CN110866673A - Method for estimating sector number of area control - Google Patents
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Abstract
The invention relates to a method for estimating the number of sectors controlled by a region. The method for estimating the number of the regional control sectors utilizes key models and methods such as control load analysis, sector operation capacity, flight guarantee number, operation safety and efficiency and the like which are provided by research, establishes simulation models by using simulation software, the operation condition of the regional control sectors, airway route distribution, the number of flights in busy seasons, airspace operation characteristics, current interval regulations and the like, provides scientific suggestions for sector division on the basis, and provides a theoretical method basis for developing sector number estimation and division research of the total guarantee number of the regional control sectors based on flight guarantee prediction data.
Description
Technical Field
The invention relates to the field of regional control, in particular to a method for estimating the number of sectors in regional control.
Background
There are many ways of capacity assessment, each with its own advantages and disadvantages. The result of the computer simulation evaluation depends on the accuracy of the simulation model and the input data, the result accuracy degree is high, and the result is an important supporting section and development direction for capacity evaluation, but the result is limited by the prior art and funds and cannot be widely applied. From the existing means and technologies in various places, the radar simulator evaluation method based on the workload of controllers, the evaluation method based on the historical statistical data analysis and the evaluation method based on the mathematical computation model are simple and feasible, and the obtained data is accurate.
An evaluation method based on a computer simulation model. The method is suitable for evaluating the maximum capacity and the operation capacity, and has the advantages of high result accuracy; the defects are that the construction and the use of the simulation model need to invest more technical support and fund, and the evaluation period is longer. At present, the types of software which are popular internationally are SIMMOD software of the Federal aviation administration in the United states and software of the Boeing company in the United states, and the software has more applications in the aspects of simulation of airports and airspace and evaluation of actual capacity.
A radar simulator evaluation method based on controller workload. The method is suitable for evaluating the maximum capacity and the operation capacity, and has the advantages of simplicity, practicability, strong operability and more accurate result; the disadvantage is that the individual difference of the controller and the simulation environment have great influence on the accuracy of the result.
Evaluation method based on historical statistical data analysis. The method is suitable for evaluating the maximum capacity, and has the advantages that the method is convenient to operate and the result is accurate; the defects are that data collection is difficult, the data volume is large, and the quantity and quality of sample data directly influence the correctness of results; the confidence degree is only an empirical value and needs to be determined by a qualification controller according to different situations.
An evaluation method based on a mathematical computation model. The method is suitable for the assessment of the maximum capacity of the runway in combination with the last approach phase. The method mainly adopts a time-space analysis mathematical model, and has the advantages of simplicity, rapidness, less investment and more accurate capacity evaluation result; the disadvantage is that only the capacity of one runway can be evaluated, and human factors are not considered quantitatively.
How to solve the above problems is a need to be solved.
Disclosure of Invention
The invention aims to provide a method for estimating the number of area control sectors.
In order to solve the above technical problem, the present invention provides a method for estimating the number of sectors under regional regulation.
The method comprises the following steps:
establishing a regional control sector model;
analyzing the control load of each sector of the regional control sector model to obtain the control load data of each sector of the region;
estimating the running capacity according to the control load data of each sector of the area;
acquiring historical total guarantee rack number data, and calculating a final guarantee rack number according to the historical total guarantee rack number data;
selecting a full-year operation peak period based on the final guarantee frame, calculating the daily guarantee frame, and calculating the number of sectors according to the operation capacity;
and analyzing the safety and efficiency of the area control sector according to the calculation result of the number of the sectors.
The invention has the beneficial effect that the invention provides a method for estimating the number of the area control sectors. The method for estimating the number of the area control sectors comprises the following steps: establishing a regional control sector model; analyzing the control load of each sector of the regional control sector model to obtain the control load data of each sector of the region; estimating the running capacity according to the control load data of each sector of the area; acquiring historical total guarantee rack number data, and calculating a final guarantee rack number according to the historical total guarantee rack number data; on the basis of the final guarantee frame, selecting a full-year operation peak time period, calculating the daily guarantee frame, and calculating the number of sectors according to the operation capacity; and analyzing the safety and efficiency of the area control sector according to the calculation result of the number of the sectors. By utilizing key models and methods such as control load analysis, sector operation capacity, flight guarantee number, operation safety and efficiency and the like, which are provided by research, simulation software, the operation condition of the regional control sector, the distribution of airway routes, the number of flights in busy seasons, the operation characteristics of the vacant domain, the current interval regulation and the like are utilized to establish a simulation model, and on the basis, scientific suggestions for sector division are provided, so that a theoretical method basis is provided for the sector number estimation and division research of the total guarantee number of the regional control sector based on flight guarantee prediction data.
Drawings
The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is a flowchart of a method for estimating the number of sectors under regional regulation according to the present invention.
FIG. 2 is a detailed illustration of the compilation of the simulation model.
Fig. 3 is a diagram of the distribution of specific flights throughout the day.
Fig. 4 is a diagram of the final simulation operation effect.
Fig. 5 is a diagram showing the distribution of the control load in each sector hour throughout the day.
