A method for comprehensive evaluation of operation efficiency of aircraft task set in low-altitude airspace

Abstract

The application discloses a kind of low airspace in aircraft mission set of airfield comprehensive evaluation method of operation efficiency, its method includes: constructing airspace gridding low airspace grid map;Obtain the flight mission data of all aircraft of airspace grid and according to the aircraft position of research time period corresponding to gather in the airspace grid of low airspace grid map;Airspace grid comprehensive evaluation model includes operation evaluation index system, index evaluation calculation module and dynamic weight adjustment module, and index evaluation calculation module calculates the evaluation value of each index item of airspace grid;Dynamic weight adjustment module is internally provided with the dynamic weight adjustment rule of conflict safety class index and the collaborative adjustment rule of non-security class index;Airspace grid comprehensive evaluation model calculates and obtains the comprehensive evaluation value of airspace grid.The application can realize the dynamic adjustment of index weight of operation evaluation index system based on the total conflict entropy of airspace grid, realizes the scientific evaluation and decision of core safety in airspace grid and efficiency.

Description

Technical Field

[0001] This invention relates to the field of low-altitude airspace assessment, and more particularly to a comprehensive assessment method for the operational efficiency of an aircraft mission set within low-altitude airspace. Background Technology

[0002] Airspace is the specific geographical area within which aircraft (including manned and unmanned aircraft; manned aircraft include passenger planes, transport planes, helicopters, etc.; unmanned aircraft include drones, etc.) operate. With the rapid development of drone logistics and civil aviation passenger and cargo transport, the number of aircraft operating in low-altitude airspace will increase, and the airspace will become increasingly busy. The operational safety and efficiency of each aircraft within low-altitude airspace are crucial indicators for comprehensive evaluation. Ensuring the maximum efficiency of all aircraft operations within the airspace while guaranteeing safety is the current goal of airspace utilization research. Currently, there is no established operational evaluation index system for low-altitude airspace, making it impossible to comprehensively evaluate the operational efficiency of individual low-altitude airspaces. Furthermore, considering conflict safety and non-safety-related factors (primarily efficiency, economy, and profitability) for aircraft operating in low-altitude airspace are also technical challenges for comprehensive low-altitude airspace evaluation. Traditional methods use static or quasi-static weights to set the weights for two types of indicators. However, because aircraft operations in low-altitude airspace are dynamic and highly variable, static weights cannot effectively reflect operational risks. Therefore, even with an operational assessment indicator system, traditional methods can only assess low-altitude airspace according to the indicators in that system and the preset static weights, leading to delays in both the assessment method and results, and potentially impacting low-altitude airspace assessment decisions. In assessment decisions that balance core safety and efficiency in low-altitude airspace operations, traditional static or quasi-static weighting methods are unsuitable. There is an urgent need to research a method that adapts to dynamic weight adjustments for core safety in low-altitude airspace and relies on dynamic weighting strategies for comprehensive operational efficiency assessment. Summary of the Invention

[0003] The purpose of this invention is to provide a comprehensive evaluation method for the operational efficiency of an aircraft mission set in low-altitude airspace. The dynamic weight adjustment module utilizes the total conflict entropy of all aircraft in the low-altitude airspace and sets a conflict entropy threshold range to achieve dynamic adaptive adjustment of weights. It uses dynamic weight adjustment rules for conflict safety indicators to adaptively adjust the weights of conflict safety indicators, and uses collaborative adjustment rules for non-safety indicators to perform collaborative constraint adjustment of non-safety indicators. The airspace grid comprehensive evaluation model, based on the comprehensive evaluation formula, can obtain the comprehensive evaluation value of each airspace grid in the low-altitude airspace and can visualize the spatial distribution on the low-altitude airspace grid map.

[0004] The objective of this invention is achieved through the following technical solution:

[0005] A comprehensive evaluation method for the operational efficiency of aircraft mission sets in low-altitude airspace, the method comprising:

[0006] S1. Construct a gridded low-altitude airspace map, which includes several airspace grids; obtain flight mission data of all aircraft in the airspace grids to construct an aircraft mission dataset.

[0007] S2. Selecting the research time period Flight mission data for each aircraft was obtained from the aircraft mission dataset and organized according to the research time period. The aircraft positions are correspondingly grouped into the airspace grid of the low-altitude airspace grid map.

[0008] S3. Construct a comprehensive evaluation model for the airspace grid. The comprehensive evaluation model for the airspace grid includes an operational evaluation index system, an index evaluation calculation module, and a dynamic weight adjustment module. The operational evaluation index system is constructed according to the correlation between index type, index item, index item evaluation standard, and initial weight of index item. The index types include two types: conflict and security type and non-security type. The index evaluation calculation module calculates the evaluation value of each index item of the airspace grid. The dynamic weight adjustment module internally sets the dynamic weight adjustment rules for conflict and security type indicators and the collaborative adjustment rules for non-security type indicators.

[0009] S4. The comprehensive evaluation value of the spatial grid is calculated using the following formula: :

[0010] ,in The indicator items output by the dynamic weight adjustment module The weight, The indicator items output by the indicator evaluation calculation module The evaluation value, This represents the total number of indicator items.

[0011] To better achieve the present invention, the present invention also includes the following methods:

[0012] S5. Set up a study low-altitude airspace on the low-altitude airspace grid map. The study low-altitude airspace includes several airspace grids; calculate the comprehensive evaluation value of all airspace grids within the study low-altitude airspace according to method S4. And visualize it on a low-altitude airspace grid map.

