CN105225193A - A kind of method and system of the sector runnability aggregative index based on multiple regression model - Google Patents
A kind of method and system of the sector runnability aggregative index based on multiple regression model Download PDFInfo
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Abstract
The present invention discloses a kind of air traffic control sector runnability method for comprehensive detection and system, comprise step: step 1: the control sector running performance index sample choosing certain time length interval, and control sector runnability aggregative index sample corresponding to These parameters is as sample data; Step 2: according to above-mentioned sample data, sets up linear regression model (LRM) and nonlinear regression model (NLRM); Step 3: by degree of fitting, conspicuousness and error analysis, compares to linear regression model (LRM) and nonlinear regression model (NLRM), determines sector runnability comprehensive detection multiple regression model; Step 4: real-time control sector running performance index is imported sector runnability comprehensive detection multiple regression model, obtains control sector runnability aggregative index.The present invention adopts Quantitative research method, will affect each dimension index of control sector runnability, carries out comprehensively, considers; Designed control sector runnability method for comprehensive detection and system, can be applied to air traffic control unit, have very strong application project operability.
Description
Technical Field
The invention relates to the field of monitoring, in particular to a comprehensive detection method and a comprehensive detection system for the operation performance of an air traffic control sector.
Background
With the development of the air transportation industry, in order to ensure the safety and the order of various flight activities, the air traffic control service is developed and perfected at the same time, and the air traffic control service is mature in the 20 th century and the 80 th era. The main content of modern air traffic control services is: an air traffic controller (referred to as a 'controller', the same below) manages and controls the administered aircrafts by relying on modern communication, navigation and monitoring technologies, coordinates and guides the movement paths and modes of the administered aircrafts, so as to prevent the aircrafts from colliding with the aircrafts and the aircrafts from colliding with obstacles in an airport maneuvering area, maintain and accelerate the ordered flow of air traffic. An air traffic control sector (referred to as a "control sector", hereinafter) is a basic spatial unit for air traffic control (referred to as a "control", hereinafter). Typically, the airspace that provides air traffic control services for aircraft is divided into control sectors, with each control sector corresponding to a control personnel operating seat. The operation performance of the control sector is the technical index refinement of the aircraft operation situation in the control sector, which not only reflects the quality and level of the control service provided by a controller to the controlled sector under jurisdiction, but also reflects the use efficiency of a specific control airspace. Therefore, effective detection of the operation performance of the control sector is the basis and precondition for adjusting the control operation strategy and optimizing the control airspace structure.
Patent document CN104636890A discloses a system and method for dynamically adjusting air traffic control sectors in 20/05/2015. The method comprises the following steps: (1) generating a set of feasible sector combinations for optimization based on each sector data in the spatial domain database; (2) based on the radar historical data, calculating a historical control workload coefficient of each sector; (3) calculating flight flow entering each sector according to the current flight position and flight state information read in by the radar in real time; (4) calculating flight flow entering each sector according to the current flight plan; (5) and calculating the control workload of each sector based on the historical workload coefficient and the real-time flow and the planned flow of the flight, and then calculating the optimal sector combination in the feasible sector combination set based on an optimal sector combination selection model.
Currently, there is little research on the operation performance of air traffic control sectors, and most of the research focuses on the following isolated aspects: (1) the air traffic flow density is divided into two levels of strategy and tactics, wherein the former mainly represents airspace complexity indexes, and the latter mainly represents the judgment of air traffic congestion degree of a control unit. At present, the air traffic flow density index is still mainly presented by aircraft number statistics of a control unit in application. (2) And controlling the safety performance of operation, including both quantitative and qualitative aspects. In the aspect of quantification, the total safety Target Level (TLS) established by the International Civil Aviation Organization (ICAO) according to collision risk analysis is 1.5 multiplied by 10 < -8 > fatal flight accidents/flight hour, and the civil aviation air traffic control system in China takes the accident symptom ten thousands times rate as a key safety index according to danger approach risk analysis. In qualitative aspect, ICAO recommends using a Threat Error Management (TEM) or a normal operation safety monitoring (NOSS) method to perform qualitative control operation safety performance evaluation. The domestic scholars respectively establish a safety risk assessment index system around 4 types of factors such as people, machines, rings, management and the like, and develop index weight analysis. (3) The performance of the control operation efficiency mainly surrounds the aspect of flight delay indexes. At present, the foreign flight delay statistical indexes relate to delay frame rate and delay time. The detailed statistics of the flight delay time lacking in civil aviation in China needs to be improved in aspects of defining the flight delay reason, designing statistical indexes, and carrying out statistical methods and flows. (4) The workload of the policer is an important consideration for capacity estimation of the policing sector. Foreign scholars respectively provide physiological indexes such as response of electric shock skin, heart rate, electrocardiogram, blood pressure, body fluid and the like, and behavior indexes such as equipment operation times, land-air call time records and the like from the perspectives of physiological/behavior characteristics, subjective evaluation and work subdivision; subjective evaluation technologies such as an ATWIT technology, a NASA-TLX scale, a SWAT scale and an MCH method; and measuring the working time of a manager by using a weighing method such as DORATASK, MBB method, RAMS method and the like. The domestic scholars develop subjective evaluation methods and provide a controller workload evaluation model based on the extension science.
The existing research content aiming at the operation performance of the air traffic control sector at present mainly has the following defects: (1) in the aspect of research methods, more qualitative researches, less quantitative researches and insufficient objectivity are carried out. (2) In the aspect of index detection, the index dimension is single, and comprehensive detection is not enough, so that the comprehensive detection capability is insufficient. (3) In the aspect of applicability, the existing research still stays in the laboratory research stage, mainly serves strategic decisions, and has less practical engineering application for air traffic control units. Due to the defects, the existing research on the detection of the operation performance of the control sector at home and abroad is deficient in the aspects of objectivity, comprehensiveness, operability and the like, and particularly, the requirement of real-time detection and response alarm on the operation performance of the control sector in practice is not effectively met.
Disclosure of Invention
The invention provides a comprehensive detection method and system for the operation performance of air traffic control sectors, which are more efficient and can improve objectivity and prediction accuracy.