Fig. 6 is a distribution diagram of regulated load types.
Fig. 7 is a relational diagram obtained by performing regression processing on scatter data.
Fig. 8 is a diagram showing the distribution of each type of collision in the regional total sky domain.
FIG. 9 is a diagram of a potential conflict location distribution.
Fig. 10 is a conflict severity proportion graph.
FIG. 11 is a chart of waypoint hour traffic versus situation.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
Example 1
As shown in fig. 1, this embodiment 1 provides a method for estimating the number of sectors under regional regulation. The method for estimating the number of the regional control sectors utilizes key models and methods such as control load analysis, sector operation capacity, flight guarantee number, operation safety and efficiency and the like which are provided by research, establishes simulation models by using simulation software, the operation condition of the regional control sectors, airway route distribution, the number of flights in busy seasons, airspace operation characteristics, current interval regulations and the like, provides scientific suggestions for sector planning on the basis, and provides a theoretical method basis for carrying out sector number estimation and planning research on the total guarantee number of the regional control sectors based on flight guarantee prediction data. The method comprises the following specific steps:
s110: establishing a regional control sector model;
s120: analyzing the control load of each sector of the regional control sector model to obtain the control load data of each sector of the region;
s130: estimating the running capacity according to the control load data of each sector of the area;
s140: acquiring historical total guarantee rack number data, and calculating a final guarantee rack number according to the historical total guarantee rack number data;
s150: selecting a full-year operation peak period based on the final guarantee frame, calculating the daily guarantee frame, and calculating the number of sectors according to the operation capacity;
s160: and analyzing the safety and efficiency of the area control sector according to the calculation result of the number of the sectors.
The workload of the controller is closely related to the number of sectors in the area, and in order to determine the number of sectors, the calculation method of the workload must be considered first. Each of the control commands issued by the controller and the command repetitions received from the pilot take time during the control process. For each controller, the personal ability is limited, and the workload is too heavy or too low, which is not good for the performance of the personal ability. Insufficient workload is a waste of resources; heavy workload often causes controllers to fail to give instructions correctly and seriously jeopardize air traffic safety. The workload level of the controllers is the comprehensive reflection of multiple influence factors such as airspace structures, traffic compositions, control capacity, control programs and the like, and is an important basis for determining the number of planned airspace sectors. The time of flight of the aircraft in the controlled area is a result of a combination of the above mentioned influencing factors.
Based on the controller workload definition, the managed load in step S120 includes:
basic control load, conflict control load, coordination control load, altitude change load and altitude layer change control load;
the basic control load is a transfer load in simulation data, and is determined by the number of flights in a sector and the flight time of the sector, and the specific calculation method comprises the following steps:
WLM＝N+AT×F；
wherein N represents the effective number of flights flying through the sector in the statistical time period; f represents an adjustment factor; b is an adjustment coefficient; AT shows the average time of flight of the aircraft in the sector; p represents a turning point factor, and the unit is the number of turns; m represents an activity factor in the unit of number of racks/minute;
conflict control load WLCFIs determined based on the conflict between the aircrafts detected in the simulation operation, and the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
the conflict adjustment factors are expressed and are divided into 9 types according to the conflict type,
representing the conflict severity coefficient, dividing the conflict severity coefficient into 4 grades according to the difference between the conflict severity coefficient and the actual interval, and corresponding to the 4 severity coefficients;
coordinated load WLCThe method is determined based on the user-defined handover control action, reflects the magnitude of a load value brought by a sector handover process, and has the following calculation formula:
in the formula, NC1Indicating the number of flights leaving the sector, NC2Indicating the number of flights into the sector,
indicating the away-sector coordinated action adjustment factor,
entering a sector coordination action adjustment factor, wherein x represents the type of a coordination action;
height change load WLLCIs based on a high licenseThe type of the command can be determined, the load brought by the change of the altitude of the aircraft in the sector is mainly characterized, and the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
indicating the number of class i height allowed leveling instructions,
indicating the corresponding adjustment factor of the corresponding height permission leveling instruction; i.e. i11 denotes a leveling instruction, i1Denotes a climb command, i 13 represents a down command;
load WL of height-changing layerFLThe method is based on the type determination of an altitude layer change instruction, and characterizes the load value brought by the change of the altitude layer of the aircraft in a sector, and the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
indicating the number of class i height level change instructions,
indicating the corresponding adjustment factor, i, of the corresponding height level change command 21 denotes a climbing height layer command, i 22 represents a lower level instruction;
therefore, the control load WTThe calculation formula of (2) is as follows:
WT＝WLM+WLCF+WLC+WLLC+WLFL。
the load adjustment factors mentioned in the above various types of loads can be adjusted according to the situation. The embodiment analyzes the control load of each sector in the area from two aspects of time distribution and type distribution, and provides reasonable data support for better performing control staff work allocation.
