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
- Publication number
- CN105225193A CN105225193A CN201510641511.6A CN201510641511A CN105225193A CN 105225193 A CN105225193 A CN 105225193A CN 201510641511 A CN201510641511 A CN 201510641511A CN 105225193 A CN105225193 A CN 105225193A
- Authority
- CN
- China
- Prior art keywords
- sector
- runnability
- index
- regression model
- control
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Traffic Control Systems (AREA)
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 present invention relates to monitoring field, is espespecially a kind of air traffic control sector runnability method for comprehensive detection and system.
Background technology
Along with the development of air-transport industry, in order to ensure that the safety of all kinds of flying activity is with orderly, air traffic control service arises at the historic moment and is constantly developed perfect, is tending towards ripe to the eighties in 20th century.Modern wireless air traffic control service is to the effect that: air traffic controller is (referred to as " controller ", lower same) rely on modern communications, navigation, surveillance technology, management is implemented to administrative aircraft and controls, coordinate and instruct its motion path and pattern, hit aircraft and barrier in airdrome maneuver district mutually to prevent aerial aircraft and aircraft to bump against, safeguard and accelerate the olderly flowage of air traffic.Air traffic control sector (referred to as " control sector ", lower same) is the fundamental space unit of air traffic control (referred to as " control ", lower same).Generally, be set to some control sectors for aircraft provides the spatial domain of air traffic control service to be drawn, the corresponding controller of each control sector works seat.Control sector runnability is that the technical index of aircraft operation situation in control sector is refined, and has both reflected that controller provided quality and the level of regulatory service to administrative control sector, and has reflected again specific control zone effective utilization.Therefore, be adjustment control operation reserve, the basis optimizing control zone structure and prerequisite to effective detection of control sector runnability.
Patent documentation CN104636890A disclosed a kind of air traffic control sector dynamic debugging system and method on 05 20th, 2015.The method comprises the following steps: (1), based on each sector data in the database of spatial domain, generating can for the set of preferred feasible sector combination; (2) based on radar historical data, the history control workload coefficient of each sector is calculated; (3) the current flight position of reading in real time according to radar and state of flight information, calculate the flight flow entering each sector; (4) according to flight planning on the same day, the flight flow entering each sector is calculated; (5) based on real-time traffic and the plan flow of above-mentioned historical work-load coefficient and flight, calculate the control workload of each sector, then, the optimum sector combination in the set of described feasible sector combination is calculated based on optimum sector combination preference pattern.
At present, research for air traffic control sector runnability is less, major part research concentrates on following several isolated aspect: (1) air traffic current density, be divided into Strategy & Tactics two aspect, wherein the former major embodiment is spatial domain complexity profile, and the latter's major embodiment is control unit air traffic congestion deciding degree.At present, transport air flow density index is still added up as mainly presenting using the aircraft sortie of control unit in application.(2) control operational safety performance, comprises quantitatively and qualitative two aspects.Quantitative aspect, International Civil Aviation Organization (ICAO) according to collision risk analysis formulate total Security Target grade (TLS) be 1.5 × 10-8 time fatal aircraft accident/pilot time, and China's Civil Aviation ATM system according to danger close to venture analysis using accident proneness ten thousand sortie rate as key safety index.Qualitative aspect, ICAO recommends to adopt and threatens error management (ThreatandErrorManagement, TEM) or day-to-day operation safety monitoring (NormalOperationsSafetySurvey, NOSS) method, control operational safety performance evaluation is qualitatively implemented.Domestic scholars establishes security risk assessment index system respectively around 4 class factors such as people, machine, ring, management, and has carried out index weights analysis.(3) control operational efficiency performance, mainly around airliner delay index aspect.At present, external airliner delay statistical indicator relates to and incurs loss through delay sortie rate and delay time at stop.The refinement statistics of civil aviaton of China shortcoming airliner delay time, defines in airliner delay reason, statistical indicator design, urgently to improve in statistical method and flow process etc.(4) ATC controller workload is the important consideration of control sector Capacity Assessment.Foreign scholar, from the angle of physiology/behavioural characteristic, subjective test and appraisal, job breakdown, proposes the physical signs such as reaction, heart rate, cardiogram, blood pressure, body fluid of electric shock skin respectively, the behavioral indicators such as equipment operating number of times, the empty air time record in land; The subjective evaluation technology such as ATWIT technology, NASA – TLX scale, SWAT scale and MCH method; DORATASK, MBB method, RAMS method etc. weigh the method for controller's working time.Domestic scholars has developed subjective assessment method, proposes the ATC controller workload evaluation model based on extension science.
At present for the existing research contents of air traffic control sector runnability, mainly have the following disadvantages: (1) research method aspect, qualitative examination is more, and quantitative examination is less, and objectivity is not enough.(2) Testing index aspect, index dimension is comparatively single, not comprehensively, comprehensively, causes comprehensive detection scarce capacity.(3) application aspect, existing research still rests on the laboratory study stage, serves primarily in strategic decision, and few towards the practical engineering application of air traffic control unit.Due to above-mentioned deficiency, the domestic and international research detected for control sector runnability is at present caused to be short of all to some extent in objectivity, comprehensive, operability etc., particularly for needing in reality, real-time this demand of detection and response alarm being carried out to control sector runnability, not yet effectively realizing.
Summary of the invention
The invention provides a kind of more efficiently, objectivity, the air traffic control sector runnability method for comprehensive detection of forecasting accuracy and system can be improved.
The object of the invention is to be achieved through the following technical solutions:
A kind of air traffic control sector runnability method for comprehensive detection, comprises 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.
Further, the described control sector running performance index in described step 1 comprises sector and runs road ability index, sector operation complexity profile, sector safety in operation index, sector performance driving economy index and ATC controller workload Testing index.
Further, described sector road ability Testing index comprises sector flow, sector shipping kilometre, sector hours underway and sector traffic flow density respectively;
Described sector complicacy Testing index comprise sector aircraft climb number of times, sector aircraft decline number of times, sector aircraft changes fast number of times, sector aircraft changes flight number number;
Described sector security Testing index comprises sector short term collision alert frequency and sector minimum safe altitude alert frequency;
Described sector economy Testing index comprises sector saturation degree, sector queue length, sector aircraft delay sortie rate, sector aircraft delay time at stop, sector aircraft mean delay time;
Described ATC controller workload Testing index comprises the empty talk channel occupancy in land, the empty talk times in land.
