WO2020206798A1 - 对流天气条件下基于最优穿越路径的航路阻塞度评估方法 - Google Patents

对流天气条件下基于最优穿越路径的航路阻塞度评估方法 Download PDF

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WO2020206798A1
WO2020206798A1 PCT/CN2019/086516 CN2019086516W WO2020206798A1 WO 2020206798 A1 WO2020206798 A1 WO 2020206798A1 CN 2019086516 W CN2019086516 W CN 2019086516W WO 2020206798 A1 WO2020206798 A1 WO 2020206798A1
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grid
path
route
airway
transportation box
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French (fr)
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聂建强
陈曦
严勇杰
黄吉波
陈飞飞
葛然
田靖
马园园
徐善娥
毛亿
王煊
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中国电子科技集团公司第二十八研究所
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Priority to JP2020545137A priority Critical patent/JP7077413B2/ja
Publication of WO2020206798A1 publication Critical patent/WO2020206798A1/zh

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    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
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  • the invention belongs to the field of air traffic management, and particularly relates to a method for evaluating the degree of congestion of a route based on an optimal crossing path under convective weather conditions.
  • Airway as a basic airspace structural unit, is usually represented by a sequence of location points composed of a set of geographic locations (longitude, latitude), and plays an important role in the operation of air traffic management.
  • Route Blockage refers to the extent to which a route affected by severe weather can be used by air traffic, and is an important indicator for evaluating the available capacity of a route under convective weather.
  • the degree of airway congestion is the main influencing factor is the intensity of convective weather and the characteristics of temporal and spatial distribution, which has nothing to do with the workload limit of the driver or controller or the operating state of ATM.
  • Airway congestion degree can be used as a quantitative analysis index to measure the impact of weather on air traffic operation, and it can be correlated with historical operation data or air traffic operation capacity in a specific scenario, providing decision-making reference for delay statistics and capacity prediction.
  • the degree of airway congestion can also effectively assist air traffic controllers and airline dispatchers to achieve refined management of flight operations under convective weather conditions. Therefore, it is very necessary and very important to evaluate the congestion degree of the route under convective weather conditions.
  • the present invention provides a method for evaluating the degree of airway congestion based on the optimal crossing path under convective weather conditions, and provides support for the future development of more robust air traffic management decision support tools integrated with weather information.
  • the specific technical scheme for realizing the method of the present invention includes the following steps:
  • Step 1 Divide the route grid
  • Step 2 Determine the probability of route weather avoidance
  • Step 3 construct the optimal crossing path of the air route
  • Step 4. Calculate the degree of congestion of the route.
  • Step 1 includes:
  • Step 1-1 route segment interpolation:
  • the interpolation interval L of the route section is calculated, and then calculated according to the Euclidean distance D between two adjacent waypoints Number of interpolation points K:
  • Step 1-2 Construct and grid the airway section transportation box.
  • step 1-1 calculate the Euclidean distance D between two adjacent waypoints according to the following formula:
  • step 1-1 the position of the route interpolation point is obtained by solving the equation of the distance and slope between the interpolation point and the starting route point.
  • the specific calculation method includes:
  • the position of the route interpolation point is obtained, and the sequence of the route interpolation point is obtained.
  • Steps 1-2 include:
  • the sequence of route interpolation points calculate the route azimuths of two adjacent waypoints in turn (the azimuth of the route is the azimuth of the next waypoint relative to the previous one), and then calculate the two perpendicular to the azimuth of the route The azimuth angle, respectively, taking the two adjacent waypoints as the starting points, and extending the distance from both sides of the airway to the width of the airway (for example, 20km) along the two azimuth directions perpendicular to the azimuth of the airway, to obtain four Location point.
  • the rectangular transportation box is the airway section transportation box;
  • the two vertices of the broad side are the start and end points of the interpolation, and the spatial granularity of the convective weather meteorological data is used as the interpolation interval.
  • the interpolation method in step 1-1 is used to interpolate the broad side of the transport box of the airway section.
  • the corresponding interpolation points of the two broad sides of the airway section transportation box are sequentially connected, and the airway section transportation box is meshed and subdivided to obtain the airway section transportation box grid.
  • Step 2 includes:
  • Step 2-1 Convert radar reflectivity into avoidance probability, and assign grid avoidance probability values at different moments for each route section transportation box according to the time granularity of the evaluation start time and avoidance probability;
  • Step 2-2 calculate the evasion probability of the transportation box grid:
  • step 1-1 Take the midpoint of the two broad sides of the airway section transportation box grid as the start and end points of the interpolation, and use the spatial granularity that avoids the probability distribution as the interpolation interval.
  • step 1-1 Use the interpolation method in step 1-1, in the direction parallel to the airway, Each grid of the section transportation box is further subdivided to obtain the transportation box grid; according to the coordinate position of the center of the transportation box grid, the avoidance probability value of the 4 nearest meteorological grid points is calculated, and the reciprocal of the distance is used as the weight Perform a weighted summation of the 4 avoidance probabilities to calculate the avoidance probability of the transportation box grid.
  • the formula is as follows:
  • Prob is the avoidance probability of the transportation box grid
  • Wp i is the avoidance probability value of the i-th point in the 4 meteorological grid points closest to the transportation box grid
  • D i is the i-th point and the transportation box network The distance between grids. Then take the average of the avoidance probability of each sub-grid in the transportation box grid of the airway section as the avoidance probability of the transportation box grid, the formula is as follows:
  • Pb is the avoidance probability of the transportation box grid
  • N is the total number of sub-grids contained in the transportation box grid
  • Prob i is the avoidance probability of the i-th sub-grid.
  • Step 3 includes:
  • Step 3-1 Determine the candidate blocking threshold and initialize the blocking threshold:
  • the minimum candidate blockage threshold is the minimum avoidance probability value
  • the maximum candidate blockage threshold is the maximum avoidance probability value
  • a 7-level candidate blockage threshold is designed , From level 1 to level 7 are 0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0.
  • the minimum candidate blocking threshold is initialized to the blocking threshold; the transportation box grid with the avoidance probability greater than the blocking threshold is defined as the blocking network Grid, the transportation box grid whose avoidance probability value is less than the blocking threshold is defined as non-blocking grid;
  • Step 3-2 Construct a directional route through the network:
  • each route section transportation box is divided into a blocking grid area and a non-blocking grid area, and the adjacent non-blocking grids in each route section transportation box are merged to form a network node according to the route direction.
  • the expression of grid node A is A[1,(3,4,5)], where A represents the name of the grid node, 1 represents the number of the transport box on the route where the grid node A is located, 3,4, 5 is the grid number sequence; the network nodes in the transportation box of the adjacent route section are judged to be adjacent nodes. If the adjacent nodes have one or more identical grid numbers, the adjacent nodes are judged to be connected and connected by connected edges.
  • the direction is from the transportation box of the preceding flight section to the transportation box of the succeeding flight section, and the weight of the connected edges is the number of the same grid numbers of adjacent nodes;
  • Step 3-3 Determine network connectivity:
  • the grid node in the transport box of the first airway section is used as the starting node, and the grid node in the transport box of the last airway section is the leaf node.
