Weight self-adaptive adjustment processing method based on inter-aircraft conflict quantification evaluation
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA ACAD OF CIVIL AVIATION SCI & TECH
- Filing Date
- 2026-01-22
- Publication Date
- 2026-06-23
Smart Images
Figure CN121998335B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of dynamic adjustment of the weights of safety and non-safety indicators in the research airspace of aircraft, and particularly to a weight adaptive adjustment method based on quantitative assessment of inter-aircraft conflicts. 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 in airspace will increase, and the airspace will become increasingly busy. The operation of aircraft within airspace presents different safety risks at different times (mainly due to the increased risk from aircraft congregating within airspace). The current goal of airspace use research is to maximize the efficiency of all aircraft operations within airspace while ensuring safety. Therefore, how to consider conflict safety and non-safety factors (primarily efficiency, economy, and profitability) for aircraft within airspace is a technical challenge facing airspace use. The goal is to achieve a situation where the core consideration is the safe operation of aircraft within airspace, while also better adapting to non-safety considerations—that is, prioritizing aircraft safety while also taking other non-safety indicators into account. To achieve a comprehensive safety and efficiency assessment that prioritizes the safe operation of aircraft within airspace while also considering other non-safety indicators, both conflict-related and non-safety-related indicators need to be effectively evaluated. Traditional methods use static or quasi-static weighting for these two categories. However, given the dynamic and ever-changing nature of aircraft operations within airspace, static weighting fails to effectively reflect operational risks, leading to outdated assessment methods and results, and even impacting core safety decisions. In the assessment and decision-making process for core safety while also considering efficiency within airspace, traditional static or quasi-static weighting methods are unsuitable. There is an urgent need to research and develop dynamic weighting methods adapted for core safety within airspace, enabling dynamic adjustment of weights to meet the core requirements of safe aircraft operation and ensuring the fundamental basis for safe operation within airspace. Summary of the Invention
[0003] The purpose of this invention is to provide a weight adaptive adjustment processing method based on quantitative assessment of inter-aircraft conflict. This method obtains the conflict entropy of two aircraft based on the quantitative assessment of inter-aircraft conflict, then calculates the total conflict entropy of the set of aircraft within the study airspace. It provides real-time quantitative assessment of conflict risk within the airspace and dynamically adjusts the weights of conflict safety indicators based on dynamic weight adjustment rules. When the conflict risk of aircraft operations within the airspace increases, it can adaptively adjust the weights of conflict safety indicators, ensuring the core requirement of safe operation within the airspace. Furthermore, it can constrain and adjust two types of non-safety indicators based on the operational indicator system and preset initial static weights, providing important technical support for the quantitative assessment and decision-making of core safety within the airspace while also considering efficiency.
[0004] The objective of this invention is achieved through the following technical solution:
[0005] An adaptive weight adjustment method based on quantitative assessment of inter-aircraft conflict, comprising:
[0006] S1. Construct a conflict entropy calculation model with spatial proximity, relative motion state, and time urgency as key parameters. Obtain key parameter data of two aircraft i and j in the study airspace and input them into the conflict entropy calculation model to calculate the conflict entropy of the two aircraft i and j.
[0007] S2. Within the study airspace, select a set of aircraft for research. The conflict entropy calculation model divides the set of aircraft into several combinations of two aircraft each, and calculates the total conflict entropy of all aircraft combinations within the study airspace at the time of study. .
[0008] S3. Construct a collaborative constraint dynamic weight model that includes an operational indicator system. The operational indicator system includes two categories of indicators: conflict safety and non-safety, each corresponding to a preset initial static weight. The sum of all preset initial static weights is 1. The collaborative constraint dynamic weight model sets dynamic weight adjustment rules for conflict safety indicators. These dynamic weight adjustment rules set a conflict entropy threshold range and adjust the weights 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 preset for conflict security indicators.
[0009] To better realize this invention, in method S3, a collaborative constraint dynamic weight model is used to study the time step. Dynamic weights of conflict security indicators The initial static weights of the non-safety class indicators at the research time are dynamically adjusted in a coordinated manner. The coordinated dynamic adjustment constraint rule of the coordinated constraint dynamic weight model is: after dynamic adjustment, the conflict safety class and the non-safety class at the research time... The total weight is 1.