Further, before execution step 2, first standardization conversion is carried out to sample data; Standardization transfer process is as follows:
Make x
ij, x '
ijrepresent the raw data of i-th sample data and the data after standardization conversion respectively,
s
jrepresent average and the variance of a jth index sample respectively, then:
Data x ' after standardization is changed
ij, as the input data setting up linear regression model (LRM) and nonlinear regression model (NLRM).
Further, described step 2 comprises the steps:
Step 2.1, according to above-mentioned sample data, is set up Multivariate regression model and multiple nonlinear regression model (NLRM) respectively, and is solved coefficient b
i,
Wherein Multivariate regression model is:
Y＝XB+U
Wherein,
Multiple nonlinear regression model (NLRM) is:
Y＝f[(b
1,b
2,…,b
k)；X
1,X
2,…,X
n]
Wherein dependent variable Y is control sector runnability aggregative index, and independent variable X is 17 control sector runnability comprehensive detection indexs, x
mn(n=1,2 ...., 17), m represents the m group time interval, and U is except n independent variable is on the stochastic error except the impact of dependent variable Y, Normal Distribution, and f represents nonlinear solshing;
The coefficient of determination R that step 2.2 returns according to each model
2value, F inspection, t inspection, verify respectively and compare degree of fitting, the conspicuousness of two kinds of regression models, on the obvious basis of, conspicuousness higher in model-fitting degree, calculate the metrical error of two kinds of regression models, and choose the minimum a kind of model of error, as the multiple regression model that controller's fatigue index detects.
Further, the real time input data in described step 4 will carry out standardization conversion before input multiple regression model; Standardization transfer process is as follows:
According to the average of 17 indexs of the sample data in the m group time interval
variance s
j, to real-time import control sector running performance index t
j(j=1,2 ...., 17) carry out standardization conversion:
by the data t ' after conversion
jimport in multiple regression model.
Further, the method also comprises step 5, when control sector runnability aggregative index exceeds threshold value, and sector runnability response alarm.
A kind of air traffic control sector runnability comprehensive detection system, comprises,
Build module, for sample data is substituted into linear regression model (LRM) and nonlinear regression model (NLRM);
Multiple regression model module, for 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;
Prediction module, for real time input data is imported multiple regression model, obtains control sector runnability aggregative index.
Further, also comprise, standardization modular converter: for carrying out standardization conversion to the sample inputted and real time input data;
Alarm module: when control sector runnability aggregative index exceeds threshold value, control sector runnability response alarm.
Further, also comprise control sector runnability Test database, the data be coupled with described control sector runnability Test database draw connection device and data calculation element;
Described data are drawn connection device and are comprised the telegram data-interface, integrated track data-interface and the control speech data interface that are coupled with described control sector runnability Test database respectively;
Described data calculation element is for calculating the control sector running performance index collected, and described control sector running performance index comprises sector and runs road ability index, sector operation complexity profile, sector safety in operation index and sector performance driving economy index and ATC controller workload Testing index; Sector road ability Testing index comprises sector flow, sector shipping kilometre, sector hours underway and sector traffic flow density respectively; Sector complicacy Testing index comprise sector aircraft climb number of times, sector aircraft decline number of times, sector aircraft changes fast number of times, sector aircraft changes flight number number; Sector security Testing index comprises sector short term collision alert frequency and sector minimum safe altitude alert frequency; Sector economy Testing index comprises sector saturation degree, sector queue length, sector aircraft delay sortie rate, sector aircraft delay time at stop, sector aircraft mean delay time; ATC controller workload Testing index comprises the empty talk channel occupancy in land, the empty talk times in land;
Described structure module reads described sector runnability comprehensive detection index of correlation and sector runnability comprehensive detection sample data from described control sector runnability Test database; Described prediction module reads described real time input data from described control sector runnability Test database.
Beneficial effect of the present invention:
The present invention adopts quantitative analysis method, by to the uninterrupted detection of magnanimity service data with excavate computational analysis, realize judging accurately, clearly control sector runnability and grasping, evaded the defect problem of the empirical management such as manpower management fatiguability, easily internalise.Index system comprehensively, synthetically covers all kinds of influence factors of control sector runnability.What is more important, put forward system and can meet air traffic control unit carries out real-time detection and response alarm actual demand to control sector runnability, for lifting control operation and management level, optimize control zone structure there is Data support effect.To each dimension index of control sector runnability be affected, and carry out comprehensively, consider; Designed control sector runnability method for comprehensive detection and system, can be applied to air traffic control unit, have very strong application project operability.
Accompanying drawing explanation
Fig. 1 is the schematic diagram of the embodiment of the present invention one control sector runnability method for comprehensive detection;
Fig. 2 is the schematic diagram of the embodiment of the present invention one control sector runnability comprehensive detection system;
Fig. 3 is the configuration diagram of the embodiment of the present invention two control sector runnability comprehensive detection system;
Fig. 4 is the network diagram of the embodiment of the present invention two control sector runnability comprehensive detection system;
Fig. 5 is the functional schematic of the embodiment of the present invention two control sector runnability comprehensive detection system;
Fig. 6 is the embodiment of the present invention three integrated track data acquisition function schematic diagram;
Fig. 7 is the embodiment of the present invention three data under voice schematic flow sheet;
Fig. 8 is the embodiment of the present invention three telegram data acquisition function schematic diagram;
Fig. 9 is the schematic flow sheet of embodiment of the present invention four-pipe system sector runnability method for comprehensive detection;
Figure 10 is the schematic diagram before the embodiment of the present invention four multiple linear regression fitting result rounds;
Figure 11 is the schematic diagram before the embodiment of the present invention four multiple linear regression error of fitting rounds;
Figure 12 is the schematic diagram after the embodiment of the present invention four multiple linear regression fitting result rounds;
Figure 13 is the schematic diagram after the embodiment of the present invention four multiple linear regression error of fitting rounds;
Figure 14 is the structural representation of the embodiment of the present invention five control sector runnability comprehensive detection system;
Wherein: 1, build module; 2, multiple regression model module; 3, prediction module; 4, standardization modular converter; 5, alarm module: 6, runnability Test database; 7, data draw connection device; 8, data calculation element.
Embodiment
Below in conjunction with accompanying drawing and preferred embodiment, the invention will be further described.