  • the connected path is searched from each starting node in turn based on the principle of depth first. If If a connected path to the leaf node is found, it is determined that the airway is connected through the network; if the connected path does not exist, it is determined that the airway is not connected through the network, the blocking threshold level is incremented by one, and step 3-2 is returned; if it exists, the airway Connect through the network and go to step 3-4;
  • Step 3-4 Search for connected paths:
  • Step 3-5 Select the optimal route crossing path:
  • the connected edge with the smallest weight value is defined as the path bottleneck, and the smallest weight value is taken as the bottleneck value. If there is an intersection between the grid numbers of the start and end nodes of the connected edges, it is determined that there is no jump, that is, the jump value is 0; if there is no intersection, it is determined that there is a jump, and the jump value is the minimum distance between the start and end nodes;
  • Step 4 includes:
  • Step 4-1 Calculate the blocking degree of the optimal route crossing path:
  • Step 4-2 Determine the degree of congestion of the route:
  • the airway blocking degree is 1; if the blocking threshold is not 1, the optimal airway traversing path blocking degree is calculated for each optimal airway traversing path in turn, and the maximum optimal airway traversing path blocking degree is selected as the airway blocking degree.
  • the path bottleneck congestion degree calculation method is as follows: extract the transportation box grids with meteorological feature values greater than or equal to the blocking threshold in the transportation boxes on both sides of the path bottleneck, and determine the center position of each transportation box grid to transport the box network
  • the reciprocal of the longitudinal distance between the grid center and the path bottleneck center is the weight
  • the weighted average of the meteorological feature values of all extracted grids in the transport boxes on both sides of the path bottleneck is calculated as follows:
  • PB is the degree of path congestion
  • L is the transport box on the left side of the path bottleneck
  • R is the transport box on the right side of the path bottleneck
  • i is the transport box network whose meteorological feature values in the transport boxes on both sides of the path bottleneck are greater than or equal to the congestion threshold.
  • cell identification w i is the characteristic weather transport box of the grid i
  • d i is the radial distance between the cartridge transport grid center i and the center path bottlenecks.
  • step 1 for the route section that is affected by convective weather and composed of several position point sequences (more than or equal to two), according to the space-time granularity of the radar reflectivity data representing the intensity of convective weather and the average flight speed information of the aircraft, All two adjacent waypoints in the airway section are interpolated to obtain a new waypoint sequence with finer spatial granularity.
  • a rectangular transport box is constructed for the route section according to the width of the route and the spatial granularity of the meteorological data, and each transport box is meshed in the direction parallel to the route to realize the impact of convective weather The meshing of the airway section.
  • step 2 the gridded reflectivity data is converted into avoidance probability data according to the radar reflectivity range of convective weather.
  • the route segment grid in step 1 is interpolated and refined to obtain a finer granular route sub-grid.
  • the avoidance probability values of the four closest meteorological grids around the route sub-grid weighted and summed with the reciprocal of the distance to obtain the avoidance probability of the route sub-grid.
  • the average value of the avoidance probability of each route sub-grid in the route section grid is taken as the avoidance probability of the route section grid.
  • the candidate blocking threshold set is determined according to the value distribution of the avoidance probability and arranged from small to large.
  • the minimum candidate threshold is the minimum avoidance probability value
  • the maximum candidate threshold is the maximum avoidance probability value
  • the minimum candidate blocking threshold is initialized to the blocking threshold.
  • the route grid with the avoidance probability greater than the blocking threshold is defined as a blocking grid
  • the route grid with the avoidance probability value less than the blocking threshold is defined as a non-blocking grid.
  • a directional route traversing network is constructed and the connectivity of the network is judged. If the network is disconnected, update the current blocking threshold to be the smallest candidate blocking threshold that is greater than the current blocking threshold, and rebuild the directional route to traverse the network. If the network is connected, search out all connected paths in the network, and select the optimal route traversal path according to the width of the narrowest part of the path and the complexity of the path.
  • step 4 it is determined whether the blocking threshold is 1. If it is 1, the airway congestion degree is 1. Otherwise, calculate the congestion degree of all the optimal crossing paths constructed based on the route to be evaluated in turn, and then select the maximum optimal crossing path congestion degree as the route congestion degree.
  • the present invention aims to evaluate the degree of influence of convective weather on an aircraft that passes through a certain area along a route within a specified time slice, relates to a quantitative evaluation method of the influence of convective weather on air traffic management operations, and belongs to the field of air traffic management.
  • a route section that is affected by convective weather and consists of several positional point sequences (more than or equal to two)
  • the route section All the two adjacent waypoints within are interpolated to obtain a more fine-grained new waypoint sequence.
  • a rectangular transport box is constructed for the route section according to the width of the route and the spatial granularity of the meteorological data, and each transport box is meshed in the direction parallel to the route, so as to realize the convective weather
  • the affected route section is meshed.
  • the gridded radar reflectivity data is converted into gridded avoidance probability data.
  • the route segment grid is further interpolated into a finer granular route sub-grid.
  • the route grid with the avoidance probability greater than the blocking threshold is defined as a blocking grid
  • the route grid with the avoidance probability value less than the blocking threshold is defined as a non-blocking grid.
  • a directional route traversing network is constructed and the connectivity of the network is judged. If the network is disconnected, update the current blocking threshold to be the smallest candidate blocking threshold that is greater than the current blocking threshold, and rebuild the directional route to traverse the network. If the network is connected, all connected paths in the network are searched out, and the optimal traversal path of the route is selected according to the width of the narrowest part of the path and the complexity of the path. Finally, determine whether the blocking threshold is 1. If it is 1, the airway congestion degree is 1. Otherwise, calculate the congestion degree of all the optimal traversal paths constructed based on the route in turn, and then select the maximum optimal route traversal path congestion degree from them as the route congestion degree.
  • the present invention ignores the complexity of the flight path formed by the aircraft during flight along the route and the dynamics of the evolution of convective weather in order to avoid convective weather, and needs to incorporate more human subjective judgment results, which may cause convective weather to affect the route Route blockage (RB) evaluation method based on optimal traversing path for trajectory operation based on the characteristics of judgment deviation of the degree of route traffic flow, and comprehensive consideration of the intensity of weather encountered by the aircraft in the process of crossing the airspace Based on the optimal route traversal path constructed for track operation, calculate the degree of convective weather blocking the route, and quantify the impact of convective weather on flight operations.
  • RB route Blockage
  • the innovation of the present invention is mainly reflected in the following three aspects: 1)
  • the evasion probability assigned to the grid of each transport box in the route section is determined by the airspace meteorological characteristics when the aircraft passes through the corresponding transport box, ensuring the evaluation of the degree of congestion The results are suitable for refined specific flight operation management; 2)
  • the congestion threshold is determined autonomously based on the connectivity of the segmented airspace defined by the traversal of different candidate congestion thresholds from low to high, reduces the intervention of subjective judgments, and improves the consistency and consistency of quantitative evaluation results.
  • the optimal route traversal path is selected from the directed network graph established based on the congestion threshold and the route congestion degree is calculated accordingly. Quantify the objectivity and accuracy of the impact of convective weather on flight operation constraints.
  • the present invention has significant advantages in that it comprehensively considers the intensity of the worst convective weather encountered by the aircraft in the process of traversing the airspace and the complexity characteristics of the crossing path, and autonomously determines the meteorological avoidance threshold to avoid subjective judgments to the evaluation results.
  • the intervention is based on the optimal route traversal path constructed for trajectory operation, to calculate the degree of convective weather blocking the route, and to improve the accuracy and robustness of quantitatively assessing the impact of convective weather on flight operations.