[0013] A further technical solution is that the present invention also includes the following method:

[0014] S6. Select time periods sequentially according to the timeline. The time series data of the comprehensive evaluation values ​​are obtained by processing the spatial grid in sequence according to methods S2 to S4.

[0015] Preferably, in method S3, the indicators of the operational evaluation index system include low-altitude operational safety P1, low-altitude airspace utilization level P2, low-altitude flight timeliness P3, mission accessibility P4, operational economy P5, and environmental impact level P6. The low-altitude operational safety P1 is a conflict safety indicator, while the low-altitude airspace utilization level P2, low-altitude flight timeliness P3, mission accessibility P4, operational economy P5, and environmental impact level P6 are all non-safety indicators. The index evaluation calculation module calculates the low-altitude operational safety P... The evaluation method for P1 is as follows: The ratio of operational conflict time to total flight time is obtained from the flight mission data of all aircraft in the airspace grid as the operational conflict incidence rate; the attitude angles are obtained from the flight mission data of all aircraft in the airspace grid, and the standard deviation of the attitude angle deviation is calculated, with the reciprocal of the standard deviation used as the attitude stability index; the ratio of the mean vibration acceleration to a set vibration safety threshold is obtained from the flight mission data of all aircraft in the airspace grid as the aircraft vibration intensity; the evaluation value of low-altitude operational safety P1 is calculated according to the following formula. : The operational conflict rate and aircraft vibration intensity are negative indicators, while the attitude stability index is a positive indicator. Both positive and negative indicators are standardized. The data represents the operational conflict rate after standardization using negative indicators. The data represents the attitude stability index after standardization with a positive index. The data represents the aircraft vibration intensity after negative index standardization. , , These are the weighted parameters for operational conflict incidence rate, attitude stability index, and aircraft vibration intensity, respectively.

[0016] Preferably, the indicator evaluation calculation module calculates the evaluation value of the low-altitude airspace utilization level P2. The expression is as follows: ,in The data represents the spatial grid capacity saturation after being standardized using a positive index. The data represents the spatial time utilization rate after standardization using positive indicators. , These are the weights; the evaluation value of the low-altitude flight timeliness P3. The expression is as follows: , The average mission delay ratio for aircraft missions is the data after being standardized using negative indicators. The average ground speed of the aircraft mission is the data after being standardized by a positive index. This is the data of the aircraft mission trajectory deviation after negative index standardization. , , These are the weighting parameters; and the evaluation value of the task reachability P4. The expression is as follows: , The data represents the task completion rate after standardization using positive metrics. The data represents the unplanned return rate after standardization using negative indicators. , These are the weighting parameters; and the evaluation value of the operational economy P5. The expression is as follows: , The data represents energy consumption per unit flight distance after being standardized using negative indicators. The data represents the cost per flight after standardization using negative indicators. , These are the weighting parameters; and the assessment value of the environmental impact level P6. This is an environmental assessment value for noise monitoring.

[0017] Preferably, in method S4, the dynamic weight adjustment module includes a conflict entropy calculation model, the number of indicators of the conflict security class is one, and the preset initial weight of the conflict security class indicator is... The number of non-safety indicators is five, and the preset initial weights are as follows: The dynamic weight adjustment module adjusts the dynamic weights of conflict security-type indicators as follows:

[0018] S41. The conflict entropy calculation model is constructed using spatial proximity, relative motion state, and time urgency as key parameters. In the airspace grid, all aircraft sets are divided into several combinations, with each combination consisting of two aircraft. The key parameter data of spatial proximity, relative motion state, and time urgency of the two aircraft i and j within each combination are obtained and input into the conflict entropy calculation model to calculate the conflict entropy of the aircraft combination. The conflict entropy expression is as follows:

[0019] ,in For the spatial proximity of aircraft i and j, This provides the relative motion state data for aircraft i and j. For time-critical functions, These are normalization parameters;

[0020] S42. Calculate the total conflict entropy of the aircraft set within the airspace grid. The dynamic weight adjustment rule sets the conflict entropy threshold range and adjusts it according to the total conflict entropy. The weight adjustment value is obtained corresponding to the conflict entropy threshold range of the attribution. Then, the research time is obtained by dynamically adjusting according to the following formula. Dynamic weights of conflict security indicators ; ;

[0021] S43. Using dynamic weights for conflict security indicators The initial weights of non-safety indicators are dynamically adjusted according to a coordinated adjustment rule: the sum of the weights of all non-safety indicators is dynamically adjusted to... And adjust dynamically according to the proportion; the constraint of the collaborative adjustment rule is: after dynamic adjustment, the total weight of the conflict-safe class and the non-safe class is 1.

[0022] Preferably, the weight correction value The expression is as follows:

[0023] ,in, Preset baseline or initial weights for conflict security indicators; , , , The conflict entropy thresholds are preset to four levels: low, medium, high, and critical. The conflict entropy threshold range includes... ; , For the weighting increase parameter, ; , It is a smoothing factor; It is a hyperbolic tangent function used to ensure that the weights transition smoothly around the conflict entropy threshold.

[0024] Preferably, the spatial proximity The calculation expression is as follows:

[0025] ,in The three-dimensional distance between aircraft i and j Distance influences the scale factor.