The sector capacity includes air traffic service capacity, operation capacity, maximum capacity and the like, the maximum capacity refers to the maximum number of aircrafts capable of being served in a given time period in a specified airport or airspace range, namely, the operation capacity when the delay tends to infinity, and reflects the limit service level. The operation capacity is also called dynamic capacity, and refers to the maximum number of flights that the airway can accommodate when the delay time of all the flights is within an acceptable range in a certain time interval by considering the influence of random factors, and the operation capacity has the most guiding significance for the actual operation. The sector operation capacity is also called actual capacity, and means that a controller can safely and efficiently manage the number of aircrafts in a specified sector within a specified time period.
The maximum capacity is an ideal situation, and the practical application significance is not great, especially in the sector. Due to the real-time change of the traffic situation in the air, the workload level of the controller cannot be constant, so that the dynamic capacity evaluation aiming at the sector only has practical significance
In step S130, that is,
when the control load reaches a preset threshold value, the corresponding sector traffic frame is the corresponding capacity of each sector, wherein the sector capacity refers to the dynamic capacity of the sector and is defined as: considering the influence of factors changing along with time or space change on traffic situation of a certain control sector in a designated time period, and when the workload level of a controller reaches the maximum, the number of aircrafts which can be served by the sector is increased;
the simulation platform is utilized to clone the flight time, the change condition of the control load of each sector along with the flight number under the condition of large flow is found out, the threshold value of the acceptable control load is found out to be used as the basis of capacity judgment, and finally the capacity value of the corresponding sector is obtained.
In this embodiment, the method for obtaining the historical total guaranteed rack number data and calculating the final guaranteed rack number according to the historical total guaranteed rack number data includes:
acquiring historical total guarantee rack data;
respectively adopting a geometric mean prediction method, a linear regression analysis prediction method, a quadratic moving mean prediction method, a Brown linear index smooth prediction method and a Brown quadratic polynomial index smooth prediction method to carry out guarantee shelf prediction on historical total guarantee shelf data;
wherein, the geometric mean prediction method
The method uses geometric mean to calculate the development speed of the prediction target, and then carries out prediction. The method is suitable for predicting the situation that the target development process consistently rises or falls and the periodic cycle specific speed is approximately close. Is the n-th square root of the product of the n-th order variables. Are commonly used in statistical studies to calculate the average rate of development. When the average increasing amplitude of the rack flow in different periods of time is calculated, the method is also used,
wherein, ytIn order to predict the predicted value of the subject in the year t,
is the actual value of the base year, t is the number of years since the base year, k is the geometric mean growth rate, and b is the mean difference fluctuation coefficient.
Linear regression analysis method
The linear regression analysis is an analysis of the quantitative relation of objective objects, is an important statistical analysis method, and is widely applied to research on influencing factors and association among social and economic phenomenon variables. Because the change of the complicated economic phenomenon of the relation of objective matters can not be described by one variable, the unitary linear regression analysis prediction method is a method for establishing a linear regression equation of x and Y for prediction according to the correlation between an independent variable x and a dependent variable Y. Since the market phenomenon is generally influenced by a variety of factors, not just one. Therefore, by applying the unitary linear regression analysis prediction method, various factors influencing the market phenomenon must be comprehensively analyzed. Only when one variable with obviously higher influence on the dependent variable than other factors does exist in a plurality of influencing factors, the variable can be used as an independent variable and can be predicted by applying a univariate correlation regression analysis market prediction method. The unitary regression equation of this embodiment is:
y＝a+bx+e；
knowing n sets of observations y of y, xi、xi(i ═ 1, 2,. n), the univariate linear regression prediction model established using regression analysis was:
in the formula
Are estimates of the parameters a, b, also called regression coefficients,
in order to predict the value of the regression coefficient,
the calculation is as follows:
linear quadratic moving average method
In order to avoid systematic errors when predicting trending data using the moving average method, a linear quadratic moving average method was developed. The basis of this method is to calculate a moving average twice, i.e. on the basis of a moving average of the actual values, a moving average is performed again. The general formula is:
at＝2S′t-S″t
Ft+m＝at+btm m is the number of predicted advance periods
The first formula calculates a primary moving average value, the second formula calculates a secondary moving average value, and the third formula basically corrects an initial point of prediction (latest value) so that no hysteresis exists between a predicted value and an actual value; (S 'for formula II)'t-S″t) Is divided by
This is because the moving average is an average over N points, which should be centered between the N points.
Linear quadratic exponential smoothing method
The basic principle is similar to linear quadratic moving average, because when a trend exists, the first and second smoothed values lag behind the actual value, and the difference between the first and second smoothed values is added to the first smoothed value, the trend can be corrected.