  • the avoidance probability assigned to the grid of each transport box in the route section is determined by the meteorological characteristics of the airspace when the aircraft passes through the corresponding transport box, ensuring that the congestion evaluation results are suitable for refinement Specific flight operation management; 2)
  • the congestion threshold is determined autonomously based on the connectivity of the segmented airspace defined by traversing different candidate congestion thresholds from low to high, reducing the intervention of human subjective judgment, and improving the consistency and robustness of the quantitative evaluation results; 3 ) Based on the complexity of the path that the aircraft traverses along the route and the intensity of the weather encountered, the optimal route traversal path is selected from the directed network map established based on the congestion threshold and the route congestion degree is calculated based on this, and the quantitative assessment of convective weather is improved. Flight operation constraints affect the objectivity and accuracy.
  • Figure 1 is a detailed flow chart of the method for evaluating the degree of congestion of the route based on the optimal crossing path under convective weather conditions.
  • Figure 2 is a schematic diagram of the grid division of the route section.
  • Figure 3 is a schematic diagram of determining the probability of weather avoidance on air routes.
  • Figure 4 is a schematic diagram of the construction of the optimal traversal path for the route section.
  • the present invention is mainly composed of 4 parts: dividing the route grid, determining the route weather avoidance probability, constructing the route optimal crossing path and calculating the route blockage degree.
  • the detailed process of the present invention is further refined as shown in Figure 1, and the specific steps are as follows:
  • Step 1-1 Route segment interpolation
  • the time granularity of the convective weather radar reflectance data combined with the average flight speed of the aircraft, calculate the interpolation interval L of the route section (for example, the time granularity of the weather data is 6min and the average flight speed of the aircraft is 200m/s, then the interpolation interval L 72km), then according to the Euclidean distance between two adjacent waypoints (among them Is the longitude difference between two points, ⁇ is the latitude difference between two points, Is the average latitude value of two points), calculate the number of interpolation points Finally, linear interpolation is performed on two adjacent waypoints in turn to obtain a more refined sequence of airway interpolation points. The position of the interpolation point is obtained by solving the equation of the distance between the interpolation point and the starting waypoint and the slope.
  • the specific calculation method is as follows.
  • the first waypoint is recorded as (x 1 ,y 1 )
  • the second waypoint is recorded as (x 2 ,y 2 )
  • the kth interpolation point between the first waypoint and the second waypoint Marked as Where x represents longitude and y represents latitude, the equations are constructed as follows:
  • Step 1-2 Construct and grid the airway section transportation box
  • the distance between two adjacent waypoints is taken as the length
  • the width of the route (such as 20km) is the width
  • the two directions are perpendicular to the route.
  • the side is expanded to construct a rectangular transport box.
  • the transportation box is gridded and subdivided in the direction perpendicular to the route section to obtain the grid transportation box.
  • Step 2-1 Conversion of radar reflectivity to avoidance probability
  • the radar reflectivity reflects the intensity characteristics of convective weather. The larger the reflectivity value, the more severe the convective weather, and the higher the probability that the aircraft will avoid it. Therefore, based on experience, according to the range of radar reflectivity data under convective weather conditions, the gridded radar reflectivity data at different times is converted into corresponding avoidance probability data. The specific conversion relationship is shown in Table 1.
  • Radar reflectivity (unit: dBZ) Avoidance probability dBZ ⁇ 18 0.0 18 ⁇ dBZ ⁇ 30 0.1 30 ⁇ dBZ ⁇ 41 0.3 41 ⁇ dBZ ⁇ 46 0.5 46 ⁇ dBZ ⁇ 50 0.7 50 ⁇ dBZ ⁇ 57 0.9 57 ⁇ dBZ 1
  • each transportation box is assigned the grid avoidance probability value at different times.
  • the evaluation start time is 13:00
  • the time granularity of the avoidance probability is 6min
  • the route section to be assessed is divided into 5 transport boxes, then along the sailing direction, the corresponding time to the avoidance probability in each transport box is 13:00, 13:06, 13:12, 13:18 and 13:24.
  • Step 2-2 Calculate the avoidance probability of the transportation box grid
  • each grid of the transport box is further subdivided in the direction parallel to the airway.
  • Get the transport box grid According to the coordinate position of the center of the sub-grid, calculate the avoidance probability value of the 4 nearest meteorological grid points, and use the reciprocal of the distance as the weight to sum the 4 avoidance probabilities to calculate the avoidance probability of the transportation box grid .
  • the average value of the avoidance probability of each sub-grid in the transportation box grid is taken as the avoidance probability of the transportation box grid.
  • Step 3-1 Determine the candidate blocking threshold and initialize the blocking threshold
  • the candidate blocking threshold set is determined and arranged from small to large.
  • the minimum candidate blocking threshold is the minimum avoidance probability value
  • the maximum candidate blocking threshold is the maximum avoidance probability value.
  • 7-level candidate blocking thresholds are designed, followed by (0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0), and the minimum candidate blocking threshold is initialized to the blocking threshold.
  • the route grid with the avoidance probability greater than the blocking threshold is defined as a blocking grid
  • the route grid with the avoidance probability value less than the blocking threshold is defined as a non-blocking grid.
  • Step 3-2 Construct a directional route through the network
  • each transport box is divided into a blocked grid area and a non-blocked grid area according to the blocking threshold.
  • the node expression form is: node name [location box number, (grid number sequence)], for example, the expression form of node A in Figure 4 is A[1,(3,4,5)].
  • Nodes in adjacent transport boxes are considered adjacent nodes. Adjacent nodes have one or more identical grid numbers, then it is considered that the adjacent nodes are connected and connected by connected edges. The direction is from the preceding transportation box to the succeeding transportation box. The weight of the connected edges is the same grid number of the adjacent nodes quantity.
  • Step 3-3 Determine network connectivity
  • a connected path from the starting node to the leaf node is searched from the airway traversing network according to the depth-first algorithm. If it does not exist, the route is not connected through the network, the blocking threshold is increased by one level, and step 3-2 is returned. If it exists, the route will pass through the network to connect and go to step 3-4.
  • Step 3-4 Search for connected paths
  • the connected path searched out in the airway traversing network is represented by a sequence composed of node and connected edge weights.
  • the three connected paths are: ⁇ A, 2, C, 2, E, 1, G ⁇ , ⁇ A, 2, C, 2, E, 1, H ⁇ and ⁇ B, 2, D, 1, F, 1, H ⁇ , the letter of each path represents the node, and the number represents the connection between its left and right adjacent nodes Edge weight.
  • Step 3-5 Select the optimal route crossing path
  • the connected edge with the smallest weight value is defined as the path bottleneck (referred to as the bottleneck), and this weight value is used as the bottleneck value (although the bottleneck value is unique, the number of bottlenecks may not be unique, and there is a path with multiple bottlenecks Possibility). If there is an intersection between the grid numbers of the start and end nodes of the connected edges, it is considered that there is no jump, that is, the jump value is 0; if there is no intersection, it is considered that there is a jump, and the jump value is the minimum distance between the start and end nodes.
  • the paths with the largest bottleneck value and the smallest number of bottlenecks are ⁇ A, 2, C, 2, E, 1, G ⁇ and ⁇ A, 2, C, 2, E, 1, H ⁇ , further calculation
  • the path complexity is 1/44 and 0 respectively, so the optimal route crossing path is ⁇ A, 2, C, 2, E, 1, H ⁇ .
  • Step 4-1 Calculate the degree of blocking of the optimal route crossing path
  • Step 4-2 Determine the degree of airway congestion
  • the optimal path blocking degree is calculated for each optimal route traversing path in turn, and the maximum optimal path traversing path blocking degree is selected as the path blocking degree.