[0026] Preferably, the relative motion state data The calculation expression is as follows:

[0027] ;

[0028] ;

[0029] in The velocity vectors of aircraft i and j are respectively. As a speed difference smoothing factor, It is the angle between the headings of the two aircraft. This is the influence coefficient of the heading angle.

[0030] Preferably, the time-pressure function The method to obtain it is as follows: Pre-set a time-urgent threshold. and , Set a time urgency factor and The time urgency function is obtained by using the following piecewise function expression. :

[0031] , ,in The estimated time for aircraft i and j to reach their closest point. Let be the relative position vector of aircraft i and j. Let be the relative velocity vector between aircraft i and j.

[0032] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0033] (1) The dynamic weight adjustment module of this invention uses the total conflict entropy of all aircraft in the low-altitude airspace and sets the conflict entropy threshold range to realize the dynamic adaptive adjustment of weights. It uses the dynamic weight adjustment rules of conflict safety indicators to adaptively adjust the weights of conflict safety indicators and uses the collaborative adjustment rules of non-safety indicators to perform collaborative constraint adjustment of non-safety indicators. The airspace grid comprehensive evaluation model can obtain the comprehensive evaluation value of each airspace grid in the low-altitude airspace under study based on the comprehensive evaluation formula and can visualize the spatial distribution on the low-altitude airspace grid map. This invention calculates the time series data of the comprehensive evaluation value of each airspace grid in the study according to the time axis order, and combined with the spatial distribution of the comprehensive evaluation value of each airspace grid, it can obtain the spatiotemporal distribution data of the comprehensive evaluation value of the low-altitude airspace under study.

[0034] (2) The dynamic weight adjustment module of this invention obtains the conflict entropy of two aircraft based on the quantitative assessment of inter-aircraft conflict, and then calculates the total conflict entropy of the set of aircraft in the study airspace. It conducts real-time quantitative assessment of the conflict risk in the airspace and dynamically adjusts the weights of conflict safety indicators based on the dynamic weight adjustment rules. When the conflict risk of aircraft operation in the airspace increases, it can adaptively adjust the weights of conflict safety indicators, ensuring the core requirement of safe operation in the airspace. It can also constrain and adjust the two types of non-safety indicators based on the operation assessment indicator system and the preset initial static weights, providing important technical support for the quantitative assessment and decision-making of core safety and efficiency in the airspace.

[0035] (3) The conflict entropy calculation model of this invention calculates the conflict entropy of any two aircraft in the airspace and the total conflict entropy of the set of aircraft under study in the airspace. Based on the total conflict entropy, it creates dynamic weight adjustment rules for conflict safety indicators, realizes the dynamic adjustment mechanism of the weight of conflict safety indicators based on the conflict entropy, realizes the adaptive dynamic adjustment and intelligent weighting based on the conflict safety driven weight, and can realize the dynamic adjustment of the indicator weight of the operation evaluation indicator system based on the total conflict entropy of the airspace grid. It realizes the scientific evaluation and decision-making of core safety and efficiency in the airspace grid. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method for comprehensive evaluation of operational efficiency of the present invention;

[0037] Figure 2 This is a flowchart of the first preferred method in the embodiments;

[0038] Figure 3 This is a flowchart of the second preferred method in the embodiments. Detailed Implementation

[0039] The present invention will be further described in detail below with reference to embodiments:

[0040] Example

[0041] like Figure 1 As shown, a comprehensive evaluation method for the operational efficiency of an aircraft mission set in low-altitude airspace is proposed, the method comprising:

[0042] S1. Construct a gridded low-altitude airspace map, which includes several airspace grids. In practical use, the low-altitude airspace to be studied is set or selected on the low-altitude airspace grid map. The low-altitude airspace to be studied can be the low-altitude airspace corresponding to the take-off and landing airport of low-altitude aircraft, or the low-altitude airspace corresponding to the air logistics corridor of a certain city. Since this invention studies low-altitude airspace (including ultra-low altitude and low altitude; low-altitude airspace is below 1000 meters, generally below 500 meters, and the airspace for light UAVs flying below 120 meters is generally uncontrolled airspace), the aircraft are mainly UAVs flying in low-altitude airspace. Of course, this invention can also be used for the airspace for other aircraft (such as medium and low altitude, high altitude, etc.). The aircraft used in this invention is a logistics drone with an altitude of less than 200 meters (flight altitude ≤ 150 meters, typical speed 5-20 m / s, mainly multi-rotor logistics drones). The low-altitude airspace selected in the low-altitude airspace grid map is the low-altitude airspace corresponding to the air logistics corridor of a certain city. This embodiment describes the method of dividing the low-altitude airspace under study into several airspace grids for comprehensive evaluation of operational efficiency.

[0043] The flight mission data of all aircraft in the airspace grid (including flight mission plans, flight mission execution, and monitoring data of planned and executed flight routes, etc., and the monitoring data of planned and executed flight routes includes weather, noise, environmental data, etc. monitored by the flight mission plans and flight mission execution routes) are used to construct an aircraft mission dataset. The flight mission data in the aircraft mission dataset includes operation conflict time, total aircraft flight time, attitude angle, vibration acceleration, etc.

[0044] S2. Selecting the research time period Flight mission data for each aircraft was obtained from the aircraft mission dataset and organized according to the research time period. The aircraft positions are correspondingly grouped into the airspace grid of the low-altitude airspace grid map.