S′t＝axt+(1-a)S′t-1
S″t＝aS′t+(1-a)S″t-1
S′tIs a once exponential smoothing value, S ″tIs a quadratic exponential smoothing value.
at＝2S′t-S″t
Ft+m＝at+btm m is the number of predicted advance periods
Cubic exponential smoothing method
When the basic model of the data has a power of second, third or higher order, then a form of high order smoothing is required. The basic approach is to perform another smoothing (i.e., cubic smoothing) and make an estimate of the parameters of the quadratic polynomial. Similarly, the transition may be smoothed by a quadratic polynomial into a cubic or higher order polynomial. Calculating the formula:
S′t＝axt+(1-a)S′t-1
S″t＝aS′t+(1-a)S″t-1
S″′t＝aS″t+(1-a)S″′t-1
at＝3S′t-S″t+S″′t
the final guarantee frame is calculated by adopting an equal-weight average combined prediction method for the calculation results of the five prediction methods, namely,
in this embodiment, the determination of the number of sectors should adopt a method with scientific data, simple calculation and strong operability. Because the reference capacity of a specific sector is comprehensively reflected by factors such as airspace environment, airspace structure, traffic flow, complexity, controller capacity and the like, generally, the average guarantee capacity of the sector of the current controlled airspace is taken as a reference value of the sector to be divided, and the sector number can be estimated by carrying out equipartition according to the predicted traffic flow, wherein the calculation formula of the sector number is as follows:
in the formula (I), the compound is shown in the specification,
planning the total traffic flow of the airspace for prediction;
the sector capacity value of each sector in the current control air space is obtained; e is the number of sectors to be divided, and if the number is not an integer, rounding up is carried out.
In this embodiment, step S160 includes:
running safety analysis;
and analyzing the operation efficiency.
Wherein the method of running a security analysis comprises:
and reflecting the operation safety level of the aircraft in the airspace by adopting three indexes of potential conflict amount, potential conflict level and sector instantaneous flight amount.
The potential conflict amount refers to the number of times of potential flight conflicts of the aircraft in a certain specific time period and space range, and is divided into three types of homodromous conflicts, reverse conflicts and cross conflicts according to the states of the aircraft. A potential conflict is a situation where space-time convergence between aircraft does not meet the separation criteria for a future time instant. The expression formula for the potential conflict amount may be defined as follows:
where χ represents the number of potential collisions, f represents the discriminant function of the potential collisions of the aircraft i and j, Sij，minRepresenting the minimum separation, S, between aircraft i and j*Indicating the interval specification. When S isij，min＜S*A potential conflict between aircraft is considered.
The potential conflict level depends on the difference between the minimum spacing and the specified spacing in the dynamic process of the actual distance between the aircraft pairs, reflecting the magnitude of the risk of the aircraft pairs colliding in actual operation. The potential conflicts are classified into four grades of 0, 1, 2 and 3 according to the difference of the closeness degree
Wherein S isij，minRepresenting the minimum actual separation, S, between the pair of aircraft*Representing the separation between pairs of aircraft.
Instantaneous flow of sector if aircraft
Represents tkThe aircraft set accommodated by sector i at time instant tkInstantaneous flow of
Can be expressed as:
the instantaneous flow of the sector can reflect the flight number which needs to be processed by the controller in the sector, and when the processing flight number exceeds or approaches the capacity of the controller, the operation risk of the airspace is greatly improved
In this embodiment, the method for analyzing the operation efficiency includes:
and measuring the operating efficiency of the airspace by using the indexes of sector saturation, waypoint flow, average delay time and delay proportion.
The sector saturation refers to the ratio of the sector flow to the sector capacity in a fixed period, and reflects the utilization condition of air space by air transportation. The specific calculation formula is as follows:
in the formula, FsIndicating the flow value, C, of sector ssIndicating the capacity value of sector s. The higher the saturation of the sector is, the higher the utilization rate of the sector airspace is;
waypoint flow refers to the number of aircraft passing a waypoint per unit time. The flow distribution condition in the airspace unit can be better reflected by the flow of the waypoint, and the operation efficiency of the airspace can be better reflected. The specific calculation formula of the waypoint flow is as follows:
in the formula, FwWhich represents the traffic at the waypoint w,
representing the aircraft passing through waypoint w, T representing the statistical period of waypoint traffic,
representing the moment when the ith aircraft passes through the waypoint w;
the average delay time is an average value of difference values between the actual arrival time of the aircraft at a certain airspace unit (including an airport, an waypoint and a sector) and the predicted time, the operation efficiency of the airspace can be visually reflected, and the shorter the delay time is, the higher the operation efficiency of the airspace is. The specific calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
indicating the time at which the ith aircraft actually arrives at a airspace unit,
represents the time when the ith aircraft is expected to reach a certain airspace unit, and n represents the number of aircraft passing through the airspace unit.
The flight delay proportion is the proportion of delayed flights to the total number of flights, and can reflect delay conditions more carefully. The specific calculation method is as follows:
in the formula, wherein, NdelyRepresenting the number of aircraft racks that have incurred a delay, and n represents the total number of aircraft racks.
Example analysis
The radar data of a certain day of 2025-year peak period in the simulated Wujiquan region is used as the basis of initial flight modeling, the proportion of each model, the company distribution and the altitude layer distribution of the flight used by simulation are kept consistent with the current flight plan actually operated, and the reliability of simulation operation is improved. The 8 months and days of the initial flight plan which all contain the Wulu wood airport regional support are 1618 frames. The specific contents of the simulation model compilation are shown in FIG. 2.