  • the optimal route crossing paths ⁇ A, 3, C, 3, E, 1, G ⁇ and ⁇ A, 3, C, 3, E, 1, H ⁇ blocking degrees are 0.764 and 0.768, respectively , So the airway jam degree is 0.768.
  • the airway blocking degree is 1.
  • the airway congestion degree value is an index to quantify the influence of convective weather on the airway, and provides an important basis for the formulation of subsequent air traffic flow management strategies.
  • the present invention provides a route blockage evaluation method based on the optimal crossing path under convective weather conditions.

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Abstract

本发明公开了对流天气条件下基于最优穿越路径的航路阻塞度评估方法，包括以下步骤：步骤1，划分航路网格；步骤2，确定航路气象规避概率；步骤3，构建航路最优穿越路径；步骤4，计算航路阻塞度。

Description

对流天气条件下基于最优穿越路径的航路阻塞度评估方法 技术领域
本发明属于空中交通管理领域，尤其涉及对流天气条件下基于最优穿越路径的航路阻塞度评估方法。
背景技术
在美国国家空域系统中，天气导致的延误占到70％以上。在天气导致的延误中，对流天气占到60％。因此，对于空中交通管理者而言，在制定战略计划和实施战术措施时，对流天气及其对国家空域系统运行的影响预报是极其关键的。天气雷达是探测对流天气现象的重要气象设备，其提供的雷达反射率也是表征对流天气现象时空特征的重要气象数据。航路，作为基础的空域结构单元，通常由一组地理位置(经度，纬度)构成的位置点序列表示，在空中交通管理运行过程中发挥着重要作用。航路阻塞度(Route Blockage，RB)是指受恶劣天气影响航路可被空中交通使用的程度，是评估航路在对流天气覆盖下的可用能力的重要指标。航路阻塞度作为一个恶劣天气属性，其主要影响因素是对流天气的强度与时空分布特征，与驾驶员或管制员工作负荷限制或ATM运行状态无关。航路阻塞度可以作为衡量天气对空中交通运行影响的定量分析指标，与历史运行数据或特定场景下的空中交通运行容量关联，为延误统计、容量预测等提供决策参考。同时，航路阻塞度还可以有效辅助空中交通管制员和航空公司签派员对对流天气条件下的航班运行实现精细化管理。因此，对流天气条件下的航路阻塞度评估是十分必要的，也是非常重要的。
查阅国内外相关资料，尚未发现针对对流天气条件综合考虑航路穿越路径复杂性和遭遇天气强度的基于最优穿越路径的航路阻塞度评估方法。
发明内容
针对现有将高空或终端区空域的对流天气现象转换为对航班运行的量化约束的评估方法中，缺乏对航空器在穿越空域过程中的路径复杂性的考虑，人为主观判断对量化评估结果的干预严重等特点，本发明提供了一种对流天气条件下基于最优穿越路径的航路阻塞度评估方法，为未来开发更加鲁棒的集成天气信息的空中交通管理决策支持工具提供支持。
实现本发明方法的具体技术方案包括以下步骤：
步骤1，划分航路网格；
步骤2，确定航路气象规避概率；
步骤3，构建航路最优穿越路径；
步骤4，计算航路阻塞度。
步骤1包括：
步骤1-1，航路段插值：
根据现有数值天气预报系统的对流天气雷达反射率预报数据的时间颗粒度，结合飞机平均飞行时速，计算得到航路段的插值间隔L，再根据相邻两个航路点间的欧式距离D，计算插值点个数K：
Figure PCTCN2019086516-appb-000001
最后依次对相邻两个航路点进行线性插值，得到航路插值点的位置，从而得到航路插值点序列；
步骤1-2：构建航路段运输盒并进行网格化。
步骤1-1中，根据如下公式计算相邻两个航路点间的欧式距离D：
Figure PCTCN2019086516-appb-000002
其中
Figure PCTCN2019086516-appb-000003
为相邻两个航路点间经度差，Δθ为相邻两个航路点间纬度差，
Figure PCTCN2019086516-appb-000004
为相邻两个航路点的平均纬度值。
步骤1-1中，所述航路插值点的位置根据插值点与起始航路点的距离和斜率列方程求解得到，具体计算方法包括：
设定第1个航路点位置记为(x 1,y 1)，第2个航路点位置记为(x 2,y 2)，x 1,y 1分别表示第1个航路点的经度和纬度；第1个航路点和第2个航路点之间的第k个航路插值点位置记为
Figure PCTCN2019086516-appb-000005
其中，
Figure PCTCN2019086516-appb-000006
表示第k个插值点的经度，
Figure PCTCN2019086516-appb-000007
表示第k个插值点的纬度，构建方程组如下：
Figure PCTCN2019086516-appb-000008
根据该方程组得到航路插值点的位置，从而得到航路插值点序列。
步骤1-2包括：
根据航路插值点序列，依次计算两个相邻航路点的航路方位角(航路方位角为后一 航路点相对于前一航路点的方位角)，再计算与所述航路方位角垂直的两个方位角，分别以所述两个相邻航路点为起始点，沿着垂直于航路方位角的两个方位角方向，向航路两侧外扩至航路宽度的距离(例如20km)，得到四个位置点。以所述四个位置点为矩形运输盒顶点，以平行于航路方向为长，以垂直于航路方向为宽，构建矩形运输盒，所述矩形运输盒即为航路段运输盒；然后以运输盒宽边的两个顶点为插值起止点，以对流天气气象数据的空间颗粒度为插值间隔，使用步骤1-1的插值方法，对航路段运输盒宽边进行插值处理。依次连接航路段运输盒两条宽边的对应插值点，对航路段运输盒进行网格化细分，得到航路段运输盒网格。
步骤2包括：
步骤2-1，将雷达反射率转换成规避概率，根据评估起始时间和规避概率的时间颗粒度，为每个航路段运输盒分配不同时刻的网格化规避概率值；
步骤2-2，计算运输盒网格规避概率：
以航路段运输盒网格两条宽边的中点为插值起止点，以规避概率分布的空间颗粒度为插值间隔，使用步骤1-1的插值方法，在平行于航路的方向上，对航路段运输盒的每个网格进一步细分，得到运输盒子网格；根据运输盒子网格中心的坐标位置，计算得到距离其最近的4个气象网格点的规避概率值，以距离倒数为权重对4个规避概率进行加权求和，计算得到运输盒子网格的规避概率，其公式如下所示：