The purpose of the invention is realized by the following technical scheme:
a comprehensive detection method for operation performance of air traffic control sectors comprises the following steps:
step 1: selecting a control sector operation performance index sample with a certain time interval and a control sector operation performance comprehensive index sample corresponding to the index as sample data;
step 2: establishing a linear regression model and a nonlinear regression model according to the sample data;
and step 3: comparing the linear regression model with the nonlinear regression model through the fitting degree, the significance and the error analysis to determine a multiple regression model for the comprehensive detection of the sector operation performance;
and 4, step 4: and importing the real-time control sector operation performance index into a sector operation performance comprehensive detection multiple regression model to obtain a control sector operation performance comprehensive index.
Further, the operation performance indexes of the control sector in the step 1 include a sector operation trafficability index, a sector operation complexity index, a sector operation security index, a sector operation economy index, and a controller workload detection index.
Further, the sector trafficability detection indexes respectively comprise sector flow, sector navigation mileage, sector navigation time and sector traffic flow density;
the sector complexity detection indexes comprise the climbing times of a sector aircraft, the descending times of the sector aircraft, the speed change times of the sector aircraft and the navigation change times of the sector aircraft;
the sector safety detection indexes comprise sector short-term conflict alarm frequency and sector minimum safety height alarm frequency;
the sector economy detection indexes comprise sector saturation, sector queuing length, sector aircraft delay frame rate, sector aircraft delay time and sector aircraft average delay time;
the controller workload detection indexes comprise land-air communication channel occupancy rate and land-air communication times.
Further, before step 2, the standard conversion is performed on the sample data; the normalization conversion process is as follows:
let xij、x′ijRespectively representing the original data of the ith sample data and the data after the normalization conversion,sjrespectively representing the mean and variance of the jth index sample, then:
normalizing the data x 'after conversion'ijAs input data for building linear and non-linear regression models.
Further, the step 2 comprises the following steps:
step 2.1 respectively establishing a multiple linear regression model and a multiple non-linear regression model according to the sample data, and solving a coefficient bi，
Wherein the multiple linear regression model is:
Y＝XB+U
wherein,
the multiple nonlinear regression model is:
Y＝f[(b1,b2,…,bk)；X1,X2,…,Xn]
wherein the dependent variable Y is the comprehensive index of the operation performance of the controlled sector, the independent variable X is the comprehensive detection index of the operation performance of 17 controlled sectors, and X ismn(n ═ 1, 2.. times, 17), m denotes m sets of time intervals, U is a random error other than the effect of n independent variables on the dependent variable Y, obeying a normal distribution, f denotes a non-linear regression function;
step 2.2 Return from each model according to the coefficient of determinability R2And (3) value, F test and t test, respectively verifying and comparing the fitting degree and the significance of the two regression models, calculating the detection errors of the two regression models on the basis of higher model fitting degree and obvious significance, and selecting the model with the minimum error as the multiple regression model for the fatigue index detection of the controller.
Further, the real-time input data in the step 4 is subjected to a standardization conversion before being input into the multiple regression model; the normalization conversion process is as follows:
mean of 17 indices of sample data according to m groups of time intervalsVariance sjFor real-time input control sector operation performance index tj(j ═ 1, 2...., 17) normalized conversion:converting the data t'jAnd (4) introducing into a multiple regression model.
Further, the method also comprises a step 5, when the comprehensive index of the operation performance of the control sector exceeds a threshold value, the operation performance of the sector responds to an alarm.
A comprehensive detection system for the operation performance of air traffic control sector includes,
the construction module is used for substituting the sample data into the linear regression model and the nonlinear regression model;
the multiple regression model module is used for comparing the linear regression model with the nonlinear regression model through the degree of fitting, significance and error analysis to determine a sector operation performance comprehensive detection multiple regression model;
and the prediction module is used for importing the real-time input data into the multiple regression model to obtain the comprehensive index of the operation performance of the control sector.
Further, the method also comprises the following steps: the device is used for carrying out standardized conversion on input samples and real-time input data;
an alarm module: and when the comprehensive index of the operation performance of the control sector exceeds the threshold value, responding to an alarm by the operation performance of the control sector.
Furthermore, the system also comprises a control sector operation performance detection database, a data leading device and a data calculation device which are coupled with the control sector operation performance detection database;
the data leading device comprises a telegraph data interface, a comprehensive track data interface and a control voice data interface which are respectively coupled with the control sector operation performance detection database;
the data calculation device is used for calculating collected operation performance indexes of the control sector, wherein the operation performance indexes of the control sector comprise a sector operation trafficability index, a sector operation complexity index, a sector operation safety index, a sector operation economy index and a controller workload detection index; the sector trafficability detection indexes respectively comprise sector flow, sector navigation mileage, sector navigation time and sector traffic flow density; the sector complexity detection indexes comprise the climbing times of the sector aircraft, the descending times of the sector aircraft, the speed change times of the sector aircraft and the navigation change times of the sector aircraft; the sector safety detection indexes comprise sector short-term conflict alarm frequency and sector minimum safety height alarm frequency; the sector economy detection indexes comprise sector saturation, sector queuing length, sector aircraft delay frame rate, sector aircraft delay time and sector aircraft average delay time; the controller workload detection indexes comprise the land-air communication channel occupancy rate and the land-air communication times;
the construction module reads the sector operation performance comprehensive detection related indexes and the sector operation performance comprehensive detection sample data from the control sector operation performance detection database; the prediction module reads the real-time input data from the regulated sector operation performance detection database.
The invention has the beneficial effects that:
the invention adopts a quantitative analysis method, realizes accurate and clear judgment and grasp of the operation performance of the control sector by uninterrupted detection and mining calculation analysis of mass operation data, and avoids the defect of empirical management such as easy fatigue and easy subjectivity of manpower management. The index system comprehensively and comprehensively covers various influence factors for controlling the operation performance of the sector. More importantly, the system can meet the actual requirements of an air traffic control unit on real-time detection of the operation performance of the control sector and response warning, and has a data support effect on improving the control operation management level and optimizing the control airspace structure. Comprehensively and comprehensively considering all dimension indexes influencing the operation performance of the control sector; the designed comprehensive detection method and system for the operation performance of the control sector can be applied to air traffic control units and have strong application engineering operability.