According to the simulation data of the flow in 2025 years, flight time guaranteed by the current Ulmus dorsalis sector is taken as a basis, flight cloning is carried out by using a flight random cloning tool in the model, cloning is carried out according to the proportion of different airway flows in the cloning process, and the final flight time totally comprises flights 1160. The distribution of specific flights throughout the day is shown in figure 3.
As can be seen from fig. 3, when the simulation schedule is designed, the proportion of the hour distribution of the flights in the whole day is consistent with the original flight schedule.
The airport uses a first-come first-serve control strategy, in the aspect of interval setting, the wake flow interval of continuous takeoff and landing in a single runway mode is set at first, the continuous takeoff time interval is set to be 2min, and the system selects the larger value of the wake flow interval and the time interval as the interval of practical application in simulation. For the setting of the sector interval, the intervals of the approach regulation sector and the area regulation sector are set to 5nm and 16nm, respectively, which will be the criteria for collision discrimination. The corresponding regulatory handover protocols between sectors and the measures governing conflicting deployment within sectors will also be written into the simulation model by the relevant settings.
(1) AR01, AR07, AR04, AR08 policy settings: the horizontal flight interval is 25 km; traversing the interval in the forward direction for 15 km; crossing the interval 40km for the head; the boundary flat flight interval is 20 km.
(2) AR02, AR03, AR05, AR06 policy settings: the horizontal flying interval is 50 km; the forward crossing interval is 50 km; crossing the interval 70km for the head; the boundary flat flight interval is 20 km.
After the modeling of the airspace simulation model is completed, the airspace simulation model needs to be verified, and the reliability of the simulation model is the key for judging whether the airspace simulation analysis result is credible. In the macroscopic aspect, according to the current historical data and the result of statistical analysis, the accuracy of the output result of the analog simulation is verified by comparing the key indexes of the airspace operation performance such as sector flow, flight delay and the like. In the microscopic aspect, the simulation operation scene is combined, whether the output result is consistent with the set airspace scene or not is judged according to the experience of air traffic management personnel, and the simulation model verification is carried out by analyzing the time of the typical flight key route point. The final simulation run effect is shown in fig. 4: by combining five categories of the control load in the model, namely, basic control load, conflict control load, coordination control load, height-changing layer control load and the like, the load data of each control sector in 2025 years in the Wuluqiqi area is obtained according to the control sector load, as shown in the table.
In order to reflect the load distribution situation of each sector in the air in detail all day, the control load of each sector in the Wuluqiqi area in hour is calculated according to the five control load calculation methods with 1 hour as a time slice, and the distribution situation of the control load of each sector in hour all day is shown in fig. 5. The Peak Workload Threshold load shown in the figure is a recommended Threshold value of the regulated load given according to the regulation experience, and when the value is exceeded, the sector regulated load is considered to be too large, and the flow value of the sector needs to be controlled. Mean Workload Threshold represents the average of the load of the full spatial domain sector over the statistical period. From the comparison situation of the loads of all sectors, the loads of all sectors in the Xinjiang area are extremely unevenly distributed, and the loads of two control sectors in the terminal area are basically the average value of the total loads of the sectors. The loads of the area regulation sectors AR01 fan and AR04 fan are significantly greater than the average. Especially, for the AR01 fan, in the time period of 09:00-21:00, the sector load values all exceed the sector load threshold, the controller load is large, the distribution situation of the control load types of each sector in the Wulu-wood-level information area is counted, and the specific distribution situation is shown in fig. 6.
From the type of the managed load, the managed load is mainly the basic managed load, and the conflict load ratio of the two managed sectors in the terminal area is relatively large. The AR01 fan load with larger control load value is mainly of a basic control load type, which indicates that the fan load is caused by a larger number of aircrafts and a longer flight time of the fan. Similarly, the proposal of the adjustment of the spatial structure in the sector can be further provided through the analysis of the control load types of different sectors.
In this embodiment, in order to quickly obtain the capacity value of each control sector, the simulation platform is used to clone the flight time, the change situation of the control load of each sector along with the flight number under the condition of large flow is found out, the threshold value of the acceptable control load is found out to be used as the basis of capacity judgment, and finally the capacity value of the corresponding sector is obtained. And obtaining the corresponding relation between the control load and the flight number through platform rapid simulation. In fig. 6, Peak Workload Threshold represents an acceptable Threshold of the managed load, in order to obtain an accurate capacity value, regression processing is performed on the scattered point data of the load data of the managed sector to obtain a corresponding relation fig. 7, and it is found that when the AR01 fan managed load reaches the Threshold, 16 times are corresponded.
In the same way, one can obtain: when the control load of the AR02 fan reaches a threshold value, the number of the fan is 12 corresponding to the control load; when the control load of the AR03 fan reaches the threshold value, corresponding to 13 times; when the control load of the AR04 fan reaches the threshold value, the number of the corresponding 14 is counted; when the control load of the AR05 fan reaches a threshold value, corresponding to 8 times; when the control load of the AR06 fan reaches the threshold value, the number corresponds to 7; when the control load of the AR07 fan reaches a threshold value, the number of the fan is 12 corresponding to the control load; when the regulation load of the AR08 reaches the threshold, the number corresponds to 14.