Figure PCTCN2019086516-appb-000009
其中Prob为运输盒子网格的规避概率，Wp i为距离所述运输盒子网格最近的4个气象网格点中第i个点的规避概率值，D i为第i个点与运输盒子网格间的距离。再取航路段运输盒网格内每个子网格的规避概率的平均值作为运输盒网格的规避概率，公式如下所示：
Figure PCTCN2019086516-appb-000010
其中Pb为运输盒网格的规避概率，N为运输盒网格包含的子网格总数，Prob i为第i个子网格的规避概率。
步骤3包括：
步骤3-1：确定候选阻塞阈值并初始化阻塞阈值：
根据运输盒网格的规避概率的取值分布确定候选阻塞阈值集并由小到大排列，最小 候选阻塞阈值为最小规避概率值，最大候选阻塞阈值为最大规避概率值；设计7级候选阻塞阈值，从第1级到第7级依次为0，0.1，0.3，0.5，0.7，0.9，1.0，将最小候选阻塞阈值初始化为阻塞阈值；规避概率大于阻塞阈值的运输盒网格被定义为阻塞网格，规避概率值小于阻塞阈值的运输盒网格被定义为非阻塞网格；
步骤3-2：构建有向航路穿越网络：
根据阻塞阈值将每一个航路段运输盒分割成阻塞网格区域和非阻塞网格区域，将每一个航路段运输盒内的相邻非阻塞网格合并，作为网络节点，按航路方向生成网络节点序列，网格节点A的表达形式为A[1,(3,4,5)]，其中，A表示网格节点名称，1表示网格节点A所在航路段运输盒的编号，3,4,5为网格编号序列；相邻航路段运输盒子中的网络节点判定为相邻节点，相邻节点有一个或两个以上相同的网格编号，则判定相邻节点连通，并用连通边连接，方向从前序航路段运输盒指向后序航路段运输盒，连通边的权重为相邻节点的相同网格编号的数量；
步骤3-3：判别网络连通性：
以第一个航路段运输盒中网格节点为起始节点，最后一个航路段运输盒中网格节点为叶子节点，以深度优先为原则依次从每一个起始节点出发开始搜索连通路径，如果找到一条到达叶子节点的连通路径，则判定航路穿越网络连通；若连通路径不存在，则判定航路穿越网络不连通，阻塞阈值的级别递增1级，并返回步骤3-2；若存在，则航路穿越网络连通，进入步骤3-4；
步骤3-4：搜索连通路径：
以第一个航路段运输盒中网格节点为起始节点，最后一个航路段运输盒中网格节点为叶子节点，按深度优先原则从航路穿越网络中搜索出所有从起始节点出发到达叶子节点的连通路径，航路穿越网络中搜索出的连通路径用网格节点和连通边权重组成的序列表示；
步骤3-5：选取最优航路穿越路径：
针对每一条连通路径，将其权重值最小的连通边定义为路径瓶颈，所述最小权重值作为瓶颈值。连通边起止节点的网格编号如果存在交集，则判定不存在跳跃，即跳跃值为0；如果不存在交集，则判定存在跳跃，跳跃值为起止节点之间的最小距离；
连通路径中所有连通边的跳跃值之和与最大总跳跃值的比值表征连通路径的复杂 性，记为
Figure PCTCN2019086516-appb-000011
其中N为航路段运输盒的个数，M为一个航路运输盒中网格数目，m i为连通路径中第i条连通边的跳跃值，i＝1,2,…,N-1，则基于具有N个运输盒的航路段构建的连通路径的连通边数为N-1。
在所有连通路径中挑选出瓶颈值最大的路径作为初始候选最优路径，然后从初始候选最优路径中挑选出路径瓶颈数目最少的路径作为候选最优路径，最后从候选最优路径中选出复杂性Complexity最低的路径作为最优航路穿越路径。
步骤4包括：
步骤4-1：计算最优航路穿越路径阻塞度：
确定最优航路穿越路径中路径瓶颈的个数和各个路径瓶颈中心位置，依次基于每个路径瓶颈计算路径瓶颈阻塞度，选取最大路径瓶颈阻塞度与最优航路穿越路径复杂性Complexity相加，结果作为最优航路穿越路径阻塞度；
步骤4-2：确定航路阻塞度：
如果阻塞阈值为1，航路阻塞度为1；如果阻塞阈值不为1，依次对每条最优航路穿越路径计算最优航路穿越路径阻塞度，从中选取最大最优航路穿越路径阻塞度作为航路阻塞度。
步骤4-1中，路径瓶颈阻塞度计算方法如下：提取路径瓶颈两侧运输盒中气象特征值大于或等于阻塞阈值的运输盒网格，确定各个运输盒网格的中心位置，以运输盒网格中心和路径瓶颈中心之间的纵向距离的倒数为权重，加权平均路径瓶颈两侧运输盒中所有提取的网格的气象特征值，具体计算公式如下：
Figure PCTCN2019086516-appb-000012
式中PB为路径阻塞度，L为路径瓶颈左侧的运输盒，R为路径瓶颈右侧的运输盒，i为路径瓶颈两侧运输盒中的气象特征值大于或等于阻塞阈值的运输盒网格标识，w i为运输盒网格i的气象特征值，d i为运输盒网格i中心与路径瓶颈中心之间的径向距离。
步骤1中，针对受对流天气影响的、由若干位置点序列(大于等于两个)构成的航路段，根据表征对流天气强度的雷达反射率数据的时空颗粒度和飞机平均飞行时速信息，对该航路段内所有相邻的两个航路点进行插值处理，得到更细空间颗粒度的新的航路点序列。在新航路点序列的基础上，根据航路宽度和气象数据的空间颗粒度将航路段构建矩形运输盒，并在平行于航路方向上对每个运输盒进行网格划分，实现对受对流天气影 响的航路段的网格划分。
步骤2中，根据对流天气的雷达反射率取值范围将网格化的反射率数据转换成规避概率数据。根据此规避概率数据的空间颗粒度，对步骤1中的航路段网格进行插值细化处理，得到颗粒度更细的航路子网格。根据航路子网格周围距离最近的四个气象网格的规避概率值，以距离倒数为权重加权求和得到航路子网格的规避概率。在此基础上，再取航路段网格内每个航路子网格的规避概率的平均值作为航路段网格的规避概率。
步骤3中，根据规避概率取值分布确定候选阻塞阈值集并由小到大排列。最小候选阈值为最小规避概率值，最大候选阈值为最大规避概率值，并将最小候选阻塞阈值初始化为阻塞阈值。规避概率大于阻塞阈值的航路网格被定义为阻塞网格，规避概率值小于阻塞阈值的航路网格被定义为非阻塞网格。基于当前阻塞阈值和航路飞行方向构建有向航路穿越网络并对网络的连通性进行判别。如果网络不连通，更新当前阻塞阈值为大于当前阻塞阈值的最小候选阻塞阈值，并重新构建有向航路穿越网络。如果网络连通，搜索出网络中所有连通路径，并根据路径最窄处宽度和路径复杂性选取最优航路穿越路径。
步骤4中，判断阻塞阈值是否为1。如果为1，航路阻塞度为1。否则，依次计算所有基于待评估航路构建的最优穿越路径的阻塞度，然后从中选取最大最优穿越路径阻塞度作为航路阻塞度。