[0010] Preferably, the conflict and security categories of the operational indicator system are constructed according to a hierarchical indicator architecture. The conflict and security category indicators include several primary indicators, and each primary indicator of the conflict and security category includes several secondary indicators at the next level. All secondary indicators of the conflict and security category indicators correspond to preset initial static weights at the secondary level. The primary indicators of the conflict and security category indicators are the sum of the preset initial static weights of all secondary indicators at the hierarchical level. The non-security category indicators include several primary indicators, and each primary indicator includes several secondary indicators at the next level. The primary indicators of the non-security category indicators are the sum of the preset initial static weights of all secondary indicators at the hierarchical level. The collaborative constraint dynamic weight model is based on the research time of the conflict and security category indicators. Dynamic weights Within the hierarchy of conflict security indicators, adjustments are made sequentially and collaboratively; the collaborative constraint dynamic weight model is applied at the research time. Non-safety indicators are adjusted sequentially and collaboratively within their respective hierarchical levels.
[0011] Preferably, in method S1, the conflict entropy calculation model for the two aircraft i and j is expressed as follows:
[0012] ,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 the normalized parameters.
[0013] Preferably, spatial proximity The calculation expression is as follows:
[0014] ,in The three-dimensional distance between aircraft i and j Distance influences the scale factor.
[0015] Preferably, relative motion state data The calculation expression is as follows:
[0016]
[0017]
[0018] 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.
[0019] 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. :
[0020] , ,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.
[0021] Preferably, in method S2, all aircraft records within the study airspace are selected as the study aircraft set. The conflict entropy calculation model divides the study aircraft set into several aircraft combinations, each aircraft combination including two aircraft, and calculates the time of study for each aircraft combination. The conflict entropy will be studied in the context of the study of the airspace's intrinsic research moments. The total conflict entropy is obtained by aggregating the various aircraft combinations within the research aircraft set. .
[0022] Preferably, the weight correction value The expression is as follows:
[0023] ,in, Preset baseline weights or initial static 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 number of conflict security category indicators is one, and the preset initial static weight of the conflict security category indicator is [value missing]. The number of non-safety indicators is m-1, and the preset initial static weights are as follows: ; to study the moment 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 .
[0025] Compared with the prior art, the present invention has the following advantages and beneficial effects:
[0026] (1) 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 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 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.
[0027] (2) 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, and realizes the adaptive dynamic adjustment and intelligent weighting based on the conflict safety driven weight. This is conducive to promoting the scientific assessment and decision-making of core security in the airspace while taking into account efficiency. Attached Figure Description
[0028] Figure 1 This is a flowchart of the weight adaptive adjustment processing method of the present invention. Detailed Implementation
[0029] The present invention will be further described in detail below with reference to embodiments:
[0030] Example
[0031] like Figure 1 As shown, a weight adaptive adjustment method based on quantitative assessment of inter-aircraft conflict is proposed, the method comprising:
[0032] S1. Construct a conflict entropy calculation model with spatial proximity, relative motion state, and time urgency as key parameters. Obtain key parameter data (including key parameter data corresponding to spatial proximity, relative motion state, and time urgency) of two aircraft i and j in the study airspace and input them into the conflict entropy calculation model to calculate the conflict entropy of the two aircraft i and j. 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:
[0033] ,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:
[0034] ,in The three-dimensional distance between aircraft i and j Distance influence scale factor According to distance (i.e., distance) (Preset factors)
[0035] Relative motion state data The calculation expression is as follows:
[0036]
[0037]
[0038] 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).
[0039] 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. :
[0040] ,in A state of emergency. It is in an adjacent state. To ease the situation.
[0041] ,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.
[0042] S2. Select a set of aircraft for study within the study airspace. This can be done by selecting a subset of key aircraft (e.g., aircraft whose routes pose a potential risk within the study airspace) or by selecting all aircraft within the study airspace. The conflict entropy calculation model divides the set of aircraft into several combinations, with each combination consisting of two aircraft. If the number of aircraft in the set is N, then the number of aircraft combinations is... The total conflict entropy of all aircraft combinations within the study airspace at the study time was calculated. In some embodiments, the 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... .