Embodiment one
As shown in Figure 1, control sector runnability method for comprehensive detection disclosed in present embodiment, it comprises 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.
As shown in Figure 2, present embodiment also discloses a kind of control sector runnability comprehensive detection system, comprises,
Build module, for sample data is substituted into linear regression model (LRM) and nonlinear regression model (NLRM);
Multiple regression model module, for 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;
Prediction module, for real time input data is imported multiple regression model, obtains control sector runnability aggregative index.
Regretional analysis is an important branch in multivariate statistical analysis, and it is the statistical method being detected one or more response variable (i.e. dependent variable) by one group of detection variable (i.e. independent variable).Only have the situation of a dependent variable to be called simple regression, multiple dependent variable is called multiple regression.Consider and control sector runnability is subject to various factors, setting control sector runnability aggregative index is as single response variable, therefore, adopt unitary multiple regression method (abbreviation multiple regression) herein, comprehensive detection is carried out to control sector runnability.
According to the linear relationship of regression function, multiple linear regression and the basic function model of multiple non-linear regression two kinds can be divided into.The present invention can adopt two kinds of models use, and then little a kind of as final forecast model of Select Error, also can single choice one predict, with simplified operation process.
The present invention adopts quantitative analysis method, by to the uninterrupted detection of magnanimity service data with excavate computational analysis, realize judging accurately, clearly control sector runnability and grasping, evaded the defect problem of the empirical management such as manpower management fatiguability, easily internalise.Index system comprehensively, synthetically covers all kinds of influence factors of control sector runnability.What is more important, put forward system and can meet air traffic control unit carries out real-time detection and response alarm actual demand to control sector runnability, for lifting control operation and management level, optimize control zone structure there is Data support effect.To each dimension index of control sector runnability be affected, and carry out comprehensively, consider; Designed control sector runnability method for comprehensive detection and system, can be applied to air traffic control unit, have very strong application project operability.
Embodiment two
Present embodiment discloses a kind of system architecture, as the implementing platform of control sector runnability comprehensive detection system of the present invention, can be used for implementing detection method of the present invention.
The control sector runnability comprehensive detection system framework of present embodiment as shown in Figure 3.Air traffic control sector runnability comprehensive detection system mainly comprise a set of control sector runnability Test database and data draw connect, data calculate three zones module.The air traffic control data (comprising radar integrated track data, telegram data, VHF recording data etc.) that each information gathering point gathers by control sector runnability Test database is sorted out, preservation, provides data foundation for control sector runnability detects.
Described data are drawn connection device and are comprised the electricity be coupled with described control sector runnability Test database respectively
Report data-interface, integrated track data-interface and control speech data interface.
Described data calculation element is for gathering control sector running performance index, and described control sector running performance index comprises sector and runs road ability index, sector operation complexity profile, sector safety in operation index, sector performance driving economy index and ATC controller workload Testing index.Sector road ability Testing index comprises sector flow, sector shipping kilometre, sector hours underway and sector traffic flow density; Sector complicacy Testing index comprise sector aircraft climb number of times, sector aircraft decline number of times, sector aircraft changes fast number of times, sector aircraft changes flight number number; Sector security Testing index comprises sector short term collision alert frequency and sector minimum safe altitude alert frequency; Sector economy Testing index comprises sector saturation degree, sector queue length, sector aircraft delay sortie rate, sector aircraft delay time at stop, sector aircraft mean delay time; ATC controller workload Testing index comprises the empty talk channel occupancy in land, the empty talk times in land.
Fig. 4,5 disclose a kind of realize detection system of the present invention network design and corresponding functional module.System collects real time data by data acquisition server, server and comprehensive detection server real time monitoring service data is detected by control sector running performance index, determination and analysis control sector runnability situation, generates alarm for control sector runnability aggregative index outlier.The network platform of whole system will rely on existing management information net, and acquisition platform and blank pipe are produced network and carried out physical isolation, ensure the unidirectional delivery of data, stop network attack, to ensure related data security and production run system reliability.
Embodiment three
Present embodiment discloses a kind of control service data acquisition scheme, including, but not limited to the collection of control sector runnability comprehensive detection index of correlation, control sector runnability comprehensive detection and real time input data.
This research for dependent variable, is designated as Y with control sector runnability aggregative index.Control sector running performance index amounts to 17, and note independent variable X is:
X＝{X
i,i＝1,2,…,17}
Wherein, road ability Testing index in sector is { X
1, X
2, X
3, X
4, represent sector flow, sector shipping kilometre, sector hours underway and sector traffic flow density respectively; Sector complicacy Testing index is { X
5, X
6, X
7, X
8, represent respectively sector aircraft climb number of times, sector aircraft decline number of times, sector aircraft changes fast number of times, sector aircraft changes flight number number; Sector security Testing index is { X
9, X
10, represent sector short term collision alert frequency and sector minimum safe altitude alert frequency respectively; Sector economy Testing index is { X
11, X
12, X
13, X
14, X
15, represent that sector saturation degree, sector queue length, sector aircraft incur loss through delay sortie rate, sector aircraft delay time at stop, sector aircraft mean delay time respectively; ATC controller workload Testing index is { X
16, X
17, represent the empty talk channel occupancy in land, the empty talk times in land respectively.The main of these parameter indexs obtains from the following aspects collection.
Integrated track gathers
Air traffic control automation system carries out data to supervisory signals such as aviation management first and second radars and merges and data processing, and output integrated flight path information, its main processing module comprises radar front end processing module, radar data processing module and flight planning processing module.
Native system gathers integrated track data from air traffic control automation system, is transmitted by the mode of network.Data acquisition server is resolved the integrated track data gathered, and the information such as height, speed, position obtaining aircraft is used in reference to target and calculates.
Integrated track data acquisition module comprises track data format converting module, track data parsing module, track data memory module, as shown in Figure 6.
Data under voice
Controller and pilot realize the empty voice call in land by VHF communication system.This system receives and dispatches radio station and Signal transmissions by very high frequency(VHF) (VeryHighFrequency, VHF), treating apparatus forms.
Data under voice is from distributing frame and connect collection voice signal, empty for land call-information is carried out decoding and storing, for the analysis of controller's control commander call load.
As shown in Figure 7, seat speech data is by interior telephone system distributing frame by being with shielding netting wire and connecing drawing-in system data acquisition server, and voice channel is corresponding with seat (sector).