本发明旨在评估在指定时间片内沿航路穿越某区域的航空器受对流天气影响的程度，涉及对流天气对空中交通管理运行影响的量化评估方法，属于空中交通管理领域。首先，针对受对流天气影响的、由若干位置点序列(大于等于两个)构成的航路段，根据表征对流天气强度的雷达反射率数据的时空颗粒度和飞机平均飞行时速信息，对该航路段内所有相邻的两个航路点进行插值处理，得到更细粒度的新的航路点序列。在新航路点序列的基础上，根据航路宽度和气象数据的空间颗粒度将航路段构建矩形运输盒，并在平行于航路方向上对每个运输盒进行网格划分，从而实现对受对流天气影响的航路段进行网格划分。然后，根据构建对流天气的雷达反射率与规避概率映射表，将格点化的雷达反射率数据转换成网格化的规避概率数据。根据格点化气象数据的空间颗粒度，对航路段网格进行进一步插值成颗粒度更细的航路子网格。提取航路子网格周围距离最近的四个气象格点的规避概率值，以距离倒数为权重加权求和得到航路子网格的规避概率。在此基础上，取航路段网格内每个子网格的规避概率的平均值作为航路段网格的规避概率。接着，根据规避概率取值分布确定候选阻塞阈值集并由小到大排列，将最小候 选阻塞阈值初始化为阻塞阈值。规避概率大于阻塞阈值的航路网格被定义为阻塞网格，规避概率值小于阻塞阈值的航路网格被定义为非阻塞网格。基于当前阻塞阈值和航路飞行方向构建有向航路穿越网络并对网络的连通性进行判别。如果网络不连通，更新当前阻塞阈值为大于当前阻塞阈值的最小候选阻塞阈值，并重新构建有向航路穿越网络。如果网络连通，搜索出网络中所有连通路径，并根据路径最窄处宽度和路径复杂性选取航路最优穿越路径。最后，判断阻塞阈值是否为1。如果为1，航路阻塞度为1。否则，依次计算所有基于航路构建的最优穿越路径的阻塞度，然后从中选取最大最优航路穿越路径阻塞度作为航路阻塞度。
本发明针对现有方法忽略航空器在沿航路飞行过程中为规避对流天气形成的飞行路径的复杂性和对流天气演变的动态性，且需要融入较多人为的主观判断结果，可能导致对流天气影响航路航线交通流程度的判断偏差等特点，独创性地提出面向航迹运行的基于最优穿越路径的航路阻塞度(Route Blockage，RB)评估方法，综合考虑航空器在穿越空域过程中遭遇的天气的强度和穿越路径复杂性特征，自主确定气象规避阈值，以面向航迹运行构建的最优航路穿越路径为基础，计算对流天气对航路的阻塞程度，量化评估对流天气对航班运行的影响。具体而言，本发明的创新性主要体现在以下三方面：1)航路段中每一个运输盒的网格被赋予的规避概率由航空器经过对应运输盒时的空域气象特征决定，确保阻塞度评估结果适用于精细化的具体航班运行管理；2)根据由低到高遍历不同候选阻塞阈值定义的分割空域的连通性自主确定阻塞阈值，减少人为主观判断的干预，提高量化评估结果的一致性和鲁棒性；3)以航空器沿航路穿越的路径复杂性和遭遇天气的强度为依据，从基于阻塞阈值建立的有向网络图中挑选出最优航路穿越路径并据此计算航路阻塞度，改进量化评估对流天气对航班运行约束影响的客观性和准确性。
与现有方法相比，本发明的显著优点在于：综合考虑航空器在穿越空域过程中遭遇的最恶劣对流天气的强度和穿越路径的复杂性特征，自主确定气象规避阈值避免人为主观判断对评估结果的干预，以面向航迹运行构建的最优航路穿越路径为基础，计算对流天气对航路的阻塞程度，提高量化评估对流天气对航班运行的影响的准确性和鲁棒性。具体而言，主要体现在以下三方面：1)航路段中每一个运输盒的网格被赋予的规避概率由航空器经过对应运输盒时的空域气象特征决定，确保阻塞度评估结果适用于精细化 的具体航班运行管理；2)根据由低到高遍历不同候选阻塞阈值定义的分割空域的连通性自主确定阻塞阈值，减少人为主观判断的干预，提高量化评估结果的一致性和鲁棒性；3)以航空器沿航路穿越的路径复杂性和遭遇天气的强度为依据，从基于阻塞阈值建立的有向网络图中挑选出最优航路穿越路径并据此计算航路阻塞度，改进量化评估对流天气对航班运行约束影响的客观性和准确性。
附图说明
下面结合附图和具体实施方式对本发明做更进一步的具体说明，本发明的上述或其他方面的优点将会变得更加清楚。
图1是对流天气条件下基于最优穿越路径的航路阻塞度评估方法详细流程图。
图2是航路段网格划分示意图。
图3是航路段气象规避概率确定示意图。
图4是航路段最优穿越路径构建示意图。
具体实施方式
下面结合附图及实施例对本发明做进一步说明。
本发明主要由4部分构成：划分航路网格，确定航路气象规避概率，构建航路最优穿越路径和计算航路阻塞度。在此基础上，对本发明进一步细化后的详细流程如图1所示，具体步骤如下：。
步骤1-1：航路段插值
根据对流天气雷达反射率数据的时间颗粒度，结合飞机平均飞行时速，计算航路段的插值间隔L(如气象数据的时间颗粒度为6min，飞机的平均飞行速度为200m/s，则插值间隔L为72km)，再根据相邻两个航路点间的欧式距离
Figure PCTCN2019086516-appb-000013
(其中
Figure PCTCN2019086516-appb-000014
为两点间经度差，Δθ为两点间纬度差，
Figure PCTCN2019086516-appb-000015
为两点的平均纬度值)，计算插值点个数
Figure PCTCN2019086516-appb-000016
最后依次对相邻两个航路点进行线性插值，得到更精细的航路插值点序列。插值点位置根据插值点与起始航路点的距离和斜率列方程求解得到，具体计算方法示例如下。
例如第1个航路点记为(x 1,y 1)，第2个航路点记为(x 2,y 2)，第1个航路点和第2个航路点之间的第k个插值点记为
Figure PCTCN2019086516-appb-000017
其中x表示经度，y表示纬度，构建方程组如下：
Figure PCTCN2019086516-appb-000018
步骤1-2：构建航路段运输盒并进行网格化
如图2所示，根据步骤1-1的新航路点序列，依次以两个相邻航路点间的距离为长，以航路宽度(如20km)为宽，以垂直于航路的方向向航路两侧外扩，构建矩形运输盒。然后以对流天气气象数据的空间颗粒度(如3km)为插值间隔，在垂直于航路段方向上，对运输盒进行网格化细分，得到网格化运输盒。
步骤2-1：雷达反射率转换成规避概率
雷达反射率反映了对流天气的强度特征，反射率值越大，对流天气越剧烈，飞行器规避它的概率相应就越高。因此依据经验，根据对流天气条件下的雷达反射率数据取值范围，将不同时刻的、网格化的雷达反射率数据转换成相应的规避概率数据。具体的转换关系如表1所示。
表1
雷达反射率(单位：dBZ) 规避概率
dBZ<18 0.0
18≤dBZ＜30 0.1
30≤dBZ＜41 0.3
41≤dBZ＜46 0.5
46≤dBZ＜50 0.7
50≤dBZ＜57 0.9
57＜dBZ 1
其次，根据评估起始时间和上一步得到的规避概率的时间颗粒度，为每个运输盒分配不同时刻的网格化规避概率值。例如评估起始时间为13:00，规避概率的时间颗粒度为6min，待评估航路段用划分为5个运输盒，则沿着航行方向，与每个运输盒中规避概率的对应时刻分别为13:00,13:06,13:12,13:18和13:24。
步骤2-2：计算运输盒网格规避概率
如图3所示，以规避概率分布的空间颗粒度(如3km)为插值间隔，使用步骤1-1 的方法，在平行于航路的方向上，对运输盒的每个网格进一步细分，得到运输盒子网格。根据子网格中心的坐标位置，计算得到距离其最近的4个气象网格点的规避概率值，以距离倒数为权重对4个规避概率进行加权求和，计算得到运输盒子网格的规避概率。在此基础上，再取运输盒网格内每个子网格的规避概率的平均值作为运输盒网格的规避概率。
步骤3-1：确定候选阻塞阈值并初始化阻塞阈值