[0043] In some embodiments, all aircraft (or a subset of key aircraft) within the study airspace are selected and recorded as the study aircraft set. The conflict entropy calculation model divides the study aircraft set into several aircraft combinations, each consisting of two aircraft, and calculates the time of study for each aircraft combination. The conflict entropy will be studied in the context of the study of the airspace's intrinsic research moments. The total conflict entropy is obtained by aggregating the various aircraft combinations within the research aircraft set. .
[0044] S3. Construct a collaborative constraint dynamic weight model incorporating an operational indicator system. The operational indicator system includes two categories: conflict-safe and non-safe indicators, each corresponding to a preset initial static weight. The sum of all preset initial static weights is 1. Preferably, the conflict-safe and non-safe indicators in the operational indicator system are constructed according to a hierarchical indicator structure. Conflict-safe indicators include several primary indicators, and each primary indicator's next level includes several secondary indicators. All secondary indicators of the conflict-safe indicators correspond to preset secondary initial static weights. The primary indicators of the conflict-safe indicators are the sum of the preset secondary initial static weights of all secondary indicators under the hierarchical level. Non-safe indicators include several primary indicators, and each primary indicator's next level includes several secondary indicators. The primary indicators of the non-safe indicators are the sum of the preset secondary initial static weights of all secondary indicators under the hierarchical level. The collaborative constraint dynamic weight model is based on the conflict-safe indicator research time. Dynamic weights Within the hierarchy of conflict security indicators, adjustments are made sequentially and collaboratively. The collaborative constraint dynamic weight model applies to the research time. Non-safety indicators are adjusted sequentially and collaboratively within their respective hierarchical levels.
[0045] In some embodiments, the conflict safety index of the present invention is an indicator to ensure the safe operation of aircraft. In this embodiment, the number of conflict safety indicators is one (defined as low-altitude operational safety). The preset initial static weight of the conflict safety index is... The next level of safety for low-altitude operations includes operational conflict rate, attitude stability index, and aircraft vibration intensity, as shown in Table 1 below:
[0046]
[0047] The number of non-safety indicators is m-1, and the preset initial static weights are as follows: The conflict safety indicators of this invention are indicators to ensure the safe operation of aircraft, while the non-safety indicators are efficiency indicators under the premise of ensuring the conflict safety indicators. This embodiment exemplifies five non-safety indicators, namely, primary indicators such as low-altitude airspace utilization level, low-altitude flight timeliness, mission accessibility, operational economy, and environmental impact level. Each primary indicator includes several secondary indicators at the next level. Examples of non-safety indicators are shown in Table 2 below:
[0048]
[0049] The initial static weight of a certain primary indicator is the sum of the initial static weights of all secondary indicators under that primary indicator level. Taking the primary indicator of low-altitude flight timeliness as an example... .
[0050] The collaborative constraint dynamic weight model sets dynamic weight adjustment rules for conflict safety indicators. These rules define conflict entropy threshold ranges and adjust the weights 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 .
[0051] In some embodiments, the weight adjustment value of conflict security category indicators The expression is as follows:
[0052] ,in, The preset baseline weights or initial static weights (referred to as static weights or baseline 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.
[0053] This invention uses a collaborative constraint dynamic weight model to study time... Dynamic weights of conflict security indicators The initial static weights of non-safety indicators at the research time are dynamically adjusted in a coordinated manner, specifically as follows: based on the research time... 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 The collaborative dynamic adjustment constraint rule of the collaborative constraint dynamic weight model is as follows: After dynamic adjustment, the conflict-safe class and the unsafe class at the research time... The total weight is 1.
[0054] 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:
[0055]
[0056] 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:
[0057] 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 weight remains at the baseline value (i.e., the initial static weight), and all indicator weights remain consistent with the static weights (i.e., the initial static weights).
[0058] 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 And adjust dynamically according to the corresponding proportion.
[0059] 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... And adjust dynamically according to the corresponding proportion.