In summary, the operation capacity value of each controlled sector in the wuluqiqi region is as shown in the table:
guaranteed shelf prediction
Firstly, according to a geometric mean prediction method, a linear regression analysis method, a linear quadratic moving average method, a linear quadratic exponential smoothing method, a cubic exponential smoothing method and an equiweight mean value combination prediction, by using historical statistical data of 2010-2017, prediction results of a past year and a target year are obtained. Wherein, F1Predicted value 694084, F for geometric mean prediction2Prediction 709569, F for Linear regression analysis3Predictor 429788, F for linear quadratic moving average4Predictor 438752, F for linear quadratic exponential smoothing5Is the predicted value 577917 of the three-time exponential smoothing method.
Then, set up
Parameter (1) of (1), i.e. w1Weight of 0.9961, w for geometric mean prediction2Weights 0.9959, w for Linear regression analysis3Weight 1.0000, w for linear quadratic moving average4Is a linear quadratic indexWeight of smoothing method 0.9993, w5The weight for cubic exponential smoothing is 0.9982.
In conclusion, the prediction result of the flight support number of the Wuluqiqi region in 2025 can be obtained, and as shown in the table below, 569826 number of the Wuluqiqi region flow data in 2025 can be obtained.
Total frame number prediction of Wuluqiqi region flight guarantee
From the special department of the flow management room in the Wuluqin region, it is found that the guarantee period of 8 months in summer peak period accounts for about 8.8% of the guarantee period of the whole year, and the guarantee period of 8 months in 2025 is expected to be 50145, and the average guarantee period of 1618 per day. A continuous busy work period refers to a continuous time period during which the traffic of a sector (regulated area) exceeds one-half of its capacity during a day, i.e., the period between the time (hour) during which the traffic first exceeds one-half of the capacity and the time (hour) during which the traffic last exceeds one-half of the capacity during a day. From the regulatory load versus time profiles, it can be seen that the load distribution of each sector of 0900-2100 approaches or exceeds the average regulatory load of each sector for all days, which can be defined as a busy period. Data provided by experts in the Wuluqin regional traffic management room can indicate that the guarantee number of the period accounts for about 78% of the guarantee number of the whole day, namely 1262 number, and the average guarantee number of the period per hour is 106 number in busy period.
The average hour capacity of each fan is 12 frames according to the evaluation result of the capacity of each fan, and is represented by WLMThe theoretical number of sectors is 8.83, rounded up to 9, which means that in 2025 the wullurginia sector should be AT least 9 sectors with an average sector capacity of up to 12 shelves.
According to the operation safety analysis indexes, operation safety indexes such as conflict types, conflict positions and conflict severity of all sectors of the Wuluqiqi region in 2025 are analyzed based on the current division scheme of the Wuluqiqi region control sector and by combining simulation data, so that reference basis is provided for subsequent planning and adjustment of the Wuluqiqi region control sector in 2025.
(1) Conflict type distribution
In order to better reflect the reasons for the potential conflict, the present embodiment classifies the types of conflicts more finely. And classifying the potential conflicts into 18 types according to the included angle between the aircraft headings and the flight state, wherein the specific types are classified as shown in the following table:
through statistics of simulation results, the conflict distribution conditions of various types in the whole sky domain of the Wulu wood area are shown in fig. 8:
as can be seen from fig. 8, of the 100 potential conflicts counted throughout the day in the wulluqiqi region, the same-direction conflict and the reverse conflict are dominant, and the number of conflicts of type 1, type 5, and type 6 in the same-direction conflict is relatively large, which indicates that the same-direction conflict mainly occurs in the descending and climbing stages. The reverse conflict is mainly the conflict of the type 12, which means that the reverse conflict is mainly the conflict between aircrafts in the descending stage. The cross conflict amount is low, wherein the type 15 and the type 16 are taken as main parts, and the cross conflict mainly focuses on the cruise and climbing phases.
(2) Conflicting position distributions
The potential conflict position is a position where the aircraft is expected to generate conflict, and can intuitively reflect a region with higher safety risk in the airspace, and the distribution of the potential conflict amount of each sector is shown in the following table:
as is apparent from the above table, the distribution of potential collisions in each sector in the wullurginian region is very uneven, and the potential collisions are mainly concentrated in the terminal regulatory sector and the area AR04 sector. The specific potential conflict location distribution situation is shown in fig. 9:
it can be seen from the figure that the geographic positions where the conflict occurs present uneven distribution, the regional sector conflict positions are mainly concentrated on the B215 route XKC-KABDO route section, the W99 route is close to the G470 convergent point odd station, and the conflict of the terminal sector is mainly concentrated on the approach sorting region in the approach and departure stage.
(3) Severity of conflict
According to the grade classification of the collision severity of the potential collision grades, counting the potential collisions detected in the Wuluqiqi area according to the severity, wherein the specific collision severity accounts for the example shown in FIG. 10:
as can be seen from the above figure, the level 3 potential conflict in the wullurginia region accounts for 77%, the level 0 potential conflict does not occur, and the severity of the overall conflict is not high.