Voice signal gathers (the air-ground call of controller) seat voice from high impedance distributing frame (recording module is 200K ohm), do not affect air-ground call and voice record, adopt multiple-twin cable line to be drawn from distributing frame by voice signal and be connected to speech processor, realize the collection to multiple seats voice and analysis.
Telegram data acquisition
Determine the project planning of telegram, the data marshalling that the message that Civil Aviation Flight dynamically fixes telegram is specified by several forms by permanent order arrangement.
Telegram data acquisition module draws the telegram data of switching through reporting system and exporting, and carries out format conversion, parsing and storage to data, obtains flight plan data, as shown in Figure 8.This module is preserved being stored in database after the telegram Data Analysis received, and calculates for sector running performance index.
Control sector running performance index detects
System collects the real-time running datas such as integrated track, flight planning, voice communication from air traffic control automation system, telegraph switching relay system, interior telephone system, with International Civil Aviation Organization (referred to as " ICAO ", down together), US Federal Aviation Administration (FAA) associated documents are reference, set up control sector runnability Testing index system, and based on index system export control sector running performance index testing result.System provides good man-machine interface, checks various real-time statistics figure for user.
Road ability Indexs measure is run in sector
(1) sector flow detection
Sector flow refers to the aircraft sortie of administering in the control sector unit interval.System connects the positional information of the aerial aircraft of air traffic control automation system integrated track data acquisition by drawing, in conjunction with the sector borders information configured, calculate sector flow.
(2) sector shipping kilometre detects
Sector shipping kilometre refers to the summation of the aircraft shipping kilometre of administering in the control sector unit interval.If aircraft sortie number is Q in the control sector unit interval, the shipping kilometre of q frame aircraft is M
q, sector shipping kilometre is M
total, then
by drawing the positional information connecing the aerial aircraft of air traffic control automation system integrated track data acquisition, in conjunction with the sector borders information configured, calculate sector shipping kilometre.
(3) sector hours underway detects
Sector hours underway refers to the summation of the aircraft hours underway of administering in the control sector unit interval.If aircraft sortie number is Q in the control sector unit interval, the hours underway of q frame aircraft is T
q, sector hours underway is T
total, then
by drawing the positional information connecing the aerial aircraft of air traffic control automation system integrated track data acquisition, in conjunction with the sector borders information configured, calculate sector hours underway.
(4) sector traffic flow Density Detection
Sector traffic flow density is estimating the aircraft sortie dense degree of administering in the control sector unit interval.If sector area is S
sec, in the control sector unit interval, aircraft sortie number is Q, and in the unit interval, traffic flow density in sector is D
sec, then D
sec=Q/S
sec.The sector borders information that system reads configuration obtains sector area, obtains sector traffic flow density in conjunction with sector flow rate calculation.
Sector is run complexity profile and is detected
(1) sector aircraft climb number of times detect
The aircraft number of times that climbs in sector refers in the control sector unit interval that the aircraft of administering climbs the summation of number of times.If aircraft sortie number is Q in the control sector unit interval, the number of times that climbs of q frame aircraft is C
q, the aircraft number of times that climbs in sector is C
total, then
draw and connect real time comprehensive track data, carry out monitoring and add up to the situation of climbing of aircraft in sector, an aircraft climbs a height layer for climbing once, calculates sector aircraft and to climb number of times.
(2) sector aircraft decline number of times detects
Sector aircraft decline number of times refers to the summation of aircraft decline number of times in the control sector unit interval.If aircraft sortie number is Q in the control sector unit interval, the decline number of times of q frame aircraft is D
q, sector aircraft decline number of times is D
total, then
draw and connect real time comprehensive track data, carry out monitoring and add up to the decline situation of aircraft in sector, aircraft decline a height layer for decline once, calculate sector aircraft and to climb number of times.
(3) sector aircraft changes the detection of fast number of times
Sector aircraft changes fast number of times and refers to that in the control sector unit interval, aircraft speed changes the summation of number of times.If aircraft sortie number is Q in the control sector unit interval, the fast number of times that changes of q frame aircraft is S
q, it is S that sector aircraft changes fast number of times
total, then
draw and connect real time comprehensive track data, change situation carry out monitoring and add up the speed of aircraft in sector, aircraft speed continuously changes that to reach setup parameter be a speed change, calculates sector aircraft and changes fast number of times.
(4) sector aircraft changes the detection of flight number number
Sector aircraft changes the summation that flight number number refers to aircraft course change number of times in the control sector unit interval.If aircraft sortie number is Q in the control sector unit interval, the flight number number that changes of q frame aircraft is H
q, it is H that sector aircraft changes flight number number
total, then
draw and connect real time comprehensive track data, carry out monitoring and add up to the course change situation of aircraft in sector, aircraft course continuously changes that to reach setup parameter be a course change, calculates sector aircraft and changes flight number number.
Sector safety in operation Indexs measure
(1) sector short term collision alert frequency detecting
Sector short term collision alert frequency refers to the aircraft short term collision alert number of times of administering in the control sector unit interval, draws the STCA alarm data statistics connecing air traffic control automation system obtain by system.
(2) minimum safe altitude alert frequency in sector detects
Sector minimum safe altitude alert frequency refers to the aircraft minimum safe altitude alarm number of times of administering in the control sector unit interval, draws the MSAW alarm data statistics connecing air traffic control automation system obtain by system.
Sector performance driving economy Indexs measure
(1) sector saturation degree detects
Sector saturation degree refers to the ratio of flow and capacity in the control sector unit interval, and the aircraft maximum quantity can administered in the control sector unit interval is demarcated as control sector capacity.If aircraft sortie number is Q in the control sector unit interval, control sector capacity is C, and sector saturation degree is Satu
sec, then Satu
sec=Q/C.System reads the sector capacity parameter of configuration, obtains sector saturation degree in conjunction with sector flow rate calculation.
(2) sector queue length detects
In the aircraft of administering within the control sector unit interval, as there is the queuing situation such as wait that spiral when entering sector, be then defined as queuing aircraft, definition sector queue length is the quantity of queuing aircraft.System is drawn and is connect integrated track data, judges whether target aircraft carries out in sector borders wait of spiraling, and calculates sector queue length.
