operating time for the field road: Determine the maximum allowable operating time for the airport field road to be cleared.

[0068] Step 7: Use the intelligent target optimization algorithm to calculate the final output result: calculate the operating parameters of the snowplow under the current field, current snow thickness and current allowed operating time, including: snowplow gear position, driving speed, snow removal time, fuel consumption and formation status.

[0069] Step 8: Develop an intelligent snow removal planning module: Develop an intelligent snow removal planning module in the on-board health management system of the snow removal vehicle.

[0070] further,

[0071] Step 1 is as follows:

[0072] Determine the snow-removing gear position of the snowplow: Based on the engine high-pressure compressor speed and combined with the test results of the snow-removal test, the snow-removing gear position of the snowplow is set to 72% (first gear), 82% (second gear), 86% (third gear) and 88% (fourth gear) according to the engine high-pressure compressor speed.

[0073] Step 2 is as follows:

[0074] Determine the driving speed of the snowplow: According to the results of the dynamic snow removal test of the snowplow, it can be determined that as the driving speed of the snowplow increases, the snow blowing width of the snowplow gradually decreases. The effective snow removal speed of the snowplow can be taken as 5km~40km / h.

[0075] Step 3 is as follows:

[0076] Constructing a snow removal width dataset: The existing runway widths of domestic airports are mainly 45 meters, 50 meters, 60 meters and 75 meters. The snow removal width dataset is constructed based on the snow removal width under different snow removal gears, different driving speeds and different snow thickness of the snowplow. Each row of data in the dataset contains 4 parameters, namely the snow removal gear of the snowplow, the driving speed of the snowplow, the snow thickness and the snow removal width. The snow removal gear is divided into four gears, the driving speed of the snowplow is 5Km~40Km / h, the snow thickness that can be removed is 0~100mm, and the snow removal width is the actual test result, which is the snow removal width measured in the actual test experiment by different combinations of the first three parameters. When the snow thickness is too thick, a larger gear can be used for snow removal.

[0077] Step 4 is as follows:

[0078] Determine the runway parameters: Each airport has a certain number of runs and runway types, and the length and width of each runway are constant. Determine the length and width of the runway to be cleared based on the actual conditions of the runways at different airports.

[0079] Step 5 is as follows:

[0080] Determine the snow thickness on the runway: The snow removal command system receives the snow thickness collected by the airport runway sensor, which should be the snow thickness on the runway that the snowplow is going to clear.

[0081] Step 6 is as follows:

[0082] Determine the maximum operating time allowed for the runway: The maximum operating time allowed is the runway idle time set by the airport based on flight travel plans. The allowed operating time for the runways to be cleared is collected in the snow removal command system.

[0083] Step 7 is as follows:

[0084] Intelligent target optimization algorithm calculation: The specific calculation steps of the algorithm are as follows Figure 2 As shown, first, the algorithm performs an optimal search for all the data in the database according to the input snow thickness, searches for all corresponding relationship data sets of different snow removal gears, different effective snow removal speeds and snow removal widths under the condition of the snow thickness to be removed, and sorts the data sets from small to large according to the snow removal gears;

[0085] The purpose is to give priority to using low gear for snow removal to protect the engine. Time and economic cost are the most important constraints of the intelligent target optimization algorithm. The intelligent snow removal planning method should first meet the operating time requirements allowed by the airport.

[0086] Based on the time constraint, all searched data are calculated in a loop. The length and width parameters of the airport runway are used to calculate whether the planned operation time of the current data is less than the maximum allowable operation time of the airport runway. When the snow removal time is less than the airport's allowable operation time, the loop is exited. The fuel consumption under different standard gears is combined with the snow removal time to calculate the fuel consumption of the snow removal work. Finally, the snow removal gear, driving speed, snow removal time, fuel consumption, and single formation operation information are output.

[0087] When the planned operation time is greater than the airport's allowed operation time: the algorithm will automatically optimize downward to find a larger output gear and a faster driving speed that meets the time requirements, and then calculate the final fuel consumption, and output information such as snow removal gear, driving speed, snow removal time, fuel consumption, and single formation operation.