Drawings
Fig. 1 is a schematic diagram of a comprehensive detection method for controlling the operation performance of a sector according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a comprehensive detection system for controlling the operation performance of sectors according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an architecture of a comprehensive detection system for operation performance of a second managed sector according to an embodiment of the present invention;
fig. 4 is a network schematic diagram of a comprehensive detection system for operation performance of a second managed sector according to an embodiment of the present invention;
FIG. 5 is a functional diagram of a comprehensive detection system for the operation performance of a second managed sector according to an embodiment of the present invention;
FIG. 6 is a functional diagram of a third embodiment of the invention for integrated track data collection;
FIG. 7 is a schematic diagram of a voice data collection process according to a third embodiment of the present invention;
fig. 8 is a functional diagram of a third telegram data acquisition embodiment of the present invention;
FIG. 9 is a flowchart illustrating a method for comprehensively detecting the operating performance of four managed sectors according to an embodiment of the present invention;
FIG. 10 is a diagram of a four-fold linear regression fit before rounding according to an embodiment of the present invention;
FIG. 11 is a diagram illustrating a four-fold linear regression fitting error before rounding according to an embodiment of the present invention;
FIG. 12 is a diagram illustrating a rounding of the result of a four-fold linear regression fit according to an embodiment of the present invention;
FIG. 13 is a diagram illustrating four multiple linear regression fit error rounds in accordance with an embodiment of the present invention;
fig. 14 is a schematic structural diagram of a comprehensive detection system for operation performance of a five-managed sector according to an embodiment of the present invention;
wherein: 1. building a module; 2. a multiple regression model module; 3. a prediction module; 4. a standardized conversion module; 5. an alarm module: 6. running a performance detection database; 7. a data leading device; 8. a data computing device.
Detailed Description
The invention is further described with reference to the drawings and the preferred embodiments.
Example one
As shown in fig. 1, the method for comprehensively detecting the operation performance of a managed sector disclosed in the present embodiment includes the steps of:
step 1: selecting a control sector operation performance index sample with a certain time interval and a control sector operation performance comprehensive index sample corresponding to the index as sample data;
step 2: establishing a linear regression model and a nonlinear regression model according to the sample data;
and step 3: comparing the linear regression model with the nonlinear regression model through the fitting degree, the significance and the error analysis to determine a multiple regression model for the comprehensive detection of the sector operation performance;
and 4, step 4: and importing the real-time control sector operation performance index into a sector operation performance comprehensive detection multiple regression model to obtain a control sector operation performance comprehensive index.
As shown in fig. 2, this embodiment also discloses a comprehensive detection system for the operation performance of a management sector, which includes,
the construction module is used for substituting the sample data into the linear regression model and the nonlinear regression model;
the multiple regression model module is used for comparing the linear regression model with the nonlinear regression model through the degree of fitting, significance and error analysis to determine a sector operation performance comprehensive detection multiple regression model;
and the prediction module is used for importing the real-time input data into the multiple regression model to obtain the comprehensive index of the operation performance of the control sector.
Regression analysis is an important branch of multivariate statistical analysis, which is a statistical method for detecting one or more response variables (i.e., dependent variables) through a set of detection variables (i.e., independent variables). The case of only one dependent variable is called univariate regression, and a plurality of dependent variables is called multivariate regression. Considering that the operation performance of the control sector is influenced by various factors, setting the comprehensive index of the operation performance of the control sector as a single response variable, and therefore, a unitary multiple regression method (multiple regression for short) is adopted to comprehensively detect the operation performance of the control sector.
According to the linear relationship of the regression function, two basic function models, namely multiple linear regression and multiple nonlinear regression, can be classified. The invention can adopt two models to be used, then selects one with small error as the final prediction model, and can also select one for prediction to simplify the operation process.
The invention adopts a quantitative analysis method, realizes accurate and clear judgment and grasp of the operation performance of the control sector by uninterrupted detection and mining calculation analysis of mass operation data, and avoids the defect of empirical management such as easy fatigue and easy subjectivity of manpower management. The index system comprehensively and comprehensively covers various influence factors for controlling the operation performance of the sector. More importantly, the system can meet the actual requirements of an air traffic control unit on real-time detection of the operation performance of the control sector and response warning, and has a data support effect on improving the control operation management level and optimizing the control airspace structure. Comprehensively and comprehensively considering all dimension indexes influencing the operation performance of the control sector; the designed comprehensive detection method and system for the operation performance of the control sector can be applied to air traffic control units and have strong application engineering operability.
Example two
The embodiment discloses a system architecture which is used as an implementation platform of the comprehensive detection system for controlling the operation performance of the sector and can be used for implementing the detection method.
Fig. 3 shows a comprehensive detection system architecture for the operation performance of the managed sector according to this embodiment. The comprehensive detection system for the operation performance of the air traffic control sector mainly comprises a set of detection database for the operation performance of the control sector and three functional modules of data leading and data calculation. The control sector operation performance detection database classifies and stores the air traffic control data (including radar comprehensive flight path data, telegraph data, VHF recording data and the like) collected by each information collection point, and provides data basis for the control sector operation performance detection.
The data leading device comprises an electricity which is respectively coupled with the operation performance detection database of the control sector
The system comprises a data reporting interface, a comprehensive track data interface and a control voice data interface.
The data calculation device is used for collecting the operation performance indexes of the control sector, wherein the operation performance indexes of the control sector comprise a sector operation trafficability index, a sector operation complexity index, a sector operation safety index, a sector operation economy index and a controller workload detection index. The sector trafficability detection indexes comprise sector flow, sector navigation mileage, sector navigation time and sector traffic flow density; the sector complexity detection indexes comprise the climbing times of the sector aircraft, the descending times of the sector aircraft, the speed change times of the sector aircraft and the navigation change times of the sector aircraft; the sector safety detection indexes comprise sector short-term conflict alarm frequency and sector minimum safety height alarm frequency; the sector economy detection indexes comprise sector saturation, sector queuing length, sector aircraft delay frame rate, sector aircraft delay time and sector aircraft average delay time; the controller workload detection indexes comprise land-air communication channel occupancy rate and land-air communication times.