根据规避概率取值分布确定候选阻塞阈值集并由小到大排列，最小候选阻塞阈值为最小规避概率值，最大候选阻塞阈值为最大规避概率值。根据雷达反射率与规避概率映射表中规避概率的分布，设计7级候选阻塞阈值，依次为(0，0.1，0.3，0.5，0.7，0.9，1.0)，将最小候选阻塞阈值初始化为阻塞阈值。规避概率大于阻塞阈值的航路网格被定义为阻塞网格，规避概率值小于阻塞阈值的航路网格被定义为非阻塞网格。
步骤3-2：构建有向航路穿越网络
如图4所示，根据阻塞阈值将每一个运输盒分割成阻塞网格区域和非阻塞网格区域。将每一个运输盒内的相邻非阻塞网格合并，作为网络节点(简称节点)，按航路方向生成节点序列。节点表达形式为：节点名称[所在盒子编号，(网格编号序列)]，例如图4中节点A的表达形式为A[1,(3,4,5)]。相邻运输盒子中的节点被认为是相邻节点。相邻节点有一个或多个相同的网格编号，则认为相邻节点连通，并用连通边连接，方向从前序运输盒指向后序运输盒，连通边的权重为相邻节点的相同网格编号的数量。
步骤3-3：判别网络连通性
以第一个运输盒中节点为起始节点，最后一个运输盒中节点为叶子节点，按深度优先算法从航路穿越网络中搜索出一条从起始节点出发到达叶子节点的连通路径。若不存在，则航路穿越网络不连通，阻塞阈值递增1级，并返回步骤3-2。若存在，则航路穿越网络连通，进入步骤3-4。
步骤3-4：搜索连通路径
以第一个运输盒中节点为起始节点，最后一个运输盒中节点为叶子节点，按深度优先算法从航路穿越网络中搜索出所有从起始节点出发到达叶子节点的连通路径。航路穿越网络中搜索出的连通路径用节点和连通边权重组成的序列表示，以图4为例，3条连通路径分别为：{A，2，C，2，E，1，G}、{A，2，C，2，E，1，H}和{B，2，D，1， F，1，H}，每条路径的字母表示节点，数字表示其左右两个相邻节点的连通边权重。
步骤3-5：选取最优航路穿越路径
针对每一条连通路径，将其权重值最小的连通边定义为路径瓶颈(简称为瓶颈)，此权重值作为瓶颈值(虽然瓶颈值唯一，但瓶颈数目可能不唯一，存在一条路径有多个瓶颈的可能性)。连通边起止节点的网格编号若存在交集，则认为不存在跳跃，即跳跃值为0；若不存在交集，则认为存在跳跃，跳跃值为起止节点之间的最小距离。连通路径中所有连通边的跳跃值之和与最大总跳跃值的比值表征连通路径的复杂性，记为
Figure PCTCN2019086516-appb-000019
其中N为航路段运输盒的个数，每一个航路运输盒中网格数目为M，连通路径中第i条连通边的跳跃值为m i,i＝1,2,…,N-1(基于具有N个运输盒的航路段构建的连通路径的连通边数为N-1)。
首先在所有连通路径中挑选出瓶颈值最大的路径作为候选最优路径集，然后进一步挑选出瓶颈数目最少的路径作为候选最优路径集，最后再精选出复杂性最低的路径作为最优航路穿越路径。以图4为例，瓶颈值最大且瓶颈数目最少的路径为{A，2，C，2，E，1，G}和{A，2，C，2，E，1，H}，进一步计算路径复杂性分别为1/44和0，因此最优航路穿越路径为{A，2，C，2，E，1，H}。
步骤4-1：计算最优航路穿越路径阻塞度
确定最优航路穿越路径中“瓶颈”的个数和各个“瓶颈”中心位置，依次基于每个“瓶颈”计算“瓶颈”阻塞度，选取最大“瓶颈”阻塞度与最优航路穿越路径复杂性相加，结果作为最优航路穿越路径阻塞度。“瓶颈”阻塞度计算方法如下：提取“瓶颈”两侧阻塞盒子中气象特征值大于或等于(>＝)阻塞阈值的子网格，确定各个子网格的中心位置，以子网格中心和“瓶颈”中心之间的纵向距离的倒数为权重，加权平均“瓶颈”两侧阻塞盒中所有提取的网格的气象特征值。以图4中最优航路穿越路径{A，3，C，3，E，1，G}为例，阻塞度计算过程如下：先计算瓶颈阻塞度：
[(1/2)*0.8+1*0.7+(1/2)*0.8+(1/3)*0.7+(1/4)*0.8+(1/5)*0.8+(1/6)*0.7+(1/7)*0.9+(1/8)*0.9+(1/9)*0.8+(1/10)*0.8]/[(1/2)+1+(1/2)+(1/3)+(1/4)+(1/5)+(1/6)+(1/7)+(1/8)+(1/9)+(1/10)]＝0.764；然后计算最优航路穿越路径复杂性：Complexity＝0；最后计算最优航路穿越路径阻塞度为：blockage＝0.764+0＝0.764。
步骤4-2：确定航路阻塞度
如果阻塞阈值不为1，依次对每条最优航路穿越路径计算最优路径阻塞度，从中选取最大最优航路穿越路径阻塞度作为航路阻塞度。以图4为例，最优航路穿越路径{A，3，C，3，E，1，G}和{A，3，C，3，E，1，H}阻塞度分别为：0.764和0.768，因此航路阻塞度为0.768。如果阻塞阈值为1，航路阻塞度为1。航路阻塞度值是量化对流天气对航路影响程度的指标，为后续空中交通流量管理策略的制定提供重要依据。
本发明提供了对流天气条件下基于最优穿越路径的航路阻塞度评估方法，具体实现该技术方案的方法和途径很多，以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。本实施例中未明确的各组成部分均可用现有技术加以实现。

Claims (9)

  1. 对流天气条件下基于最优穿越路径的航路阻塞度评估方法，其特征在于，包括以下步骤：
    步骤1，划分航路网格；
    步骤2，确定航路气象规避概率；
    步骤3，构建航路最优穿越路径；
    步骤4，计算航路阻塞度。
  2. 根据权利要求1所述的方法，其特征在于，步骤1包括：
    步骤1-1，航路段插值：
    根据对流天气雷达反射率预报数据的时间颗粒度，结合飞机平均飞行时速，计算得到航路段的插值间隔L，再根据相邻两个航路点间的欧式距离D，计算插值点个数K：
    Figure PCTCN2019086516-appb-100001
    最后依次对相邻两个航路点进行线性插值，得到航路插值点的位置，从而得到航路插值点序列；
    步骤1-2：构建航路段运输盒并进行网格化。
  3. 根据权利要求2所述的方法，其特征在于，步骤1-1中，根据如下公式计算相邻两个航路点间的欧式距离D：
    Figure PCTCN2019086516-appb-100002
    其中
    Figure PCTCN2019086516-appb-100003
    为相邻两个航路点间经度差，Δθ为相邻两个航路点间纬度差，
    Figure PCTCN2019086516-appb-100004
    为相邻两个航路点的平均纬度值。
  4. 根据权利要求3所述的方法，其特征在于，步骤1-1中，所述航路插值点的位置根据插值点与起始航路点的距离和斜率列方程求解得到，具体计算方法包括：
    设定第1个航路点位置记为(x 1,y 1)，第2个航路点位置记为(x 2,y 2)，x 1,y 1分别表示第1个航路点的经度和纬度；第1个航路点和第2个航路点之间的第k个航路插值点位置记为
    Figure PCTCN2019086516-appb-100005
    其中，
    Figure PCTCN2019086516-appb-100006