(3) sortie rate detection incured loss through delay by sector aircraft
In the aircraft of administering within the control sector unit interval, hours underway is defined as delay aircraft beyond the aircraft of normal range, and the part that hours underway exceeds normal range is defined as the delay time at stop.If aircraft sortie number is Q in the control sector unit interval, the delay sortie number of sector aircraft is d, and the delay sortie rate of sector aircraft is Drat
sec, then Drat
sec=d/Q.Draw and connect integrated track data, the actual flying time of every frame aircraft in control sector and experience flight time are contrasted, if actual flying time is greater than the experience flight time, be then considered as incuring loss through delay aircraft, and calculate sector aircraft and incur loss through delay sortie rate.
(4) the sector aircraft delay time at stop is detected
In the aircraft of administering within the control sector unit interval, hours underway is defined as delay aircraft beyond the aircraft of normal range, the part that hours underway exceeds normal range is defined as the delay time at stop, and delay time at stop summation is defined as the sector aircraft delay time at stop.If aircraft sortie number is Q in the control sector unit interval, the delay time at stop of q frame aircraft is Delay
q, the sector aircraft delay time at stop is Delay
sec, then
draw and connect integrated track data, the actual flying time of every frame aircraft in control sector and experience flight time are contrasted, if actual flying time is greater than the experience flight time, is then considered as incuring loss through delay aircraft, and calculates the sector aircraft delay time at stop.
(5) sector aircraft mean delay time detecting
In the aircraft of administering within the control sector unit interval, hours underway is defined as delay aircraft beyond the aircraft of normal range, and the part that hours underway exceeds normal range is defined as the delay time at stop.If the sector aircraft delay time at stop is Delay
sec, the delay sortie number of sector aircraft is Q, and the mean delay time of sector aircraft is Davg
sec, then Davg
sec=Delay
sec/ Q.Draw and connect integrated track data, the actual flying time of every frame aircraft in control sector and experience flight time are contrasted, if actual flying time is greater than the experience flight time, is then considered as incuring loss through delay aircraft, and calculates the sector aircraft mean delay time.
ATC controller workload detects
Controller need bear on health and spiritual pressure for completing Tasks of Regulation, these pressure can be converted into temporal consumption, alleviate the pressure afforded and the requirement completing objective task by time loss, the length of this time loss is exactly the size of ATC controller workload.In the controller's working time can surveying meter consumes, the empty talk times of the empty talk channel occupancy in land and land is the base values of reflection ATC controller workload.
(1) the empty talk channel occupancy in land detects
The empty talk channel occupancy in land refers to the empty duration of call accounting in control sector unit interval inland.If control sector is total to land sky call R time in unit interval T, the time span of the r time land sky call is T
r, the empty talk channel occupancy in land is T
rate, then
draw adapter speech data processed, the controller and the pilot that analyze control seat, corresponding sector converse start time and end time, then the duration that every section is conversed is added up, thus obtain the empty duration of call in land, sector, and then calculate the empty talk channel occupancy in land.
(2) the empty talk times in land detects
The empty talk times in land refers to the number of times of control sector unit interval inland sky call.System is analyzed control speech data, and a land sky call is counted in each call, and carrying out adding up to talk times in the unit interval draws the empty talk times in land.
Embodiment four
Present embodiment discloses a kind of control sector runnability method for comprehensive detection, the method can be selected the hardware platform of embodiment two to realize, choosing of the control sector runnability comprehensive detection index of correlation that it relates to, the collection of control sector runnability comprehensive detection sample data and real time input data can reference example three.
Present embodiment adopts multiple linear regression and multiple nonlinear regression model (NLRM) simultaneously, and the model selecting metrical error minimum from both is as final forecast model.
(1) multiple linear regression utilizes linear function to carry out the multiple independent variable X of matching
i(i=1,2 ..., n) and the relation of single dependent variable Y, thus determine the parameter b of Multivariate regression model
i(i=0,1,2 ..., n), be returned in null hypothesis equation, detected the trend of dependent variable by regression equation.The general type of Multivariate regression model is:
Y=b
0+ b
1x
1+ b
1x
2+ ... + b
ix
i+ ... + b
nx
n+ μ (formula 4.1)
Wherein, μ is except n independent variable is on the stochastic error except the impact of dependent variable Y, Normal Distribution.
As if statistics sample has m group statistical data, then the matrix form of Multivariate regression model can be expressed as:
Y=XB+U (formula 4.2)
Wherein,
(formula 4.3)
(2) multiple non-linear regression, be then present nonlinear relationship between supposition independent variable (Testing index) and dependent variable (sector performance), multiple nonlinear model generally can be expressed as:
Y=f [(b
1, b
2..., b
k); X
1, X
2..., X
n] (formula 4.4)
Wherein nonlinear solshing according to sample data feature, can adopt the forms such as quadratic function, power function, exponential function, hyperbolic function.Present embodiment is illustrated with quadratic function:
(formula 4.5)
The parameter b of multiple regression model
iafter estimating, after namely obtaining sample regression function, also need to carry out statistical test to this sample regression function further, comprise degree of fitting inspection (coefficient of determination R
2), significance test (p value), and the Estimating Confidence Interval etc. of parameter.Then calculate metrical error, the little model of final Select Error is as final forecast model.
Control sector runnability aggregative index algorithm based on multiple regression mainly comprises four parts, i.e. the ratio choosing of the structure of regression model, regression model, the comprehensive detection of control sector runnability and control sector runnability response alarm.See Fig. 9, specific algorithm step is:
Step 1: choose variable
Reference example three, this research for dependent variable, is designated as Y with control sector runnability aggregative index.Control sector running performance index amounts to 17, and note independent variable X is:
X={X
i, i=1,2 ..., 17} (formula 4.6)
Wherein, road ability Testing index in sector is { X
1, X
2, X
3, X
4, represent sector flow, sector shipping kilometre, sector hours underway and sector traffic flow density respectively; Sector complicacy Testing index is { X
5, X
6, X
7, X
8, represent respectively sector aircraft climb number of times, sector aircraft decline number of times, sector aircraft changes fast number of times, sector aircraft changes flight number number; Sector security Testing index is { X
9, X
10, represent sector short term collision alert frequency and sector minimum safe altitude alert frequency respectively; Sector economy Testing index is { X
11, X
12, X
13, X
14, X
15, represent that sector saturation degree, sector queue length, sector aircraft incur loss through delay sortie rate, sector aircraft delay time at stop, sector aircraft mean delay time respectively; ATC controller workload Testing index is { X
16, X
17, represent the empty talk channel occupancy in land, the empty talk times in land respectively.