[0045] S3. Construct a comprehensive airspace grid evaluation model. This model includes an operational evaluation index system, an index evaluation calculation module, and a dynamic weight adjustment module. The operational evaluation index system is constructed by associating index types, index items, index item evaluation standards, and initial weights. Index types include two categories: conflict and security-related and non-security-related. An example from this invention is as follows: There is one index of the conflict and security-related category, and the preset initial weight for the conflict and security-related index is... The number of non-safety indicators is five, and the preset initial weights are as follows: The operational evaluation index system includes the following indicators: low-altitude operational safety (P1), low-altitude airspace utilization level (P2), low-altitude flight timeliness (P3), mission accessibility (P4), operational economy (P5), and environmental impact level (P6). The indicator type for low-altitude operational safety (P1) is conflict safety (example: one indicator type). The indicator types for low-altitude airspace utilization level (P2), low-altitude flight timeliness (P3), mission accessibility (P4), operational economy (P5), and environmental impact level (P6) are all non-safety indicators (example: five non-safety indicators).

[0046] The indicator evaluation calculation module calculates the evaluation values ​​of each indicator item in the airspace grid. In some embodiments, the indicator evaluation calculation module calculates the evaluation value of low-altitude operational safety P1 as follows: The ratio of operational conflict time to total flight time of all aircraft in the airspace grid is obtained as the operational conflict occurrence rate (equivalent to the proportion of conflict time; a lower operational conflict occurrence rate indicates a lower probability of operational conflict). Attitude angles are obtained from the flight mission data of all aircraft in the airspace grid, and the standard deviation of the attitude angle deviation is calculated. The reciprocal of the standard deviation is used as the attitude stability index. The larger the attitude angle deviation (deviation from normal attitude angle) of the aircraft (in this embodiment, a logistics drone), the larger the standard deviation of the aircraft's attitude angle deviation (this invention uses the standard deviation of attitude angle deviation, which more accurately characterizes the degree to which the aircraft deviates from its average attitude angle level). The reciprocal of the standard deviation is used as the attitude stability index (a higher attitude stability index indicates more stable flight control of the drone). The ratio of the mean vibration acceleration to a set vibration safety threshold is obtained from the flight mission data of all aircraft in the airspace grid as the aircraft vibration intensity (used to evaluate the mechanical health status of the drone). The safety assessment value P1 for low-altitude operation is calculated using the following formula. : The operational conflict rate and aircraft vibration intensity are negative indicators, while the attitude stability index is a positive indicator. Both positive and negative indicators are standardized. The data represents the operational conflict rate after standardization using negative indicators. The data represents the attitude stability index after standardization with a positive index. The data represents the aircraft vibration intensity after negative index standardization. The standardization formula for the positive index standardization is as follows: ,in This represents the minimum value in a historical data sequence. This represents the maximum value in a historical data sequence. The result is a standardized result (the standardized result is in the range [0,1]). The values ​​are the original values ​​before standardization (i.e., the current original values ​​at the current research time period or moment), and the historical data sequence is a sequence of indicator data collected over a relatively long period of time; the standardization formula for the negative indicator standardization process is as follows: ,in This represents the minimum value in a historical data sequence. This represents the maximum value in a historical data sequence. The result is a standardized result (the standardized result is in the range [0,1]). The original values ​​before standardization (i.e., the current original values ​​at the current research time period or moment) are the historical data sequences of indicators collected over a longer period of time. , , These are the weighted parameters for operational conflict incidence, attitude stability index, and aircraft vibration intensity, respectively. The assessment value of low-altitude operational safety P1. The historical low-altitude flight safety data was normalized (so that the evaluation value was within the range of 0 to 1).

[0047] The indicator assessment calculation module calculates the assessment value of the low-altitude airspace utilization level P2. The expression is as follows: ,in The data is the airspace grid capacity saturation after being standardized by a positive index. The airspace grid capacity saturation is the ratio of the actual flow of aircraft (in this example, logistics drones) in the airspace grid to the theoretical capacity. The data for spatial time utilization rate has been standardized by positive indices. Spatial time utilization rate is the ratio of the actual usage time of the spatial grid to the available time. , These are the weights, respectively. The evaluation value of P3 for low-altitude flight timeliness. The expression is as follows: , The average delay ratio of aircraft missions is the data after negative index standardization. The average delay ratio is calculated as follows: the absolute value of the difference between the actual flight time of the aircraft (in this example, a logistics drone) and the theoretical shortest time. The ratio of the absolute value to the theoretical shortest time is taken as the average delay ratio of a single flight. The average ground speed of the aircraft mission is the data after being standardized by positive indices, representing the average ground speed of the UAV throughout its entire flight. The trajectory deviation of an aircraft mission is standardized by a negative index, representing the ratio of the actual flight path length of the aircraft (in this example, a logistics drone) to the planned flight path length. , , These are the weighting parameters; and the evaluation value of task reachability P4. The expression is as follows: , The task completion rate is the data after being standardized by positive indicators, which is the ratio of the number of tasks completed by aircraft (in this example, logistics drones) within the airspace grid to the total number of tasks executed. The unplanned return rate is the data after being standardized by negative indicators. It represents the percentage of times an aircraft (in this example, a logistics drone) within the airspace grid fails to arrive at its destination and return to its take-off and landing point as planned. , These are the weighting parameters. The evaluation value of operational economy P5. The expression is as follows: , This is the energy consumption per unit distance after negative index standardization processing; it represents the ratio of the cumulative energy consumed by the aircraft (in this example, a logistics drone) during its flight to the total flight distance. The cost per flight, after being standardized by a negative indicator, is the ratio of the total operating cost of the aircraft (in this example, a logistics drone) to the total number of flights. , These are the weighting parameters; the assessment value of environmental impact level P6. The noise monitoring environmental assessment value is derived based on the monitored noise level; this value has been standardized using negative indicators. The standardized formulas for each of the above positive indicators are as follows: ,in This represents the minimum value in a historical data sequence. This represents the maximum value in a historical data sequence. The result is a standardized result (the standardized result is in the range [0,1]). The values ​​are the original values ​​before standardization (i.e., the current original values ​​at the current research time period or moment), and the historical data series are the indicator data series collected over a relatively long period of time. The standardization formula for each of the above negative indicators is as follows: ,in This represents the minimum value in a historical data sequence. This represents the maximum value in a historical data sequence. The result is a standardized result (the standardized result is in the range [0,1]). The values ​​are the original values ​​before standardization (i.e., the current original values ​​for the current research period or moment), while the historical data series are indicator data sequences collected over a longer historical period. The aforementioned assessment values ​​for low-altitude airspace utilization level P2... The evaluation value of P3 for low-altitude flight timeliness. Task reachability P4 evaluation value The evaluation value of operating economy P5 Environmental impact level P6 assessment value Normalize each value using its corresponding historical evaluation value (so that each evaluation value is within the range of 0 to 1).