Sector instantaneous flow analysis
The instantaneous flow of the sector is a key index for reflecting the instantaneous operation pressure of an airspace, and the occurrence of congestion in the airspace is caused by overlarge instantaneous flow of the sector, so that the operation safety in the airspace is seriously influenced. According to
The distribution situation of the instantaneous flow of each sector in the Wuluqiqi area in the whole day is analyzed, and the peak value of the instantaneous flow of each sector is compared as shown in the following table:
analysis of operating efficiency
Based on the current division scheme of the control sector of the Wuluqiqi region, the simulation data is combined, so that the operation efficiency of the sector saturation, the waypoint flow distribution, the flight delay condition and the like of each sector of the Wuluqiqi region in 2025 is analyzed, and reference data is provided for the subsequent planning and adjustment of the control sector of the Wuluqiqi region in 2025.
(1) Sector saturation analysis
By
It can be seen that the saturation of a sector is directly related to the sector trafficIn order to better reflect the distribution of traffic flow in the Wuluqiqi area and the utilization condition of sector airspace, the saturation of each sector is counted, and the specific data is shown as a table:
as can be seen from the above table, the average saturation of AR01 and AR04 in the area sector reaches 0.9 or more, and the operation efficiency is relatively high. The saturation of the sectors AR03 and AR06 is low, which indicates that effective utilization of space domain resources cannot be well realized, and the operation efficiency of the space domain is not high.
Waypoint flow distribution
The traffic of the waypoints can better reflect the specific distribution condition of the traffic in the airspace, and find out the bottleneck where the operating efficiency of the airspace is affected. In this embodiment, waypoints 20 before the average hourly flow are selected from the waypoints in the wu-qiu region and counted, and the specific waypoint hourly flow comparison is shown in fig. 11.
As can be seen from fig. 11 in combination with the structure of the wullurginian area, the waypoints with large flow in the wullurginian area are mainly concentrated on the W99, B215 and G470 waypoints, and particularly, the QTV point is a convergence point of the G470 and W99 as the critical waypoints, the average hour flow reaches 7 frames, and the maximum hour flow reaches 19 frames.
Flight delay
In the airspace simulation operation, the influence of factors such as weather and military limits is not considered, so that the statistical delay time and the actual delay time have a certain difference. According to simulation results, only the Wulu wood level airport in the Wulu wood level area has delay conditions, and the specific delay time and proportion are shown in the following table:
as can be seen from the above table, the average delay time of Wulu wood level airport is 2 minutes 25 seconds, and the overall delay condition is not serious. The departure delay proportion is higher and reaches 50%, and the entrance delay proportion is relatively smaller. In general, the delay condition of the Wulu wood level airport is not serious, and the whole operation efficiency is higher.
The waypoint delays are also a key index for reflecting the operating efficiency of the airspace, and the waypoint delays in the Wulu-quan area are counted to obtain the specific waypoint delay conditions shown in the following table:
as can be seen from the above table, the waypoints mainly delayed are mainly concentrated on several entry points, wherein the delay condition of the BIKNO waypoint and the NUKTI waypoint is relatively high, and it can be concluded that the traffic of each waypoint is large, and the traffic of W99, B215 and G470 at the east approach affects the operation efficiency of the airspace.
In summary, the present invention provides a method for estimating the number of sectors under regional control. The method for estimating the number of the area control sectors comprises the following steps: establishing a regional control sector model; analyzing the control load of each sector of the regional control sector model to obtain the control load data of each sector of the region; estimating the running capacity according to the control load data of each sector of the area; acquiring historical total guarantee rack number data, and calculating a final guarantee rack number according to the historical total guarantee rack number data; on the basis of the final guarantee frame, selecting the annual operation peak period, calculating the daily guarantee frame, and calculating the number of sectors according to the operation capacity; and analyzing the safety and efficiency of the area control sector according to the calculation result of the number of the sectors. By utilizing key models and methods such as control load analysis, sector operation capacity, flight guarantee number, operation safety and efficiency and the like provided by research, simulation software, the operation condition of a regional control sector, airway route distribution, the number of busy-season flights, the operation characteristics of airspace, current interval regulation and the like are utilized to establish a simulation model, and on the basis, a scientific proposal for dividing and setting the sector is provided, so that a theoretical method basis is provided for the estimation of the sector number of the total guarantee number of the regional control sector based on flight guarantee prediction data and the division and setting research.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (8)
1. A method for estimating the number of sectors regulated by a region, comprising:
establishing a regional control sector model;
analyzing the control load of each sector of the regional control sector model to obtain the control load data of each sector of the region;
estimating the running capacity according to the control load data of each sector of the area;
acquiring historical total guarantee rack number data, and calculating a final guarantee rack number according to the historical total guarantee rack number data;
on the basis of the final guarantee frame, selecting the annual operation peak time, calculating the daily guarantee frame, and calculating the number of sectors according to the operation capacity;
and analyzing the safety and efficiency of the area control sector according to the calculation result of the number of the sectors.