According to M group with hour for duration sample input data, obtain the input value of above-mentioned 17 indexs.Meanwhile, according to the input value X of These parameters, by senior control expert, sample control sector runnability is classified, as control sector runnability aggregative index Y (Y=1,2,3,4,5 represent that control sector runnability is best respectively, better, generally, poor, the poorest).The sample index's data instance obtained is as follows:
Table 1 control sector runnability sample index data instance
Step 2: data processing
Consider to there is dimension difference and magnitude differences between different index, for convenience of the regretional analysis of model, need to carry out standardization conversion to achievement data.
Make x
ij, x '
ijrepresent the raw data of i-th sample and the data after standardization conversion respectively,
s
jrepresent respectively jth (j=1,2 ..., 17) and the average of individual achievement data and variance, then:
(formula 4.7)
Data x ' after standardization is changed
ij, as the input data of regretional analysis.
Step 3: build regression model
Reference example two, three, build Multivariate regression model and multiple nonlinear regression model (NLRM) respectively, wherein, the form of quadratic function selected by nonlinear regression model (NLRM).By carrying out matching to sample data, obtain the estimates of parameters of two class functions
with sample output valve
wherein
the b of representative formula 4.4
0~ b
nor the b of formula 4.5
0~ b
2nestimated value.
Step 4: inspection regression model
The parameter of multiple regression model
after estimating, after namely obtaining sample regression function, also need to carry out statistical test to this sample regression function further, comprise degree of fitting inspection (coefficient of determination R
2the coefficient of determination), significance test (p value), and the Estimating Confidence Interval etc. of parameter.According to the coefficient of determination R that model returns
2value, F inspection, t inspection, verify respectively and compare degree of fitting, the conspicuousness of 2 kinds of regression models, on the obvious basis of, conspicuousness higher in model-fitting degree, calculate the metrical error of two kinds of regression models, and choose the minimum a kind of model of error, as last detection model, as the detection model of control sector runnability.
Step 5: regression model result exports
According to the average of 17 indexs of the sample data in the m group time interval
variance s
j, to real-time import control sector running performance index
carry out standardization conversion:
by the data t ' after conversion
jimport in the runnability comprehensive detection multiple regression model of sector, obtain the classification grade of current control sector runnability.
Step 6: control sector runnability response alarm
According to the comprehensive detection result of control sector runnability, with reference to the control sector runnability comprehensive detection response alarm standard of setting, to reaching alarm standard, produce alarm by system.
According to above-mentioned algorithm flow, gather ACC01 sector, Chengdu index of correlation data and amount to 648 groups, adopt linear function and nonlinear function (quadratic function) to carry out matching to sample data respectively, the Fitting Calculation obtains the R of two class functions
2, p value, and the matching performance data such as average error, maximum error, least error.Because sector runnability aggregative index is integer, therefore, respectively fitting effect is compared according to whether rounding (rounding up) process to the sector Synthesis performance index result of Multivariate regression model and nonlinear regression model (NLRM).Conclusion is as follows:
Table 2 multiple regression fitting effect contrasts
According to upper table, degree of fitting, the conspicuousness of linear function, nonlinear function are all better; But the error of fitting of linear function is less than nonlinear function.Therefore, it is minimum as principle that this sentences error, chooses the linear function after rounding, as the detection model of control sector runnability.Effect before multiple linear regression fitting result rounds as shown in Figure 10, effect before multiple linear regression error of fitting rounds as shown in figure 11, as shown in figure 12, the effect after multiple linear regression error of fitting rounds as shown in figure 13 for effect after multiple linear regression fitting result rounds.
To sum up, control sector runnability aggregative index algorithm is chosen multiple linear regression based on following formula and is rounded model:
(formula 4.8)
This detection method and corresponding system, after putting into operation, need to manage accordingly.The system management recommended has following several:
1. management uses user right, is every user's distributing user name and authority, ensures the security of data, prevents data from leaking.
2. every user corresponding 0 is to multiple role, the authority that each role can be accessed by managerial personnel's flexible allocation and operate.
3. the parameter that system cloud gray model is necessary is set, comprises map parameter, telegram process and radar data process parameter, the long-term parameter that the timetable is shown, system display parameter are arranged, other needs are arranged.
4. provide log management function, be responsible for the operation of recording system, retain the operation information of significant data.Comprise: daily record recording module, log query module, Log backup and removing module.
5. parameter configuration function is provided, for system maintenance personnel provide the instrument of parameter configuration.
6. data exporting function is provided.
The proposed arrangement realizing detection method and system is as follows:
| Sequence number | Equipment | Quantity |
| 1 | Sector runnability detection computations server | 1 |
| 2 | Data acquisition process server | 1 |
| 3 | Monitoring data gateway | 2 |
| 4 | Sound card | 1 |
| 5 | Active divider | 1 |
| 6 | Switch | 1 |
| 7 | Evident 42U rack | 1 |
| 8 | KVM switcher | 1 |
Embodiment five
The control sector runnability method for comprehensive detection of present embodiment, comprises step:
Multiple regression model is built according to control sector runnability comprehensive detection index of correlation and control sector runnability comprehensive detection sample data.According to M group with hour for duration sample input data, obtain the input value of described control sector runnability comprehensive detection index of correlation; Described real time input data is obtained from control sector runnability Test database;
Real time input data is imported multiple regression model.
Consider to there is dimension difference and magnitude differences between different index, before structure multiple regression model, first standardization conversion is carried out to the control sector runnability comprehensive detection index of correlation inputted and control sector runnability comprehensive detection sample data; Accordingly, to the advanced column criterionization conversion of the real time input data importing sample regression function, the regretional analysis of model can be facilitated like this;
Multiple regression model comprises Multivariate regression model and multiple nonlinear regression model (NLRM), and wherein, quadratic function selected by nonlinear regression model (NLRM).Pattern function is with reference to above-described embodiment.
Respectively by Multivariate regression model and multiple nonlinear regression model (NLRM), matching is carried out to control sector runnability comprehensive detection sample data, obtain two groups of sample regression functions, statistical test is carried out to two groups of sample regression functions, described statistical test step comprises degree of fitting and significance test, when degree of fitting and conspicuousness exceed preset value, then calculate described metrical error;
Described real time input data is imported the minimum sample regression function of metrical error, obtain described control sector runnability aggregative index;
When control sector runnability aggregative index exceeds threshold value, sector runnability response alarm.