[0048] The dynamic weight adjustment module internally includes dynamic weight adjustment rules for conflict security indicators and collaborative adjustment rules for non-security indicators. In some embodiments, the dynamic weight adjustment module includes a conflict entropy calculation model, with one conflict security indicator type and a preset initial weight for the conflict security indicator. The number of non-safety indicators is five, and the preset initial weights are as follows: The dynamic weight adjustment module adjusts the dynamic weights of conflict-related security indicators as follows:

[0049] S41. The conflict entropy calculation model is constructed using spatial proximity, relative motion state, and time urgency as key parameters. In the airspace grid, all aircraft sets are divided into several aircraft combinations, with each combination consisting of two aircraft. The key parameter data of spatial proximity, relative motion state, and time urgency of the two aircraft i and j in each combination are obtained and input into the conflict entropy calculation model to calculate the conflict entropy of the aircraft combination. This invention uses conflict entropy to measure the intensity of potential conflict risk between two aircraft i and j (the conflict risk intensity is a comprehensive measure describing the intensity of potential conflict risk between aircraft due to their relative motion state within a specific spatiotemporal range). In some embodiments, the conflict entropy of two aircraft i and j is a continuous risk measurement function formed by nonlinear coupling of spatial proximity, relative motion state, and time urgency. The conflict entropy calculation model yields the following expression for the conflict entropy of two aircraft i and j:

[0050] ,in For the spatial proximity of aircraft i and j, This provides the relative motion state data for aircraft i and j. For time-critical functions, This is a normalized parameter. Preferably, spatial proximity. The calculation expression is as follows:

[0051] ,in The three-dimensional distance between aircraft i and j Distance influence scale factor According to distance (i.e., distance) (Preset factors)

[0052] Relative motion state data The calculation expression is as follows:

[0053] ;

[0054] ;

[0055] in The velocity vectors of aircraft i and j are respectively. As a speed difference smoothing factor, It is the angle between the headings of the two aircraft. This is the heading angle influence coefficient (used to adjust the contribution weight of heading differences to risk).

[0056] Time-pressure function The method to obtain it is as follows: Pre-set a time-urgent threshold. and , Set a time urgency factor and Time urgency coefficient This is a coefficient for emergency situations, specifically a time urgency coefficient. The coefficients are in the relaxed state; the time-pressure function is obtained according to the following piecewise function expression. :

[0057] ,in A state of emergency. It is in an adjacent state. To ease the situation.

[0058] ,in The estimated time for aircraft i and j to reach their closest point (used to describe the time when two aircraft i and j reach the closest distance to each other). Let be the relative position vector of aircraft i and j (used to describe the relative position of aircraft i with respect to aircraft j). Let be the relative velocity vector between aircraft i and j (used to describe the relative velocity of aircraft i relative to aircraft j). Let be the square of the magnitude of the relative velocity vector between aircraft i and j.

[0059] S42. Calculate the total conflict entropy of the aircraft set within the airspace grid. Total conflict entropy The expression is as follows: , Let i and j form the conflict entropy of an aircraft combination. The aircraft type influence factor (determined by analyzing historical accident symptom statistics) is used to define the aircraft type influence factor for aircraft i and j forming an aircraft combination. If the number of aircraft in the aircraft combination under study is N, then... .

[0060] The dynamic weight adjustment rule sets the conflict entropy threshold range and adjusts it according to the total conflict entropy. The weight adjustment value is obtained corresponding to the conflict entropy threshold range of the attribution. Then, the research time is obtained by dynamically adjusting according to the following formula. Dynamic weights of conflict security indicators ; , The initial static weights are preset for conflict safety indicators. These conflict safety indicators are for ensuring aircraft safety, and changes in their weights should be given absolute attention. In aircraft operations, ensuring aircraft safety is the absolute primary indicator. In conflict safety scenarios, changes in the weights of conflict safety indicators are considered first. These changes should be absolute, significant, and directly related to the risk intensity. Non-safety indicators are efficiency indicators that are implemented while ensuring conflict safety, and are used to study the timing of events. Calculate the corresponding weight adjustment value Calculate the dynamic weights As a research moment The weights of conflict security indicators, research time The sum of the weights of all non-safety indicators is dynamically adjusted to .