Fig. 4 and 5 disclose a network deployment and corresponding functional modules for implementing the detection system of the present invention. The system collects real-time data through the data acquisition server, monitors the operation data in real time through the control sector operation performance index detection server and the comprehensive detection server, detects and analyzes the operation performance condition of the control sector, and generates an alarm for the comprehensive index outlier of the control sector operation performance. The network platform of the whole system is physically isolated from the air traffic control production network by depending on the existing management information network, the acquisition platform and the air traffic control production network, so that the one-way transmission of data is ensured, the network attack is prevented, and the safety of related data and the reliability of a production operation system are ensured.
EXAMPLE III
The embodiment discloses a management operation data acquisition scheme, which comprises but is not limited to comprehensive detection of relevant indexes of management sector operation performance, comprehensive detection of management sector operation performance and acquisition of real-time input data.
The research takes the comprehensive index of the operation performance of the control sector as a dependent variable and is marked as Y. The operation performance indexes of the control sector total 17 items, and the independent variable X is recorded as:
X＝{Xi,i＝1,2,…,17}
wherein the sector trafficability detection index is { X }1,X2,X3,X4Respectively representing sector flow, sector navigation mileage, sector navigation time and sector traffic flow density; sector complexity detection index is { X }5,X6,X7,X8Respectively representing the climbing times of the sector aircraft, the descending times of the sector aircraft, the speed changing times of the sector aircraft and the navigation changing times of the sector aircraft; sector security detection index is { X9,X10Indicate sector short-term collisions, respectivelyAlarm frequency and sector minimum safe altitude alarm frequency; sector economy detection index is { X11,X12,X13,X14,X15Respectively representing sector saturation, sector queuing length, sector aircraft delay frame rate, sector aircraft delay time and sector aircraft average delay time; the controller workload detection index is { X }16,X17And indicating the land-air communication channel occupancy rate and the land-air communication times respectively. The parameter indexes are mainly acquired from the following aspects.
Integrated track acquisition
The air traffic control automation system performs data fusion and data processing on monitoring signals of a first air traffic control radar, a second air traffic control radar and the like, outputs comprehensive flight path information, and mainly comprises a radar front-end processing module, a radar data processing module and a flight plan processing module.
The system collects comprehensive flight path data from the air traffic control automation system and transmits the data in a network mode. And the data acquisition server analyzes the acquired comprehensive track data, and acquires information such as the height, the speed and the position of the aircraft for index calculation.
The integrated track data acquisition module comprises a track data format conversion module, a track data analysis module and a track data storage module, as shown in fig. 6.
Voice data collection
The controller and the pilot realize the land-air voice communication through the VHF communication system. The system consists of a Very High Frequency (VHF) transceiver station and a signal transmission and processing device.
The voice data acquisition is connected with the voice signal acquisition from the distribution frame, and the land-air communication information is decoded and stored for the analysis of the traffic load controlled and directed by the controller.
As shown in fig. 7, seat voice data is introduced into the system data acquisition server from the internal telephone system distribution frame through the shielded network cable, and the voice channel corresponds to the seat (sector).
The voice signal is from the high impedance (the recording module is 200K ohm) collection (controller ground-to-air conversation) seat pronunciation on the distribution frame, does not influence ground-to-air conversation and voice recording, adopts many pairs of cable wires to lead the voice signal from the distribution frame to the voice processor, realizes the collection and the analysis to a plurality of seat pronunciation.
Telegraph data acquisition
The fixed telegraph pivot device, the message of the civil aviation flight dynamic fixed telegraph, is formed by arranging a plurality of specified data marshalling according to the fixed order.
The telegram data acquisition module is used for leading telegram data output by the telegram forwarding system, carrying out format conversion, analysis and storage on the data, and acquiring flight plan data, as shown in fig. 8. The module analyzes the received telegram data and stores the analyzed telegram data in a database for storage, and the data is used for calculating the operation performance index of the sector.
Managed sector operation performance indicator detection
The system collects real-time operation data such as comprehensive flight paths, flight plans, voice communication and the like from an air traffic control automation system, a retransmission system and a telephone system, establishes a control sector operation performance detection index system by taking relevant files of the International civil aviation organization (ICAO for short, the same below) and the Federal Aviation Administration (FAA) as references, and outputs a control sector operation performance index detection result based on the index system. The system provides a good human-computer interface for a user to check various real-time statistical graphs.
Sector operation trafficability index detection
(1) Sector traffic detection
Sector traffic refers to the number of aircraft frames administered per unit time by the regulatory sector. The system acquires the position information of the air aircraft by leading the air traffic control automation system to synthesize track data, and calculates the sector flow by combining the configured sector boundary information.
(2) Sector voyage mileage detection
The sector navigation mileage refers to the sum of the aircraft navigation mileage governed by the control sector per unit time. Setting the number of times of aircraft frames in unit time of a control sector as Q, and the navigation mileage of the Q-th aircraft as MqThe navigation mileage of the sector is MtotalThen, thenAnd acquiring the position information of the air aircraft by leading the air traffic control automation system to synthesize track data, and calculating to obtain the sector navigation mileage by combining with the configured sector boundary information.
(3) Sector voyage time detection
The sector voyage time refers to the sum of the managed sector's aircraft voyage time per unit time. Setting the number of times of aircraft frames in unit time of a control sector as Q, and the navigation time of the Q-th aircraft as TqThe sector navigation time is TtotalThen, thenAnd acquiring the position information of the air aircraft by leading the air traffic control automation system to synthesize track data, and calculating to obtain the sector flight time by combining the configured sector boundary information.
(4) Sector traffic flow density detection
Sector traffic flow density is a measure of how dense the aircraft is in the jurisdiction of a control sector per unit time. Let the sector area be SsecThe number of times of the aircraft frame in unit time of the control sector is Q, and the traffic flow density of the sector in unit time is DsecThen D issec＝Q/Ssec. The system reads the configured sector boundary information to obtain the sector area, and the sector traffic flow density is obtained by combining the sector flow calculation.
Sector operation complexity indicator detection
(1) Sector aircraft climb number detection
The sector aircraft climbing times refer to the sum of the aircraft climbing times governed in unit time of the control sector. Setting the number of times of aircraft frames in unit time of a control sector as Q and the number of times of climbing of the Q-th aircraft as CqAnd the number of times of climb of the sector aircraft is CtotalThen, thenAnd leading and connecting real-time comprehensive track data, monitoring and counting the climbing condition of aircrafts in the sector, wherein one aircraft climbs one altitude layer for one time, and calculating to obtain the climbing times of the aircrafts in the sector.