Control sector running performance index comprises sector and runs road ability index, sector operation complexity profile, sector safety in operation index, sector performance driving economy index and ATC controller workload Testing index..
Sector road ability Testing index comprises sector flow, sector shipping kilometre, sector hours underway and sector traffic flow density respectively; Sector complicacy Testing index comprise sector aircraft climb number of times, sector aircraft decline number of times, sector aircraft changes fast number of times, sector aircraft changes flight number number; Sector security Testing index comprises sector short term collision alert frequency and sector minimum safe altitude alert frequency; Sector economy Testing index comprises sector saturation degree, sector queue length, sector aircraft delay sortie rate, sector aircraft delay time at stop, sector aircraft mean delay time; ATC controller workload Testing index comprises the empty talk channel occupancy in land, the empty talk times in land.
As shown in figure 14, present embodiment also discloses a kind of air traffic control sector runnability comprehensive detection system.It comprises control sector runnability Test database, and the data be coupled with described control sector runnability Test database draw connection device and data calculation element.
Data are drawn connection device and are comprised the telegram data-interface, integrated track data-interface and the control speech data interface that are coupled with control sector runnability Test database respectively; Data calculation element is for gathering control sector running performance index, and described control sector running performance index comprises sector and runs road ability index, sector operation complexity profile, sector safety in operation index and sector performance driving economy index and ATC controller workload Testing index.
Sector road ability Testing index comprises sector flow, sector shipping kilometre, sector hours underway and sector traffic flow density respectively; Sector complicacy Testing index comprise sector aircraft climb number of times, sector aircraft decline number of times, sector aircraft changes fast number of times, sector aircraft changes flight number number; Sector security Testing index comprises sector short term collision alert frequency and sector minimum safe altitude alert frequency; Sector economy Testing index comprises sector saturation degree, sector queue length, sector aircraft delay sortie rate, sector aircraft delay time at stop, sector aircraft mean delay time; ATC controller workload Testing index comprises the empty talk channel occupancy in land, the empty talk times in land.
Build module, for sample data is substituted into linear regression model (LRM) and nonlinear regression model (NLRM);
Multiple regression model module, for 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;
Prediction module, for real time input data is imported multiple regression model, obtains control sector runnability aggregative index.
Standardization modular converter: for carrying out standardization conversion to the sample inputted and real time input data;
Alarm module: when control sector runnability aggregative index exceeds threshold value, control sector runnability response alarm.
Above content is in conjunction with concrete preferred implementation further description made for the present invention, can not assert that specific embodiment of the invention is confined to these explanations.For general technical staff of the technical field of the invention, without departing from the inventive concept of the premise, some simple deduction or replace can also be made, all should be considered as belonging to protection scope of the present invention.
Claims (11)
1. an air traffic control sector runnability method for comprehensive detection, is characterized in that, 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.
2. air traffic control sector as claimed in claim 1 runnability method for comprehensive detection, it is characterized in that, the described control sector running performance index in step 1 comprises sector and runs road ability index, sector operation complexity profile, sector safety in operation index, sector performance driving economy index and ATC controller workload Testing index.
3. air traffic control sector as claimed in claim 2 runnability method for comprehensive detection, it is characterized in that, described sector road ability Testing index comprises sector flow, sector shipping kilometre, sector hours underway and sector traffic flow density respectively;
Described sector complicacy Testing index comprise sector aircraft climb number of times, sector aircraft decline number of times, sector aircraft changes fast number of times, sector aircraft changes flight number number;
Described sector security Testing index comprises sector short term collision alert frequency and sector minimum safe altitude alert frequency;
Described sector economy Testing index comprises sector saturation degree, sector queue length, sector aircraft delay sortie rate, sector aircraft delay time at stop, sector aircraft mean delay time;
Described ATC controller workload Testing index comprises the empty talk channel occupancy in land, the empty talk times in land;
Control sector running performance index is 17.
4. the air traffic control sector runnability method for comprehensive detection as described in claim 1 or 3, is characterized in that, carry out standardization conversion in described step 2 to sample data; Standardization transfer process is as follows:
Make x
ij, x '
ijrepresent the raw data of i-th sample and the data after standardization conversion respectively,
s
jrepresent average and the variance of a jth achievement data respectively, then:
5. air traffic control sector as claimed in claim 4 runnability method for comprehensive detection, it is characterized in that, step 2 concrete steps are as follows:
According to above-mentioned standardization sample data x '
uj=(i=1,2 ..., m, j=1,2 ... n) set up Multivariate regression model and multiple nonlinear regression model (NLRM) respectively, and solve coefficient b
u,
Wherein Multivariate regression model is:
Y＝XB+U
Wherein,
Multiple nonlinear regression model (NLRM) is:
Y＝f[(b
1,
2,…,b
k)；X
1,X
2,…,X
n]
Wherein dependent variable Y is control sector runnability aggregative index, independent variable X is n item control sector runnability comprehensive detection index, m represents the control sector running performance index sample under the m group time interval, U is except m independent variable is on the stochastic error except the impact of dependent variable Y, Normal Distribution, f represents nonlinear solshing.
6. the air traffic control sector runnability method for comprehensive detection as described in claim 1 or 5, it is characterized in that, step 3 concrete steps are as follows:
According to the coefficient of determination R that each model returns
2value, F inspection, t inspection, verify respectively and compare degree of fitting, the conspicuousness of two kinds of regression models, on the obvious basis of, conspicuousness high in model-fitting degree, calculate the metrical error of two kinds of regression models, and choose the minimum a kind of model of error, as the multiple regression model of sector runnability comprehensive detection.
7. air traffic control sector as claimed in claim 1 runnability method for comprehensive detection, is characterized in that, the real time input data in step 4 will carry out standardization conversion before input multiple regression model; Standardization transfer process is as follows:
According to the average of the n item index of the sample data in the m group time interval
variance s
j, to real-time import control sector running performance index
carry out standardization conversion:
by the data after conversion
import in the runnability comprehensive detection multiple regression model of sector.
8. air traffic control sector as claimed in claim 1 runnability method for comprehensive detection, it is characterized in that, the method also comprises step 5, when control sector runnability aggregative index exceeds threshold value, sector runnability response alarm.