[0061] In some embodiments, the weight adjustment value of conflict security indicators The expression is as follows:

[0062] ,in, Preset baseline weights or initial weights (also known as initial static weights) for conflict security indicators. It can be abbreviated as H. It can be abbreviated as ; , , , The conflict entropy thresholds are preset to four levels: low, medium, high, and critical. The conflict entropy threshold range includes... , This can be defined as the low conflict entropy threshold range. It can be defined as the threshold range of medium-level conflict entropy. It can be defined as the high conflict entropy threshold range. , These are the weighting increment parameters, corresponding to the weighting jump magnitudes from medium to high risk and from high to critical risk, respectively. , As a smoothing factor, It is a hyperbolic tangent function used to ensure that the weights transition smoothly around the conflict entropy threshold.

[0063] S43. Using dynamic weights for conflict security indicators The initial weights of non-safety indicators are dynamically adjusted according to a coordinated adjustment rule: the sum of the weights of all non-safety indicators is dynamically adjusted to... And adjust dynamically according to the proportion; the constraint of the collaborative adjustment rule is: after dynamic adjustment, the total weight of the conflict-safe class and the non-safe class is 1.

[0064] S4. The comprehensive evaluation value of the spatial grid is calculated using the following formula: :

[0065] ,in The indicator items output by the dynamic weight adjustment module The weight, The indicator items output by the indicator evaluation calculation module The evaluation value, This represents the total number of indicator items.

[0066] This embodiment takes a city's air logistics corridor as the research object. The airspace of this logistics corridor experiences operational changes from off-peak to peak hours between 14:00 and 14:30 on a certain afternoon. All aircraft within the airspace of the logistics corridor (the aircraft in the urban air logistics corridor are logistics drones, with a flight altitude ≤150 meters, typical speed of 5-20 m / s, and mainly multi-rotor logistics drones) are selected as the research aircraft set. In the operational indicator system, conflict safety indicators and non-safety indicators correspond to preset initial static weights (see Tables 1 and 2 above). In the case of a city's air logistics corridor as the research object, its parameter configuration is shown in Table 3 below:

[0067]

[0068] The system goes through three phases: a stable operation phase, a risk-increasing phase, and a high-risk conflict phase (i.e., a crisis state). The adaptive adjustment methods for the weights of the conflict safety and non-safety indicators in these three phases are as follows:

[0069] Stable operation phase: At 14:00 in the afternoon, the average distance between UAVs in the study airspace was greater than 400m, the headings were relatively stable, and there were no adverse weather conditions. The total conflict entropy was calculated. , , At this point, the security weights remain at the baseline value (i.e., the initial static weights), and all indicator weights remain consistent with the initial weights. Some of the initial weights in this embodiment are shown in Table 2:

[0070]

[0071] Then, the indicator evaluation calculation module calculates the evaluation value of the low-altitude airspace utilization level P2. Assessment value of low-altitude airspace utilization level P2 The evaluation value of P3 for low-altitude flight timeliness. Task reachability P4 evaluation value The evaluation value of operating economy P5 Environmental impact level P6 assessment value After normalization, the indicator items output by the dynamic weight adjustment module With the weights assigned (the dynamic weights are the initial weights during the stable operation phase), the spatial grid comprehensive evaluation model can calculate the comprehensive evaluation value of the spatial grid during the stable operation phase. .

[0072] Risk Escalation Phase: At 14:13, multiple drones arrived at the convergence point in the study airspace, forming a temporary convergence (i.e., a convergence state); three drones (A, B, and C) posed a risk of conflict. A and B were 180m apart horizontally and flying towards each other, while B and C had a vertical height difference of 30m and a heading angle of approximately 70°; the calculated total conflict entropy... , , , ;at this time, Entering a high-risk state, security weight Significantly increased, the sum of the weights of all non-safety indicators is dynamically adjusted to The weights are then dynamically adjusted proportionally (i.e., reduced proportionally according to the initial weights in Table 2); subsequently, the indicator evaluation calculation module calculates the evaluation value of the low-altitude airspace utilization level P2. Assessment value of low-altitude airspace utilization level P2 The evaluation value of P3 for low-altitude flight timeliness. Task reachability P4 evaluation value The evaluation value of operating economy P5 Environmental impact level P6 assessment value After normalization, the indicator items output by the dynamic weight adjustment module The weights (during the risk-increasing phase, the dynamic weight adjustment module adjusts the weights according to the dynamic weight adjustment rules for conflict security indicators and the coordinated adjustment rules for non-security indicators) are used to calculate the comprehensive evaluation value of the airspace grid during the stable operation phase. .