(2) Sector aircraft descent number detection
The sector aircraft descent number refers to the sum of the aircraft descent numbers in the unit time of the control sector. Setting the number of times of aircraft frames in unit time of a control sector as Q and the number of times of descent of the Q-th aircraft as DqSector number of aircraft descent DtotalThen, thenAnd leading and connecting real-time comprehensive track data, monitoring and counting the descending condition of aircrafts in the sector, wherein one aircraft descends for one time in one altitude layer, and calculating the climbing times of the aircrafts in the sector.
(3) Sector aircraft speed change number detection
The number of times the aircraft changes speed in a sector refers to the sum of the number of times the aircraft speed changes per unit time in the regulatory sector. Setting the number of times of aircraft frames in unit time of a control sector as Q and the number of times of speed change of a Q-th aircraft as SqThe number of speed changes of the sector aircraft is StotalThen, thenLeading-in real-time synthetic track data, speed change of aircraft in sectorAnd monitoring and counting, wherein the speed of one aircraft continuously changes to reach the set parameter of one speed change, and the speed change times of the aircraft in the sector are calculated.
(4) Sector aircraft diversion times detection
The sector aircraft diversion times are the sum of the aircraft heading change times in the unit time of the control sector. Setting the number of times of aircraft frames in unit time of a control sector to be Q, and the number of times of flight change of a Q-th aircraft to be HqSector aircraft number of changes is HtotalThen, thenAnd (3) leading and connecting real-time comprehensive track data, monitoring and counting the course change condition of the aircrafts in the sectors, continuously changing the course of one aircraft to reach the set parameter of one course change, and calculating to obtain the times of the aircraft changing the course of the sector.
Sector operation security index detection
(1) Sector short term collision warning frequency detection
The sector short-term conflict warning frequency refers to the number of aircraft short-term conflict warnings administered in unit time of a control sector, and is obtained through STCA warning data statistics of a system leading and connecting an air traffic control automation system.
(2) Sector minimum safe altitude alarm frequency detection
The alarm frequency of the lowest safe altitude of the sector refers to the alarm times of the lowest safe altitude of the aircraft administered in unit time of the control sector, and is obtained by MSAW alarm data statistics of a system leading and receiving air traffic control automation system.
Sector operation economy index detection
(1) Sector saturation detection
The sector saturation refers to the ratio of the flow rate to the capacity of the control sector in unit time, and the maximum number of aircrafts that can be administered in unit time of the control sectorScaled to a regulated sector capacity. Setting the aircraft frame times in unit time of the control sector as Q, the capacity of the control sector as C and the saturation of the sector as SatusecThen SatusecQ/C. And the system reads the configured sector capacity parameters and obtains the sector saturation by combining the sector flow calculation.
(2) Sector queuing length detection
In the aircraft administered in the unit time of the control sector, if a queuing condition such as spiral waiting occurs when entering the sector, the aircraft is defined as a queuing aircraft, and the sector queuing length is defined as the number of the queuing aircraft. And leading the system to connect the comprehensive track data, judging whether the target aircraft is in circling waiting at the boundary of the sector, and calculating to obtain the sector queuing length.
(3) Sector aircraft delay rate detection
Among the aircrafts governed by the control sector in unit time, the aircraft with the voyage time exceeding the normal range is defined as a delay aircraft, and the part of the voyage time exceeding the normal range is defined as the delay time. Setting the number of times of aircraft frames in unit time of a control sector as Q, the number of times of delayed frames of the aircraft in the sector as d, and the rate of the delayed frames of the aircraft in the sector as HeatsecThen at is just drivesecd/Q. And leading and connecting the comprehensive track data, comparing the actual flight time of each aircraft in the control sector with the empirical flight time, if the actual flight time is greater than the empirical flight time, determining that the aircraft is delayed, and calculating to obtain the delayed frame rate of the aircraft in the sector.
(4) Sector aircraft latency detection
Among aircrafts governed by a control sector within unit time, an aircraft with the voyage time exceeding a normal range is defined as a delay aircraft, the part of the voyage time exceeding the normal range is defined as delay time, and the sum of the delay time is defined as the delay time of the aircraft in the sector. Setting the number of times of aircraft frames in unit time of a control sector as Q, and setting the Delay time of the Q-th aircraft as DelayqSector aircraft latency ofDelaysecThen, thenAnd leading and connecting the comprehensive flight path data, comparing the actual flight time of each aircraft in the control sector with the empirical flight time, if the actual flight time is greater than the empirical flight time, determining that the aircraft is delayed, and calculating to obtain the delay time of the aircraft in the sector.
(5) Sector aircraft average delay time detection
Among the aircrafts governed by the control sector in unit time, the aircraft with the voyage time exceeding the normal range is defined as a delay aircraft, and the part of the voyage time exceeding the normal range is defined as the delay time. Setting Delay time of sector aircraft as DelaysecThe number of delayed frames of the sector aircraft is Q, and the average delay time of the sector aircraft is DavgsecThen Davgsec＝Delaysecand/Q. And leading and connecting the comprehensive flight path data, comparing the actual flight time of each aircraft in the control sector with the empirical flight time, if the actual flight time is greater than the empirical flight time, determining that the aircraft is delayed, and calculating to obtain the average delay time of the aircraft in the sector.
Controller workload detection
The controller needs to bear physical and mental stress for completing the control task, the stress can be converted into time consumption, the borne stress is relieved through the time consumption, and the requirement for completing the objective task is met, and the time consumption is the size of the workload of the controller. In the measurable controller work time consumption, the land-air communication channel occupancy rate and the land-air communication frequency are basic indexes reflecting the controller work load.
(1) Land-air communication channel occupancy detection
The land-air communication channel occupancy rate refers to the land-air communication time length ratio in the unit time of the control sector. Setting the control sector to talk over the air and the land R times in unit time TThe time length of r land-air calls is TrThe land-air communication channel occupancy rate is TrateThen, thenLeading and connecting the control voice data, analyzing the starting time and the ending time of the communication between the controller and the pilot of the corresponding sector control seat, then accumulating the time length of each section of communication to obtain the sector land-air communication time length, and further calculating to obtain the land-air communication channel occupancy rate.