2. The method for estimating the number of sectors under regional control according to claim 1, wherein the analyzing the control load of each sector of the model of sectors under regional control, and the obtaining the control load of the method for the control load data of each sector under regional control comprises:
basic control load, conflict control load, coordination control load, height change load and height layer change control load;
the basic control load is a transfer load in simulation data, and is determined by the number of flights in a sector and the flight time of the sector, and the specific calculation method comprises the following steps:
WLM＝N+AT×F；
wherein N represents the effective number of flights flying through the sector in the statistical time period; f represents an adjustment factor; b is an adjustment coefficient; AT shows the average time of flight of the aircraft in the sector; p represents a turning point factor, and the unit is the number of turns; m represents an activity factor in the unit of number of racks/minute;
conflict control load WLCFIs determined based on the conflict between the aircrafts detected in the simulation operation, and the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
the conflict adjustment factors are expressed and are divided into 9 types according to the conflict type,
representing the conflict severity coefficient, dividing the conflict severity coefficient into 4 grades according to the difference between the conflict severity coefficient and the actual interval, and corresponding to the 4 severity coefficients;
coordinated load WLCThe method is determined based on the user-defined controlled handover action, reflects the magnitude of a load value brought by a sector handover process, and has the following calculation formula:
in the formula, NC1Indicating the number of flights leaving the sector, NC2Indicating the number of flights into the sector,
indicating the away-sector coordinated action adjustment factor,
entering a sector coordination action adjustment factor, wherein x represents the type of a coordination action;
height change load WLLCThe method is determined based on the type of the altitude permission command, mainly characterizes the load brought by the change of the altitude of the aircraft in the sector, and has the following calculation formula:
in the formula (I), the compound is shown in the specification,
indicating the number of class i height allowed leveling instructions,
indicating the corresponding adjustment factor of the corresponding height permission leveling instruction; i.e. i11 denotes a leveling instruction, i1Denotes a climb command, i13 represents a down command;
load WL of height-changing layerFLThe method is based on the type determination of an altitude layer change instruction, and characterizes the load value brought by the change of the altitude layer of the aircraft in a sector, and the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
indicating the number of class i height level change instructions,
indicating the corresponding adjustment factor, i, of the corresponding height level change command21 denotes a climbing height layer command, i22 represents a lower level instruction;
therefore, the control load WTThe calculation formula of (2) is as follows:
WT＝WLM+WLCF+WLC+WLLC+WLFL。
3. the method of estimating the number of sector areas under regional regulation according to claim 2,
the running capacity estimation is performed based on the regulated load data of each sector of the area, i.e.,
when the control load reaches a preset threshold value, the corresponding sector traffic frame is the corresponding capacity of each sector, wherein the sector capacity refers to the dynamic capacity of the sector and is defined as: considering the influence of factors changing along with time or space change on traffic situation in a specified time period for a certain control sector, and when the workload level of a controller reaches the maximum, the number of aircrafts which can be served by the sector is increased;
the simulation platform is utilized to clone the flight time, the change condition of the control load of each sector along with the flight number under the condition of large flow is found out, the threshold value of the acceptable control load is found out to be used as the basis of capacity judgment, and finally the capacity value of the corresponding sector is obtained.
4. The method of estimating the number of sector areas under regional regulation according to claim 3,
the method for acquiring the historical total guarantee rack number data and calculating the final guarantee rack number according to the historical total guarantee rack number data comprises the following steps:
acquiring historical total guarantee rack data;
respectively adopting a geometric mean prediction method, a linear regression analysis prediction method, a quadratic moving mean prediction method, a Brown linear index smooth prediction method and a Brown quadratic polynomial index smooth prediction method to carry out guarantee shelf prediction on historical total guarantee shelf data;
and calculating the final guarantee number of the calculation results of the five prediction methods by adopting an equal-weight average value combined prediction method.
5. The method of estimating the number of sector areas under regional regulation according to claim 4,
the calculation formula of the sector number is as follows:
in the formula (I), the compound is shown in the specification,
planning the total traffic flow of the airspace for prediction;
the sector capacity value of each sector in the current control airspace is obtained; e is the number of sectors to be divided, and if the number is not an integer, rounding up is carried out.
6. The method for estimating the number of sectors under regional regulation according to claim 5, wherein the method for analyzing the security and efficiency of the sectors under regional regulation according to the calculation result of the number of sectors comprises:
running safety analysis;
and analyzing the operation efficiency.
7. The method of claim 6, wherein the security analysis is performed by a method of estimating the number of sectors regulated in a region
And reflecting the operation safety level of the aircraft in the airspace by adopting three indexes of the potential conflict amount, the potential conflict level and the sector instantaneous flight amount.
8. The method of estimating a number of sector due to zone regulation according to claim 7,
the method of analyzing the operating efficiency, i.e.
And measuring the operation efficiency of the airspace by adopting the indexes of sector saturation, waypoint flow, average delay time and delay proportion.
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