[0073] High-risk conflict phase (i.e., critical state): 14:22, the estimated time when drone B and drone A arrive at their closest point. The time was shortened to 35 seconds, the three-dimensional distance was reduced to 120 meters, and the relative speed was 18 m / s; the total conflict entropy was calculated. , , , ;at this time, Entering a critical state, the weight of safety is further increased, and the research airspace enters a mode where safety is paramount; the sum of the weights of all non-safety indicators is dynamically adjusted to... And adjust dynamically according to the corresponding proportions. During the stable operation phase, the dynamic weights of conflict safety indicators... The initial static weight is 0.187; the dynamic weight of conflict security indicators during the risk escalation phase. The weights of all non-safety indicators are dynamically adjusted to 0.402. And adjust them dynamically according to the corresponding proportions. During high-risk conflict phases, the dynamic weights of conflict security indicators... The weights of all non-safety indicators are dynamically adjusted to 0.562, and the sum of their weights is also dynamically adjusted to... The weights are then dynamically adjusted proportionally (i.e., reduced proportionally according to the initial weights in Table 2); subsequently, the indicator evaluation calculation module calculates the evaluation value of the low-altitude airspace utilization level P2. Assessment value of low-altitude airspace utilization level P2 The evaluation value of P3 for low-altitude flight timeliness. Task reachability P4 evaluation value The evaluation value of operating economy P5 Environmental impact level P6 assessment value After normalization, the indicator items output by the dynamic weight adjustment module The weights (in high-risk conflict phases, the dynamic weight adjustment module adjusts the weights according to the dynamic weight adjustment rules for conflict security indicators and the coordinated adjustment rules for non-security indicators) are used to calculate the comprehensive evaluation value of the airspace grid during the stable operation phase. .

[0074] In some embodiments, such as Figure 2As shown, this invention can be applied not only to a single spatial grid but also to the study of low-altitude airspace comprising several spatial grids. The method further includes:

[0075] S5. Set up a low-altitude airspace for study on a low-altitude airspace grid map. The low-altitude airspace for study includes several airspace grids. Calculate the comprehensive evaluation value of all airspace grids within the low-altitude airspace for study according to the method S4 of this invention. And visualize it on a low-altitude airspace grid map.

[0076] In some embodiments, such as Figure 2 As shown, in addition to calculating and comparing comprehensive evaluation values ​​for spatial distribution based on the research of low-altitude airspace, this invention can also calculate and compare comprehensive evaluation values ​​before and after a certain time according to the timeline. The method further includes:

[0077] S6. Select time periods sequentially according to the timeline. According to the method of the present invention, S2 to S4 are processed sequentially to obtain the time series data of the comprehensive evaluation value of the airspace grid arranged by time. Thus, the time series data of the comprehensive evaluation value before and after the airspace grid can be obtained. The low-altitude airspace to be studied is set in the low-altitude airspace grid map. The low-altitude airspace to be studied includes several airspace grids. According to the above method, the time series data of the comprehensive evaluation value of all airspace grids in the low-altitude airspace to be studied can be calculated. Thus, the spatiotemporal distribution data of the comprehensive evaluation value of the low-altitude airspace to be studied can be obtained.

[0078] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for the operational efficiency of an aircraft mission set in low-altitude airspace, characterized in that: The methods include: S1. Construct a gridded low-altitude airspace map, which includes several airspace grids; acquire flight mission data of all aircraft in the airspace grids to construct an aircraft mission dataset; S2. Selecting the research period Flight mission data for each aircraft was obtained from the aircraft mission dataset and organized according to the research time period. The aircraft positions are correspondingly grouped into the airspace grid of the low-altitude airspace grid map; S3. Construct a comprehensive evaluation model for the airspace grid. The comprehensive evaluation model for the airspace grid includes an operational evaluation index system, an index evaluation calculation module, and a dynamic weight adjustment module. The operational evaluation index system is constructed according to index type, index item, index item evaluation standard, and initial weight association of index item. The index types include two types: conflict and security type and non-security type. The index evaluation calculation module calculates the evaluation value of each index item of the airspace grid. The dynamic weight adjustment module internally sets dynamic weight adjustment rules for conflict and security type indicators and collaborative adjustment rules for non-security type indicators. S4. The comprehensive evaluation value of the spatial grid is calculated using the following formula: : ,in The indicator items output by the dynamic weight adjustment module The weight, The indicator items output by the indicator evaluation calculation module The evaluation value, This represents the total number of indicator items; the dynamic weight adjustment module adjusts the dynamic weights of indicators of the conflict and security category as follows: S41. The conflict entropy calculation model is constructed using spatial proximity, relative motion state, and time urgency as key parameters. In the airspace grid, all aircraft sets are divided into several combinations, with each combination consisting of two aircraft. The key parameter data of spatial proximity, relative motion state, and time urgency of the two aircraft i and j within each combination are obtained and input into the conflict entropy calculation model to calculate the conflict entropy of the aircraft combination. The conflict entropy expression is as follows: ,in For the spatial proximity of aircraft i and j, This provides the relative motion state data for aircraft i and j. For time-critical functions, These are normalization parameters; S42. Calculate the total conflict entropy of the aircraft set within the airspace grid. The dynamic weight adjustment rule sets the conflict entropy threshold range and adjusts it according to the total conflict entropy. The weight adjustment value is obtained corresponding to the conflict entropy threshold range of the attribution. Then, the research time is obtained by dynamically adjusting according to the following formula. Dynamic weights of conflict security indicators ; , Initial weights are preset for conflict security indicators; S43. Using dynamic weights for conflict security indicators The initial weights of non-safety indicators are dynamically adjusted according to a coordinated adjustment rule: the sum of the weights of all non-safety indicators is dynamically adjusted to... And adjust dynamically according to the proportion; the constraint of the collaborative adjustment rule is: after dynamic adjustment, the total weight of the conflict-safe class and the non-safe class is 1.