(2) Land-air call number detection
The land-air call times refer to the land-air call times in the unit time of the control sector. The system analyzes the control voice data, each call is counted as a land-air call, and the number of calls in unit time is accumulated to obtain the number of land-air calls.
Example four
The embodiment mode discloses a comprehensive detection method for the operation performance of a control sector, which can be realized by selecting a hardware platform in the second embodiment, the selection of relevant indexes of the comprehensive detection of the operation performance of the control sector is related, and the collection of sample data and real-time input data of the comprehensive detection of the operation performance of the control sector can refer to the third embodiment.
In the embodiment, multiple linear regression models and multiple nonlinear regression models are simultaneously adopted, and the model with the minimum detection error is selected from the multiple linear regression models and the multiple nonlinear regression models to serve as the final prediction model.
(1) Multiple linear regression, which uses a linear function to fit multiple independent variables Xi(i-1, 2, …, n) and a single dependent variable Y, thereby determining a parameter b of the multiple linear regression modeli(i ═ 0,1,2, …, n), regressed into the original hypothesis equation, and the trend of the dependent variable was detected by the regression equation. The general form of the multiple linear regression model is:
Y＝b0+b1X1+b1X2+…+biXi+…+bnXn+ mu (formula 4.1)
Where μ is the random error except for the effect of the n independent variables on the dependent variable Y, obeying a normal distribution.
Assuming that the statistical sample has m sets of statistics, the matrix form of the multiple linear regression model can be expressed as:
y ═ XB + U (formula 4.2)
Wherein,
(formula 4.3)
(2) Multiple nonlinear regression, assuming a nonlinear relationship between independent variables (detection indexes) and dependent variables (sector performance), a multiple nonlinear model can be generally expressed as:
Y＝f[(b1,b2,…,bk)；X1,X2,…,Xn](formula 4.4)
The nonlinear regression function can adopt forms of quadratic function, power function, exponential function, hyperbolic function and the like according to the characteristics of sample data. The present embodiment is illustrated by a quadratic function:
(formula 4.5)
Parameter b of multiple regression modeliAfter estimation, i.e. after solving the sample regression function, the sample regression function needs to be further subjected to statistical tests, including fitness test (coefficient of certainty R)2) Significance tests (p-values), and confidence interval estimates of parameters, etc. Then, the detection error is calculated, and finally, a model with small error is selected as a final prediction model.
The control sector operation performance comprehensive index algorithm based on multiple regression mainly comprises four parts, namely the construction of a regression model, the comparison and selection of the regression model, the comprehensive detection of the control sector operation performance and the control sector operation performance response alarm. Referring to fig. 9, the specific algorithm steps are:
step 1: selecting variables
Referring to the third embodiment, the present study uses the comprehensive index of the operation performance of the control sector as a dependent variable, which is denoted as Y. The operation performance indexes of the control sector total 17 items, and the independent variable X is recorded as:
X＝{Xii ═ 1,2, …,17} (formula 4.6)
Wherein the sector trafficability detection index is { X }1,X2,X3,X4Respectively representing sector flow, sector navigation mileage, sector navigation time and sector traffic flow density; sector complexity detection index is { X }5,X6,X7,X8Respectively representing the climbing times of the sector aircraft, the descending times of the sector aircraft, the speed changing times of the sector aircraft and the navigation changing times of the sector aircraft; sector security detection index is { X9,X10Respectively representing a sector short-term conflict alarm frequency and a sector minimum safe height alarm frequency; sector economy detection index is { X11,X12,X13,X14,X15Respectively representing sector saturation, sector queuing length, sector aircraft delay frame rate, sector aircraft delay time and sector aircraft average delay time; the controller workload detection index is { X }16,X17And indicating the land-air communication channel occupancy rate and the land-air communication times respectively.
And obtaining the input values of the 17 indexes according to M groups of sample input data with the time length of hours. Meanwhile, according to the input value X of the index, the qualification control expert classifies the operation performance of the sample control sector as a comprehensive index Y of the operation performance of the control sector (Y is 1,2,3,4,5 respectively indicating that the operation performance of the control sector is the best, better, generally worse, worst). Examples of the obtained sample index data are as follows:
table 1 example regulatory sector operational performance sample indicator data
Step 2: data processing
Considering that different indexes have different dimensions and different magnitude order, in order to facilitate regression analysis of the model, standard conversion needs to be carried out on index data.
Let xij、x′ijRespectively representing the raw data of the ith sample and the normalized and converted data,sjrespectively, the mean and variance of the j (j ═ 1,2, …,17) th index data, then:
(formula 4.7)
Normalizing the data x 'after conversion'ijAs input data for regression analysis.
And step 3: constructing a regression model
Referring to the second and third embodiments, a multiple linear regression model and a multiple nonlinear regression model are respectively constructed, wherein the nonlinear regression model is in the form of a quadratic function. Obtaining parameter estimation values of two types of functions by fitting sample dataAnd sample output valueWhereinB represents formula 4.40～bnOr b of formula 4.50～b2nAn estimate of (d).
And 4, step 4: test regression model
Parameters of multiple regression modelsAfter estimation, i.e. after solving the sample regression function, the sample regression function needs to be further subjected to statistical tests, including fitness test (coefficient of certainty R)2Coefficient of certainty), significance test (p-value), and confidence interval estimation of the parameters, etc. Return of a coefficient of determinability R from the model2And (3) value, F test and t test, respectively verifying and comparing the fitting degree and the significance of the 2 regression models, calculating the detection errors of the two regression models on the basis of higher model fitting degree and obvious significance, and selecting the model with the minimum error as the final detection model and the detection model for controlling the operation performance of the sector.
And 5: regression model result output
Mean of 17 indices of sample data according to m groups of time intervalsVariance sjControl sector operation performance index for real-time inputAnd (3) carrying out standardized conversion:converting the data t'jAnd importing the data into a multiple regression model for comprehensive detection of the operation performance of the sector to obtain the classification level of the operation performance of the current control sector.
Step 6: managed sector operational performance response alert
And according to the comprehensive detection result of the operation performance of the control sector, responding to the alarm standard by referring to the set comprehensive detection of the operation performance of the control sector, and generating an alarm for the system when the alarm standard is met.