[0088] When the snow removal time calculated by the snowplow at any gear and any driving speed is greater than the airport's allowed operating time, the algorithm will recommend snow removal in the snow removal formation mode. At this time, the snow removal time of the snowplow formation to complete the snow removal operation will be approximately equal to 1 / 2 of the single-vehicle operation, and the snow removal task can be completed more quickly. The data searched by S2 in step 7 is used to recalculate the parameters such as the snow removal gear, driving speed, snow removal time, and fuel consumption in the case of double formations. When the snow removal time meets the maximum allowed operating time of the airport's waiting field, the loop is jumped out and the snow removal gear, driving speed, snow removal time, fuel consumption, and information such as the opposite formation operation are output.

[0089] When the snow removal vehicles are working in the snow removal formation mode and cannot complete the snow removal operation within the specified time, or the corresponding snow thickness cannot be found in the effective snow removal width, it means that the current airport snow removal area is too small.

[0090] The snow is too thick to complete the snow removal task in a short time. It is recommended to use snow removal equipment to carry out snow removal operations.

[0091] Further, the algorithm is described as follows:

[0092] First, construct a snow removal width dataset X = {x1; x2; ...; xn}∈R n×4 There are n types of samples in the data set, each of which contains 4 parameters, namely, snow removal gear, driving speed, snow thickness and snow removal width obtained in the experiment. The i-th type of sample can be recorded as X i ={x i1 , x i2 , x i3 , x i4}, i=1, 2, ..., n.

[0093] Then, it is determined whether the snow thickness is too thick. If the input snow thickness is too thick (b = 0), a group of snow removal equipment can be used for joint operation or the snow removal operation time can be extended. If the snow thickness is within the normal range (b = 1), the snow thickness d is used to search all the data in the database, and all the snow removal width data that meet the current snow thickness are recorded as

[0094]

[0095] Sort the data subset X1 in order of speed from small to large. The objective function of the algorithm is defined as:

[0096]

[0097] Among them, V i is the speed of the snowplow, L j is the length of the snow removal road at the airport.

[0098] Repeat step (2) to solve the objective function and determine the relationship between the objective function and the airport's allowed operating time T. The expressions of the snow removal mode are shown in formula (3). When the objective function meets the time requirement, the loop is exited. When the time requirement is not met, the formula (3) is executed repeatedly, where t1 is the time difference between the two snow removal vehicles when the snow removal formation is used for snow removal.

[0099]

[0100] According to the corresponding snow removal mode, calculate the fuel consumption of the snowplow in each mode when completing the snow removal operation. Fuel is the fuel consumption per unit time when the snowplow works in different snow removal gears. According to the different snow removal gears, fue1 has four different fuel consumptions. Fuel1 is the fuel consumption when the engine starts to the working state. When F=1, the fuel consumption is O1, when F=2, the fuel consumption is O2, and when F=3, the fuel consumption is divided into two cases, O3 corresponds to the fuel consumption of a single formation, and O4 corresponds to the fuel consumption of a double formation.

[0101]

[0102] Finally, the snow removal gear, driving speed, snow removal time, fuel consumption and formation status are output.

[0103] Step 8 is as follows:

[0104] S1: The intelligent snow removal planning module developed in the on-board health management system of the snow removal vehicle is as follows: Figure 3 As shown, the algorithm shown in step 7 is embedded in the module. The intelligent snow removal planning module system can be divided into an input bar and an output bar. The input bar is located in the upper part of the module, and its input information includes the airport allowed operation time selection button, the field and track parameter selection knob, and the snow thickness parameter selection cursor. The output bar is located in the lower part of the module, and the output results mainly include five parameters: snow removal gear, driving speed, formation, snow removal time and fuel consumption. After selecting the input parameters in the system input bar, the system will calculate and output the optimal snow removal gear, driving speed and other parameters according to the above-mentioned intelligent target optimization algorithm for reference by the snow removal driver.