2. The method for comprehensive evaluation of the operational efficiency of an aircraft mission set in low-altitude airspace according to claim 1, characterized in that: It also includes the following methods: S5. Set up a study low-altitude airspace on the low-altitude airspace grid map. The study low-altitude airspace includes several airspace grids; calculate the comprehensive evaluation value of all airspace grids within the study low-altitude airspace according to method S4. And visualize it on a low-altitude airspace grid map.

3. A comprehensive evaluation method for the operational efficiency of an aircraft mission set in low-altitude airspace according to claim 1 or 2, characterized in that: It also includes the following methods: S6. Select time periods sequentially according to the timeline. The time series data of the comprehensive evaluation values ​​are obtained by processing the spatial grid in sequence according to methods S2 to S4.

4. The method for comprehensive evaluation of the operational efficiency of an aircraft mission set in low-altitude airspace according to claim 1, characterized in that: In method S3, the operational evaluation index system includes the following index items: low-altitude operational safety P1, low-altitude airspace utilization level P2, low-altitude flight timeliness P3, mission accessibility P4, operational economy P5, and environmental impact level P6. The low-altitude operational safety P1 index type is conflict safety type, while the low-altitude airspace utilization level P2, low-altitude flight timeliness P3, mission accessibility P4, operational economy P5, and environmental impact level P6 are all non-safety type. The index evaluation calculation module calculates the evaluation value of low-altitude operational safety P1 as follows: The ratio of operational conflict time to total aircraft flight time is obtained from the flight mission data of all aircraft in the airspace grid as the operational conflict occurrence rate; the attitude angles are obtained from the flight mission data of all aircraft in the airspace grid, and the standard deviation of the attitude angle deviation is calculated, with the reciprocal of the standard deviation used as the attitude stability index; the ratio of the mean vibration acceleration to a set vibration safety threshold is obtained from the flight mission data of all aircraft in the airspace grid as the aircraft vibration intensity; the evaluation value of low-altitude operational safety P1 is calculated according to the following formula. : The operational conflict rate and aircraft vibration intensity are negative indicators, while the attitude stability index is a positive indicator. Both positive and negative indicators are standardized. The data represents the operational conflict rate after standardization using negative indicators. The data represents the attitude stability index after standardization with a positive index. The data represents the aircraft vibration intensity after negative index standardization. , , These are the weighted parameters for operational conflict incidence rate, attitude stability index, and aircraft vibration intensity, respectively.

5. The method for comprehensive evaluation of the operational efficiency of an aircraft mission set in low-altitude airspace according to claim 4, characterized in that: The indicator evaluation calculation module calculates the evaluation value of the low-altitude airspace utilization level P2. The expression is as follows: ,in The data represents the spatial grid capacity saturation after being standardized using a positive index. The data represents the spatial time utilization rate after standardization using positive indicators. , These are the weights; the evaluation value of the low-altitude flight timeliness P3. The expression is as follows: , The average mission delay ratio for aircraft missions is the data after being standardized using negative indicators. The average ground speed of the aircraft mission is the data after being standardized by a positive index. This is the data of the aircraft mission trajectory deviation after negative index standardization. , , These are the weighting parameters; and the evaluation value of the task reachability P4. The expression is as follows: , The data represents the task completion rate after standardization using positive metrics. The data represents the unplanned return rate after standardization using negative indicators. , These are the weighting parameters; and the evaluation value of the operational economy P5. The expression is as follows: , The data represents energy consumption per unit flight distance after being standardized using negative indicators. The data represents the cost per flight after standardization using negative indicators. , These are the weighting parameters; and the assessment value of the environmental impact level P6. This is an environmental assessment value for noise monitoring.

6. The method for comprehensive evaluation of the operational efficiency of an aircraft mission set in low-altitude airspace according to claim 1, characterized in that: In method S4, the dynamic weight adjustment module includes a conflict entropy calculation model, and the number of indicators of the conflict security class is one. The initial weight of the conflict security class indicator is preset to be... The number of non-safety indicators is five, and the preset initial weights are as follows: .

7. The method for comprehensive evaluation of the operational efficiency of an aircraft mission set in low-altitude airspace according to claim 6, characterized in that: The weight correction value The expression is as follows: ,in , , , The conflict entropy thresholds are preset to four levels: low, medium, high, and critical. The conflict entropy threshold range includes... ; , For the weighting increase parameter, ; , It is a smoothing factor; It is a hyperbolic tangent function used to ensure that the weights transition smoothly around the conflict entropy threshold.

8. The method for comprehensive evaluation of the operational efficiency of an aircraft mission set in low-altitude airspace according to claim 6, characterized in that: spatial proximity The calculation expression is as follows: ,in The three-dimensional distance between aircraft i and j Distance influences the scale factor.

9. The method for comprehensive evaluation of the operational efficiency of an aircraft mission set in low-altitude airspace according to claim 6, characterized in that: The relative motion state data The calculation expression is as follows: ； ； in The velocity vectors of aircraft i and j are respectively. As a speed difference smoothing factor, It is the angle between the headings of the two aircraft. This is the influence coefficient of the heading angle.

10. The method for comprehensive evaluation of the operational efficiency of an aircraft mission set in low-altitude airspace according to claim 6, characterized in that: The time-urgent function The method to obtain it is as follows: Pre-set a time-urgent threshold. and , Set a time urgency factor and The time urgency function is obtained by using the following piecewise function expression. : , ,in The estimated time for aircraft i and j to reach their closest point. Let be the relative position vector of aircraft i and j. Let be the relative velocity vector between aircraft i and j.

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