According to the algorithm process, 648 groups of all ACC01 sector related index data are collected, sample data are fitted by respectively adopting a linear function and a nonlinear function (quadratic function), and R of the two functions is obtained through fitting calculation2P-value, and mean error, maximum error, minimum error, etc. of the fitting performance data. Since the sector operation performance comprehensive index is an integer, fitting effects are respectively compared according to whether rounding (rounding) processing is carried out on sector performance comprehensive index results of the multiple linear regression model and the non-linear regression model. The conclusion is as follows:
TABLE 2 multiple regression fitting effect comparison
According to the table, the fitting degree and the significance of the linear function and the nonlinear function are good; however, the fitting error of the linear function is smaller than that of the non-linear function. Therefore, the method selects the rounded linear function as a detection model for controlling the operation performance of the sector according to the principle of minimum error. The effect before the multiple linear regression fitting result is rounded is shown in fig. 10, the effect before the multiple linear regression fitting error is rounded is shown in fig. 11, the effect after the multiple linear regression fitting result is rounded is shown in fig. 12, and the effect after the multiple linear regression fitting error is rounded is shown in fig. 13.
In summary, the control sector operation performance comprehensive index algorithm selects a multiple linear regression rounding model based on the following formula:
(formula 4.8)
After the detection method and the corresponding system are put into operation, corresponding management needs to be carried out. The recommended system management is as follows:
managing user authority, distributing user name and authority to each user, ensuring data safety and preventing data leakage.
Each user corresponds to 0 to a plurality of roles, and each role can be flexibly allocated with access and operation authorities by management personnel.
Setting parameters necessary for system operation, including map parameters, telegraph processing and radar data processing parameters, long-term schedule, system display parameter setting and other parameters required to be set.
Providing log management function to record system operation and keep important data operation information. The method comprises the following steps: the device comprises a log recording module, a log query module and a log backup and clearing module.
Providing parameter configuration function, and providing tool for system maintenance personnel.
Sixthly, providing a data import and export function.
The proposed configuration for implementing the detection method and system of the present invention is as follows:
| serial number | Device | Number of |
| 1 | Sector operation performance detection calculation server | 1 |
| 2 | Data acquisition processing server | 1 |
| 3 | Monitoring data gateway | 2 |
| 4 | Voice card | 1 |
| 5 | Active distributor | 1 |
| 6 | Switch | 1 |
| 7 | Zhahong 42U cabinet | 1 |
| 8 | KVM switcher | 1 |
EXAMPLE five
The comprehensive detection method for the operation performance of the control sector of the embodiment comprises the following steps:
and constructing a multiple regression model according to the comprehensive detection related indexes of the operation performance of the control sector and the comprehensive detection sample data of the operation performance of the control sector. Obtaining input values of the comprehensive detection related indexes of the operation performance of the control sector according to M groups of sample input data with the time length of hour; obtaining the real-time input data from a control sector operation performance detection database;
real-time input data is imported into a multiple regression model.
Considering that different indexes have different dimensions and different magnitude order, before constructing a multiple regression model, firstly, carrying out standardized conversion on input control sector operation performance comprehensive detection related indexes and control sector operation performance comprehensive detection sample data; correspondingly, the real-time input data of the imported sample regression function is firstly subjected to standardized conversion, so that the regression analysis of the model can be facilitated;
the multiple regression model comprises a multiple linear regression model and a multiple non-linear regression model, wherein the non-linear regression model adopts a quadratic function. The model function is referred to the above embodiments.
Fitting the comprehensive detection sample data of the operation performance of the controlled sector through a multiple linear regression model and a multiple non-linear regression model respectively to obtain two groups of sample regression functions, and performing statistical test on the two groups of sample regression functions, wherein the statistical test step comprises the test of the degree of fitting and the significance, and when the degree of fitting and the significance exceed preset values, the detection error is calculated;
importing the real-time input data into a sample regression function with the minimum detection error to obtain a comprehensive index of the operation performance of the control sector;
and when the comprehensive index of the operation performance of the controlled sector exceeds the threshold value, responding to an alarm by the operation performance of the sector.
The operation performance indexes of the control sectors comprise a sector operation trafficability index, a sector operation complexity index, a sector operation safety index, a sector operation economy index and a controller workload detection index.
The sector trafficability detection indexes respectively comprise sector flow, sector navigation mileage, sector navigation time and sector traffic flow density; the sector complexity detection indexes comprise the climbing times of the sector aircraft, the descending times of the sector aircraft, the speed change times of the sector aircraft and the navigation change times of the sector aircraft; the sector safety detection indexes comprise sector short-term conflict alarm frequency and sector minimum safety height alarm frequency; the sector economy detection indexes comprise sector saturation, sector queuing length, sector aircraft delay frame rate, sector aircraft delay time and sector aircraft average delay time; the controller workload detection indexes comprise land-air communication channel occupancy rate and land-air communication times.
As shown in fig. 14, the present embodiment also discloses a comprehensive detection system for the operation performance of the air traffic control sector. The system comprises a control sector operation performance detection database, a data leading device and a data calculation device, wherein the data leading device is coupled with the control sector operation performance detection database.
The data leading device comprises a telegraph data interface, a comprehensive track data interface and a control voice data interface which are respectively coupled with the control sector operation performance detection database; the data calculation device is used for collecting the operation performance indexes of the control sector, wherein the operation performance indexes of the control sector comprise a sector operation trafficability index, a sector operation complexity index, a sector operation safety index, a sector operation economy index and a controller workload detection index.
The sector trafficability detection indexes respectively comprise sector flow, sector navigation mileage, sector navigation time and sector traffic flow density; the sector complexity detection indexes comprise the climbing times of the sector aircraft, the descending times of the sector aircraft, the speed change times of the sector aircraft and the navigation change times of the sector aircraft; the sector safety detection indexes comprise sector short-term conflict alarm frequency and sector minimum safety height alarm frequency; the sector economy detection indexes comprise sector saturation, sector queuing length, sector aircraft delay frame rate, sector aircraft delay time and sector aircraft average delay time; the controller workload detection indexes comprise land-air communication channel occupancy rate and land-air communication times.
The construction module is used for substituting the sample data into the linear regression model and the nonlinear regression model;
the multiple regression model module is used for comparing the linear regression model with the nonlinear regression model through the degree of fitting, significance and error analysis to determine a sector operation performance comprehensive detection multiple regression model;
and the prediction module is used for importing the real-time input data into the multiple regression model to obtain the comprehensive index of the operation performance of the control sector.