    表示第k个插值点的经度，
    Figure PCTCN2019086516-appb-100007
    表示第k个插值点的纬度，构建方程组如下：
    Figure PCTCN2019086516-appb-100008
    根据该方程组得到航路插值点的位置，从而得到航路插值点序列。
  5. 根据权利要求4所述的方法，其特征在于，步骤1-2包括：
    根据航路插值点序列，依次计算两个相邻航路点的航路方位角，再计算与所述航路方位角垂直的两个方位角，分别以所述两个相邻航路点为起始点，沿着垂直于航路方位角的两个方位角方向，向航路两侧外扩至航路宽度的距离，得到四个位置点；以所述四个位置点为矩形运输盒顶点，以平行于航路方向为长，以垂直于航路方向为宽，构建矩形运输盒，所述矩形运输盒即为航路段运输盒；然后以航路段运输盒宽边的两个顶点为插值起止点，以对流天气气象数据的空间颗粒度为插值间隔，使用步骤1-1的插值方法，对航路段运输盒宽边进行插值处理，依次连接航路段运输盒两条宽边的对应插值点，对航路段运输盒进行网格化细分，得到航路段运输盒网格。
  6. 根据权利要求5所述的方法，其特征在于，步骤2包括：
    步骤2-1，将雷达反射率转换成规避概率，根据评估起始时间和规避概率的时间颗粒度，为每个航路段运输盒分配不同时刻的网格化规避概率值；
    步骤2-2，计算运输盒网格规避概率：
    以航路段运输盒网格两条宽边的中点为插值起止点，以规避概率分布的空间颗粒度为插值间隔，使用步骤1-1的插值方法，在平行于航路的方向上，对航路段运输盒的每个网格进一步细分，得到运输盒子网格；根据运输盒子网格中心的坐标位置，计算得到距离其最近的4个气象网格点的规避概率值，以距离倒数为权重对4个规避概率进行加权求和，计算得到运输盒子网格的规避概率，其公式如下所示：
    Figure PCTCN2019086516-appb-100009
    其中Prob为运输盒子网格的规避概率，Wp i为距离所述运输盒子网格最近的4个气象网格点中第i个点的规避概率值，D i为第i个点与运输盒子网格间的距离；
    再取航路段运输盒网格内每个子网格的规避概率的平均值作为运输盒网格的规避概率，公式如下所示：
    Figure PCTCN2019086516-appb-100010
    其中Pb为运输盒网格的规避概率，N为运输盒网格包含的子网格总数，Prob i为第i个子网格的规避概率。
  7. 根据权利要求6所述的方法，其特征在于，步骤3包括：
    步骤3-1：确定候选阻塞阈值并初始化阻塞阈值：
    根据运输盒网格的规避概率的取值分布确定候选阻塞阈值集并由小到大排列，最小候选阻塞阈值为最小规避概率值，最大候选阻塞阈值为最大规避概率值；设计7级候选阻塞阈值，从第1级到第7级依次为0，0.1，0.3，0.5，0.7，0.9，1.0，将最小候选阻塞阈值初始化为阻塞阈值；规避概率大于阻塞阈值的运输盒网格被定义为阻塞网格，规避概率值小于阻塞阈值的运输盒网格被定义为非阻塞网格；
    步骤3-2：构建有向航路穿越网络：
    根据阻塞阈值将每一个航路段运输盒分割成阻塞网格区域和非阻塞网格区域，将每一个航路段运输盒内的相邻非阻塞网格合并，作为网络节点，按航路方向生成网络节点序列，网格节点A的表达形式为A[1,(3,4,5)]，其中，A表示网格节点名称，1表示网格节点A所在航路段运输盒的编号，3,4,5为网格编号序列；相邻航路段运输盒子中的网络节点判定为相邻节点，相邻节点有一个或两个以上相同的网格编号，则判定相邻节点连通，并用连通边连接，方向从前序航路段运输盒指向后序航路段运输盒，连通边的权重为相邻节点的相同网格编号的数量；
    步骤3-3：判别网络连通性：
    以第一个航路段运输盒中网格节点为起始节点，最后一个航路段运输盒中网格节点为叶子节点，以深度优先为原则依次从每一个起始节点出发开始搜索连通路径，如果找到一条到达叶子节点的连通路径，则判定航路穿越网络连通；若连通路径不存在，则判定航路穿越网络不连通，阻塞阈值的级别递增1级，并返回步骤3-2；若存在，则航路穿越网络连通，进入步骤3-4；
    步骤3-4：搜索连通路径：
    以第一个航路段运输盒中网格节点为起始节点，最后一个航路段运输盒中网格节点为叶子节点，按深度优先原则从航路穿越网络中搜索出所有从起始节点出发到达叶子节点的连通路径，航路穿越网络中搜索出的连通路径用网格节点和连通边权重组成的序列表示；
    步骤3-5：选取最优航路穿越路径：
    针对每一条连通路径，将其权重值最小的连通边定义为路径瓶颈，所述最小权重值作为瓶颈值；连通边起止节点的网格编号如果存在交集，则判定不存在跳跃，即跳跃值为0；如果不存在交集，则判定存在跳跃，跳跃值为起止节点之间的最小距离；
    连通路径中所有连通边的跳跃值之和与最大总跳跃值的比值表征连通路径的复杂性，记为
    Figure PCTCN2019086516-appb-100011
    其中N为航路段运输盒的个数，M为一个航路运输盒中网格数目，m i为连通路径中第i条连通边的跳跃值，i＝1,2,…,N-1，则基于具有N个运输盒的航路段构建的连通路径的连通边数为N-1；
    在所有连通路径中挑选出瓶颈值最大的路径作为初始候选最优路径，然后从初始候选最优路径中挑选出路径瓶颈数目最少的路径作为候选最优路径，最后从候选最优路径中选出复杂性Complexity最低的路径作为最优航路穿越路径。
  8. 根据权利要求7所述的方法，其特征在于，步骤4包括：
    步骤4-1：计算最优航路穿越路径阻塞度：
    确定最优航路穿越路径中路径瓶颈的个数和各个路径瓶颈中心位置，依次基于每个路径瓶颈计算路径瓶颈阻塞度，选取最大路径瓶颈阻塞度与最优航路穿越路径复杂性Complexity相加，结果作为最优航路穿越路径阻塞度；
    步骤4-2：确定航路阻塞度：
    如果阻塞阈值为1，航路阻塞度为1；如果阻塞阈值不为1，依次对每条最优航路穿越路径计算最优航路穿越路径阻塞度，从中选取最大最优航路穿越路径阻塞度作为航路阻塞度。
  9. 根据权利要求8所述的方法，其特征在于，步骤4-1中，路径瓶颈阻塞度计算方法如下：提取路径瓶颈两侧运输盒中气象特征值大于或等于阻塞阈值的运输盒网格，确定各个运输盒网格的中心位置，以运输盒网格中心和路径瓶颈中心之间的纵向距离的倒数为权重，加权平均路径瓶颈两侧运输盒中所有提取的运输盒网格的气象特征值，具体计算公式如下：
    Figure PCTCN2019086516-appb-100012
    式中PB为路径阻塞度，L为路径瓶颈左侧的运输盒，R为路径瓶颈右侧的运输盒，i为路径瓶颈两侧运输盒中的气象特征值大于或等于阻塞阈值的运输盒网格标识，w i为运输盒网格i的气象特征值，d i为运输盒网格i中心与路径瓶颈中心之间的径向距离。
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