[0105] Different parameters were input into the system to test its output results. The length of track 1 was 3800 meters and the width was 45 meters. The length of track 2 was 3000 meters and the width was 45 meters. The time difference between the two snowplows in snow removal operation was 0.5 minutes. The hourly fuel consumption of the four snow removal gears of this type of snowplow was 1350L, 1960L, 2792L and 3258L respectively. The test results are shown in Table 1. It can be seen from Table 1 that the method proposed in the present invention can conveniently, quickly and intelligently plan the snow removal gear, driving speed, snow removal time, fuel consumption and formation of the snowplow, and can provide a reliable intelligent snow removal planning method for snow removal operations of large-scale road snowplows.

[0106] Table 1 Test results of intelligent snow removal planning system

[0107]

[0108]

[0109] It can be understood that compared with the prior art, this embodiment has at least the following key technologies and beneficial effects.

[0110] Key technical point 1: A method for establishing a data set based on snow removal gear, driving speed, snow thickness and snow removal width is proposed.

[0111] Beneficial effect 1: It is difficult to determine the snow removal gear and driving speed of the snow removal vehicle under different snow thickness conditions, which may easily lead to problems such as too large snow removal gear, fast snow removal speed, and unsatisfactory snow removal effect, which may eventually cause airport flight delays. The snow removal vehicle data set construction method provided by the present invention includes four parameters, namely snow removal gear, driving speed, snow thickness and effective snow removal width, which can provide powerful calculation conditions for the intelligent snow removal planning algorithm.

[0112] Key technical point 2: Propose an intelligent snow removal planning algorithm for large road snow removal vehicles.

[0113] Beneficial effect 2: Breakthrough in the current research status of intelligent snow removal planning, which is all about equipment group assignment and management mode optimization. The algorithm can accurately obtain the snow removal gear, driving speed, snow removal time, fuel consumption and formation status of this type of snow removal vehicle according to the snow thickness, runway parameters and allowed operating time, ensuring that the snow removal vehicle performs snow removal operations at the appropriate gear and speed, and realizing efficient and full utilization of engine performance.

[0114] Key technical point 3: Design an intelligent snow removal planning device.

[0115] Beneficial effect 3: An intelligent snow removal planning system for vehicle-mounted computers has been developed. After selecting the airport's permitted operating time, airport runway parameters, and snow thickness conditions, the system can output the optimal snow removal vehicle operating parameters.

[0116] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

[0117] Many other changes and modifications may be made without departing from the concept and scope of the present invention.It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. An intelligent snow removal planning method for a large road snow removal vehicle, characterized in that: The method comprises: S1: Determine the snow removal gear of the snow removal vehicle; S2: Determine the effective snow removal speed range of the snow removal vehicle; S3: According to different snow removal gears and different effective snow removal speeds, determine the snow removal width of the snow removal vehicle under different snowfall thicknesses and construct a snow removal width dataset; S4: Determine the length and width parameters of the airport runway to be cleared; S5: Determine the maximum permissible operation time for the airport runway to be cleared; S6: using an intelligent target optimization algorithm to process the snow removal width data set according to the thickness of snow to be removed, the length and width parameters of the airport road to be removed, and the maximum allowable operation time of the airport road to be removed, to obtain the optimal strategy for snow removal planning and basic information for snow removal planning; The process and formula for using the intelligent target optimization algorithm to process data to obtain the best strategy and basic information are as follows: First, construct a snow removal width dataset X = {x1; x2; ...; xn}∈R n×4 There are n types of samples in the data set, each of which contains 4 parameters, namely, snow removal gear, driving speed, snow thickness and snow removal width obtained in the experiment. The i-th type of sample can be recorded as X i ={x i1 , x i2 , x i3 , x i4 }, i = 1, 2, ..., n; Then, it is determined whether the snow thickness is too thick. If the input snow thickness is too thick (b = 0), a group of snow removal equipment can be used for joint operation or the snow removal operation time can be extended. If the snow thickness is within the normal range (b = 1), the snow thickness d is used to search all the data in the database, and all the snow removal width data that meet the current snow thickness are recorded as n1<n,X1∈X; Sort the data subset X1 in order of speed from small to large. The objective function of the algorithm is defined as: Among them, V i is the speed of the snowplow, L j is the length of the snow removal road at the airport. Repeat step (2) to solve the objective function and determine the relationship between the objective function and the airport's allowed operating time T. The expressions of the snow removal mode are shown in formula (3). When the objective function meets the time requirement, the loop is exited. When the time requirement is not met, the formula (3) is executed repeatedly, where t1 is the time difference between the two snow removal vehicles when the snow removal formation is used for snow removal. According to the corresponding snow removal mode, calculate the fuel consumption of the snowplow in each mode when completing the snow removal operation. Fuel is the fuel consumption per unit time when the snowplow works in different snow removal gears. According to the different snow removal gears, fuel has four different fuel consumptions. Fuel1 is the fuel consumption when the engine starts to the working state. When F=1, the fuel consumption is O1, when F=2, the fuel consumption is O2, and when F=3, the fuel consumption is divided into two cases. O3 corresponds to the fuel consumption of a single formation, and O4 corresponds to the fuel consumption of a double formation. Finally, the snow removal gear, driving speed, snow removal time, fuel consumption and formation status are output.