A standardization conversion module: the device is used for carrying out standardized conversion on input samples and real-time input data;
an alarm module: and when the comprehensive index of the operation performance of the control sector exceeds the threshold value, responding to an alarm by the operation performance of the control sector.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (11)
1. A comprehensive detection method for operation performance of air traffic control sectors is characterized by comprising the following steps:
step 1: selecting a control sector operation performance index sample with a certain time interval and a control sector operation performance comprehensive index sample corresponding to the index as sample data;
step 2: establishing a linear regression model and a nonlinear regression model according to the sample data;
and step 3: comparing the linear regression model with the nonlinear regression model through the fitting degree, the significance and the error analysis to determine a multiple regression model for the comprehensive detection of the sector operation performance;
and 4, step 4: and importing the real-time control sector operation performance index into a sector operation performance comprehensive detection multiple regression model to obtain a control sector operation performance comprehensive index.
2. The method according to claim 1, wherein the control sector operation performance indicators in step 1 include a sector operation trafficability indicator, a sector operation complexity indicator, a sector operation security indicator, a sector operation economy indicator, and a controller workload detection indicator.
3. The air traffic control sector operation performance comprehensive detection method according to claim 2, wherein the sector trafficability detection index includes sector flow, sector voyage mileage, sector voyage time, and sector traffic flow density, respectively;
the sector complexity detection indexes comprise the climbing times of a sector aircraft, the descending times of the sector aircraft, the speed change times of the sector aircraft and the navigation change times of the sector aircraft;
the sector safety detection indexes comprise sector short-term conflict alarm frequency and sector minimum safety height alarm frequency;
the sector economy detection indexes comprise sector saturation, sector queuing length, sector aircraft delay frame rate, sector aircraft delay time and sector aircraft average delay time;
the controller workload detection indexes comprise land-air communication channel occupancy rate and land-air communication times;
the restricted sector operation performance indicator is 17.
4. The method according to claim 1 or 3, wherein the step 2 is to perform a standardized transformation on the sample data; the normalization conversion process is as follows:
let xij、x′ijRespectively representing the raw data of the ith sample and the normalized and converted data,sjrespectively representing the mean and variance of the jth index data, then:
5. the method for comprehensively detecting the operation performance of the air traffic control sector according to claim 4, wherein the step 2 comprises the following steps:
according to the standardized sample data x'ujEstablishing multiple linear regression models and multiple nonlinear regression models respectively (i is 1,2, …, m, j is 1,2, … n), and solving coefficients bu，
Wherein the multiple linear regression model is:
Y＝XB+U
wherein,
the multiple nonlinear regression model is:
Y＝f[(b1,2,…,bk)；X1,X2,…,Xn]
the method comprises the steps that a dependent variable Y is a control sector operation performance comprehensive index, an independent variable X is n control sector operation performance comprehensive detection indexes, m represents a control sector operation performance index sample under m groups of time intervals, U is a random error except for the influence of m independent variables on the dependent variable Y and obeys normal distribution, and f represents a nonlinear regression function.
6. The method for comprehensively detecting the operation performance of the air traffic control sector according to claim 1 or 5, wherein the step 3 comprises the following steps:
from the return of the coefficients of the decision R of each model2And (4) value, F test and t test, respectively verifying and comparing the fitting degree and the significance of the two regression models, calculating the detection errors of the two regression models on the basis of high model fitting degree and obvious significance, and selecting the model with the minimum error as the multiple regression model for comprehensive detection of the sector operation performance.
7. The method as claimed in claim 1, wherein the real-time input data in step 4 is subjected to a normalization conversion before being input into the multiple regression model; the normalization conversion process is as follows:
mean value of n indexes of sample data according to m groups of time intervalsVariance sjControl sector operation performance index for real-time inputAnd (3) carrying out standardized conversion:converting the dataAnd importing the data into a multiple regression model for comprehensive detection of the operation performance of the sector.
8. The method of claim 1 further comprising the step of responding to an alarm when the composite index of air traffic control sector performance exceeds a threshold.
9. A comprehensive detection system for the operation performance of air traffic control sectors is characterized by comprising,
the construction module is used for substituting the sample data into the linear regression model and the nonlinear regression model;
the multiple regression model module is used for comparing the linear regression model with the nonlinear regression model through the degree of fitting, significance and error analysis to determine a sector operation performance comprehensive detection multiple regression model;
and the prediction module is used for importing the real-time input data into the multiple regression model to obtain the comprehensive index of the operation performance of the control sector.
10. The air traffic control sector operational performance integrated detection system of claim 9, further comprising,
a standardization conversion module: the device is used for carrying out standardized conversion on input samples and real-time input data;
an alarm module: and when the comprehensive index of the operation performance of the control sector exceeds the threshold value, responding to an alarm by the operation performance of the control sector.
11. The integrated air traffic control sector operation performance detection system according to claim 10, further comprising a control sector operation performance detection database, a data docking device and a data computing device coupled to said control sector operation performance detection database;
the data leading device comprises a telegraph data interface, a comprehensive track data interface and a control voice data interface which are respectively coupled with the control sector operation performance detection database;
the data calculation device is used for acquiring the operation performance indexes of the control sector, wherein the operation performance indexes of the control sector comprise a sector operation trafficability index, a sector operation complexity index, a sector operation safety index, a sector operation economy index and a controller workload detection index; the sector trafficability detection indexes respectively comprise sector flow, sector navigation mileage, sector navigation time and sector traffic flow density; the sector complexity detection indexes comprise the climbing times of the sector aircraft, the descending times of the sector aircraft, the speed change times of the sector aircraft and the navigation change times of the sector aircraft; the sector safety detection indexes comprise sector short-term conflict alarm frequency and sector minimum safety height alarm frequency; the sector economy detection indexes comprise sector saturation, sector queuing length, sector aircraft delay frame rate, sector aircraft delay time and sector aircraft average delay time; the controller workload detection indexes comprise the land-air communication channel occupancy rate and the land-air communication times;
the construction module reads the sector operation performance comprehensive detection related indexes and the sector operation performance comprehensive detection sample data from the control sector operation performance detection database; the prediction module reads the real-time input data from the regulated sector operation performance detection database.
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