[0060] The above description is only a preferred embodiment of the present invention and is 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 weight adaptive adjustment method based on quantitative assessment of inter-aircraft conflict, characterized in that: The methods include: S1. Construct a conflict entropy calculation model with spatial proximity, relative motion state, and time urgency as key parameters. Obtain key parameter data of two aircraft i and j in the study airspace and input them into the conflict entropy calculation model to calculate the conflict entropy of the two aircraft i and j. S2. Within the study airspace, select a set of aircraft for research. The conflict entropy calculation model divides the set of aircraft into several combinations of two aircraft each, and calculates the total conflict entropy of all aircraft combinations within the study airspace at the time of study. ; S3. Construct a collaborative constraint dynamic weight model that includes an operational indicator system. The operational indicator system includes two categories of indicators: conflict safety and non-safety, each corresponding to a preset initial static weight. The sum of all preset initial static weights is 1. The collaborative constraint dynamic weight model sets dynamic weight adjustment rules for conflict safety indicators. These dynamic weight adjustment rules set a conflict entropy threshold range and adjust the weights 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 ; , Preset baseline weights or initial static weights for conflict security indicators.
2. The weight adaptive adjustment processing method based on inter-aircraft conflict quantitative assessment according to claim 1, characterized in that: In method S3, a collaboratively constrained dynamic weight model is used to study the time step. Dynamic weights of conflict security indicators For research time The initial static weights of non-safety class indicators are dynamically adjusted in a coordinated manner. The coordinated dynamic adjustment constraint rule of the coordinated constraint dynamic weight model is: after dynamic adjustment, the conflict safety class and non-safety class at the research time... The total weight is 1.
3. The weight adaptive adjustment processing method based on inter-aircraft conflict quantitative assessment according to claim 2, characterized in that: The operational indicator system comprises two categories of indicators: conflict and security, and non-security. These are constructed according to a hierarchical indicator structure. The conflict and security category includes several primary indicators, each with several secondary indicators at the next level. All secondary indicators of the conflict and security category correspond to preset initial static weights at the secondary level. The primary indicators of the conflict and security category are the sum of the preset initial static weights of all secondary indicators at the hierarchical level. Similarly, the non-security category includes several primary indicators, each with several secondary indicators at the next level. The primary indicators of the non-security category are the sum of the preset initial static weights of all secondary indicators at the hierarchical level. The collaborative constraint dynamic weight model is based on the research time of the conflict and security category indicators. Dynamic weights Within the hierarchy of conflict security indicators, adjustments are made sequentially and collaboratively; the collaborative constraint dynamic weight model is applied at the research time. Non-safety indicators are adjusted sequentially and collaboratively within their respective hierarchical levels.
4. The weight adaptive adjustment processing method based on inter-aircraft conflict quantitative assessment according to claim 1, characterized in that: In method S1, the conflict entropy calculation model for two aircraft i and j is expressed 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 the normalized parameters.
5. The weight adaptive adjustment processing method based on inter-aircraft conflict quantitative assessment according to claim 4, 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.
6. The weight adaptive adjustment processing method based on inter-aircraft conflict quantitative assessment according to claim 4, characterized in that: Relative motion state data The calculation expression is as follows: ； ； in , These are the velocity vectors of aircraft i and j, 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.
7. The weight adaptive adjustment processing method based on inter-aircraft conflict quantitative assessment according to claim 4, 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.
8. The weight adaptive adjustment processing method based on inter-aircraft conflict quantitative assessment according to claim 1, characterized in that: In method S2, all aircraft records within the study airspace are selected as the study aircraft set. The conflict entropy calculation model divides the study aircraft set into several aircraft combinations, each consisting of two aircraft. The time of study for each aircraft combination is calculated separately. The conflict entropy will be studied in the context of the study of the airspace's intrinsic research moments. The total conflict entropy is obtained by aggregating the various aircraft combinations within the research aircraft set. .
9. The weight adaptive adjustment processing method based on inter-aircraft conflict quantitative assessment according to claim 1, 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... ; , Weighting 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.
10. The weight adaptive adjustment processing method based on inter-aircraft conflict quantitative assessment according to claim 2, characterized in that: The number of conflict security indicators is one, and the preset initial static weight of the conflict security indicator is [value missing]. The number of non-safety indicators is m-1, and the preset initial static weights are as follows: ; to study the moment 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 .