2. The intelligent snow removal planning method for a large-scale road snow removal vehicle according to claim 1 is characterized in that: The optimal strategy for snow removal planning includes: the optimal gear strategy for snow removal vehicles and the optimal driving speed strategy, and the basic information for snow removal planning includes: snow removal time information, fuel consumption information and formation information.

3. The intelligent snow removal planning method for a large road snow removal vehicle according to claim 1 is characterized in that: The S1 specifically includes: Based on the engine rotor speed, the snow removal gear of the snowplow is set to several different gears from low to high according to the engine operating conditions, which correspond to different output powers of the snowplow.

4. The intelligent snow removal planning method for a large road snow removal vehicle according to claim 1 is characterized in that: The S2 specifically includes: The effective snow removal speed range of the snowplow is determined based on the dynamic snow removal test results of the snowplow. As the effective snow removal speed of the snowplow increases, the snow blowing width of the snowplow gradually decreases. The effective snow removal speed of the snowplow is 5Km / h-40Km / h.

5. The intelligent snow removal planning method for a large-scale road snow removal vehicle according to claim 1 is characterized in that: According to the actual conditions of the corresponding runways at different airports, the length and width parameters of the runways to be removed are determined.

6. The intelligent snow removal planning method for a large road snow removal vehicle according to claim 1 is characterized in that: A snow removal width dataset is constructed based on different snow removal gears, different effective snow removal speeds and snow removal widths under different snow thicknesses of snowplows. Each row of data in the snow removal width dataset contains four parameters, namely: snow removal gear parameter of the snowplow, effective snow removal speed parameter of the snowplow, snow thickness parameter and snow removal width parameter.

7. The intelligent snow removal planning method for a large road snow removal vehicle according to claim 1 is characterized in that: The maximum permissible operating time is: the runway idle time set by the airport based on flight travel plans, and the permissible operating time of the runways to be cleared is collected in the snow removal command system.

8. The intelligent snow removal planning method for a large-scale road snow removal vehicle according to claim 1 is characterized in that: The S6 specifically includes: S61: performing an optimal search for all data in the snow removal width data set according to the thickness of the snow to be removed and the width parameter of the airport road to be removed, searching for a corresponding relationship data set of all different snow removal gears that can be used for snow removal by snow removal vehicles under the condition of the thickness of the snow to be removed, different effective snow removal speeds, and snow removal widths under the snow thickness, and sorting the corresponding relationship data set from small to large according to the snow removal gears; S62: Based on the time constraint, the corresponding relationship data set is looped and calculated. According to the length and width parameters of the airport runway to be cleared, whether the planned operation time of the current data is less than the maximum allowable operation time of the airport runway to be cleared is calculated. When the planned operation time is less than the maximum allowable operation time of the airport runway to be cleared, the loop is exited. The fuel consumption of the snow removal work is calculated by combining the fuel consumption under different standard gears with the snow removal time, and finally the optimal strategy for snow removal planning and basic information of snow removal planning are output. S63: When the planned operation time is greater than the maximum allowable operation time of the airport runway to be cleared, automatically search downward to find a higher output gear and a faster effective snow removal speed that meet the time requirement, then calculate the final fuel consumption, and output the optimal strategy and basic information of snow removal planning; S64: When the planned operation time calculated by the snowplow at all gears and all effective snow removal speeds is greater than the maximum allowable operation time of the airport's road to be cleared, the algorithm will recommend snow removal in a snow removal formation mode; at this time, the snow removal time of the snowplow formation to complete the snow removal operation will be approximately equal to 1 / 2 of the single-vehicle operation, and the snow removal task can be completed more quickly; the snow removal gear, driving speed, snow removal time and fuel consumption parameters in the double formation case are recalculated using the corresponding relationship data set in step S62. When the snow removal time meets the maximum allowable operation time of the airport's road to be cleared, the loop is exited and the optimal strategy for snow removal planning and basic information for snow removal planning are output; S65: When snowplows are working in a snowplow formation and are unable to complete the snowplowing operation within the specified time, or the corresponding snow thickness cannot be found in the effective snowplowing width, it indicates that the snow on the snowplowing road at the current airport is too thick to complete the snowplowing operation in a short time. It is recommended to use a snowplow equipment group to carry out the snowplowing operation.

9. The intelligent snow removal planning method for a large-scale road snow removal vehicle according to claim 1, characterized in that: The processing of S6 using the intelligent target optimization algorithm specifically includes: constructing a snow removal width data set X = {x1; x2; ...; xn}∈R n×4 There are n types of samples in the data set, each of which contains 4 parameters, namely, snow removal gear, driving speed, snow thickness and snow removal width obtained in the experiment. The i-th type of sample can be recorded as X i ={x i1 , x i2 , x i3 , x i4 }, i = 1, 2, ..., n; Among them, X i is the sample data of the snow removal width of the i-th category, X i1 is the snow removal gear parameter of the i-th sample, X i2 is the driving speed parameter of the i-th sample, X i3 is the snow thickness parameter of the i-th sample, X i4 is the snow removal width parameter of the i-th type of sample; Determine whether the snow thickness is too thick. If the input snow thickness is too thick (b = 0), a group of snow removal equipment can be used for joint operation or the snow removal operation time can be extended. If the snow thickness is within the normal range (b = 1), the snow thickness d is used to search all data in the database, and all snow removal width data that meet the current snow thickness are recorded as n1<n,X1∈X; Where b is a Boolean variable; Sort the data subset X1 in order of speed from small to large. The objective function of the algorithm is defined as: Among them, V i is the speed of the snowplow, L j is the length of the snow removal road at the airport, and J(t) is the operation time of the snow removal vehicle; Repeat formula (2) to solve the objective function and determine the relationship between the objective function and the airport's allowed operating time T. The expressions of the snow removal mode are shown in formula (3). When the objective function meets the time requirement, the loop is exited. When the time requirement is not met, formula (3) is executed repeatedly. Where t1 is the time difference between the two snow removal vehicles when the snow removal formation is used for snow removal. Among them, T is the airport's permitted operating time, and F is the snowplow formation mode; According to the corresponding snow removal mode, calculate the fuel consumption O of the snow removal vehicle when completing the snow removal operation in each mode. Fuel is the fuel consumption per unit time when the snow removal vehicle works in different snow removal gears. According to the different snow removal gears, fuel has four different fuel consumptions. Fuel1 is the fuel consumption when the engine starts to the working state. When F=1, the fuel consumption is O1, when F=2, the fuel consumption is O2, and when F=3, the fuel consumption is divided into two cases. O3 corresponds to the fuel consumption of a single formation, and O4 corresponds to the fuel consumption of a double formation. Finally, the optimal strategy for snow removal planning and basic information of snow removal planning are output.

10. An intelligent snow removal planning system for a large road snow removal vehicle, characterized in that: The system comprises: An input bar module and an output bar module. The input bar module is used to input information, including an airport permitted operation time selection button, a field and track parameter selection knob, and a snow thickness parameter selection cursor. The output bar module is used to output and display results, including five parameters: snow removal gear, driving speed, formation, snow removal time, and fuel consumption. one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors execute the intelligent snow removal planning method for a large-scale road snow removal vehicle as described in any one of claims 1-9.

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