9. an air traffic control sector runnability comprehensive detection system, is characterized in that, comprise,
Build module, for sample data is substituted into linear regression model (LRM) and nonlinear regression model (NLRM);
Multiple regression model module, for 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;
Prediction module, for real time input data is imported multiple regression model, obtains control sector runnability aggregative index.
10. air traffic control sector as claimed in claim 9 runnability comprehensive detection system, is characterized in that, also comprise,
Standardization modular converter: for carrying out standardization conversion to the sample inputted and real time input data;
Alarm module: when control sector runnability aggregative index exceeds threshold value, control sector runnability response alarm.
11. air traffic control sector as claimed in claim 10 runnability comprehensive detection systems, it is characterized in that, also comprise control sector runnability Test database, the data be coupled with described control sector runnability Test database draw connection device and data calculation element;
Described data are drawn connection device and are comprised the telegram data-interface, integrated track data-interface and the control speech data interface that are coupled with described control sector runnability Test database respectively;
Described data calculation element is for gathering control sector running performance index, and described control sector running performance index comprises sector and runs road ability index, sector operation complexity profile, sector safety in operation index and sector performance driving economy index and ATC controller workload Testing index; Sector road ability Testing index comprises sector flow, sector shipping kilometre, sector hours underway and sector traffic flow density respectively; Sector complicacy Testing index comprise sector aircraft climb number of times, sector aircraft decline number of times, sector aircraft changes fast number of times, sector aircraft changes flight number number; Sector security Testing index comprises sector short term collision alert frequency and sector minimum safe altitude alert frequency; Sector economy Testing index comprises sector saturation degree, sector queue length, sector aircraft delay sortie rate, sector aircraft delay time at stop, sector aircraft mean delay time; ATC controller workload Testing index comprises the empty talk channel occupancy in land, the empty talk times in land;
Described structure module reads described sector runnability comprehensive detection index of correlation and sector runnability comprehensive detection sample data from described control sector runnability Test database; Described prediction module reads described real time input data from described control sector runnability Test database.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510641511.6A CN105225193A (en) | 2015-09-30 | 2015-09-30 | A kind of method and system of the sector runnability aggregative index based on multiple regression model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510641511.6A CN105225193A (en) | 2015-09-30 | 2015-09-30 | A kind of method and system of the sector runnability aggregative index based on multiple regression model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105225193A true CN105225193A (en) | 2016-01-06 |
Family
ID=54994145
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510641511.6A Pending CN105225193A (en) | 2015-09-30 | 2015-09-30 | A kind of method and system of the sector runnability aggregative index based on multiple regression model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105225193A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108171379A (en) * | 2017-12-28 | 2018-06-15 | 无锡英臻科技有限公司 | A kind of electro-load forecast method |
| CN108519988A (en) * | 2018-02-27 | 2018-09-11 | 北京航空航天大学 | Aviation based on co-integration relationship is delayed causality network construction method |
| CN108986554A (en) * | 2018-07-23 | 2018-12-11 | 南京航空航天大学 | A kind of space domain sector degree of crowding dynamic identifying method based on fuzzy comprehensive evoluation |
| CN111598327A (en) * | 2020-05-12 | 2020-08-28 | 华东师范大学 | Aviation risk evaluation method and device and computer equipment |
| WO2021218251A1 (en) * | 2020-04-29 | 2021-11-04 | 中国电子科技集团公司第二十八研究所 | Method and device for evaluating capacity on basis of historical capacity similar feature |
-
2015
- 2015-09-30 CN CN201510641511.6A patent/CN105225193A/en active Pending
Non-Patent Citations (1)
| Title |
|---|
| 袁霄: "基于扇区交通特性的航空管制员工作负荷的计算", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108171379A (en) * | 2017-12-28 | 2018-06-15 | 无锡英臻科技有限公司 | A kind of electro-load forecast method |
| CN108171379B (en) * | 2017-12-28 | 2021-12-17 | 无锡英臻科技有限公司 | Power load prediction method |
| CN108519988A (en) * | 2018-02-27 | 2018-09-11 | 北京航空航天大学 | Aviation based on co-integration relationship is delayed causality network construction method |
| CN108986554A (en) * | 2018-07-23 | 2018-12-11 | 南京航空航天大学 | A kind of space domain sector degree of crowding dynamic identifying method based on fuzzy comprehensive evoluation |
| WO2021218251A1 (en) * | 2020-04-29 | 2021-11-04 | 中国电子科技集团公司第二十八研究所 | Method and device for evaluating capacity on basis of historical capacity similar feature |
| CN111598327A (en) * | 2020-05-12 | 2020-08-28 | 华东师范大学 | Aviation risk evaluation method and device and computer equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105205565A (en) | Controller workload prediction method and system based on multiple regression model | |
| CN105225193A (en) | A kind of method and system of the sector runnability aggregative index based on multiple regression model | |
| CN105225539A (en) | Based on the method and system of the sector runnability aggregative index of principal component analysis (PCA) | |
| CN105261240A (en) | Integrated sector operation performance detection method based on cluster analysis and system | |
| CN105160201A (en) | Genetic algorithm back propagation (GABP) neural network based controller workload prediction method and system | |
| CN103745229A (en) | Method and system of fault diagnosis of rail transit based on SVM (Support Vector Machine) | |
| CN104777813A (en) | Comprehensive meteorological observation operation monitoring system and monitoring method thereof | |
| CN103489053A (en) | Intelligent water resource control platform based on cloud computing and expert system | |
| CN113592128A (en) | Big data electric wire netting operation monitoring system | |
| CN106156913A (en) | Health control method for aircraft department enclosure | |
| CN106780115A (en) | Abnormal electricity consumption monitoring and alignment system and method | |
| CN109522380A (en) | A kind of power grid disaster comprehensive monitoring warning data system and method towards mobile application | |
| TWI604324B (en) | Performance evaluation system and method | |
| CN110069551A (en) | Medical Devices O&M information excavating analysis system and its application method based on Spark | |
| CN107527121A (en) | A kind of method of the information system running status diagnosis prediction of power network | |
| CN106194264A (en) | A kind of coal and gas prominent is monitored and early warning system in real time | |
| CN105206115B (en) | A kind of air traffic control sector traffic capacity detection method based on principal component analysis | |
| CN103942251A (en) | Method and system for inputting high altitude meteorological data into database based on multiple quality control methods | |
| CN105913654B (en) | A kind of Intelligent traffic management systems | |
| CN105118333B (en) | A kind of air traffic control analog simulation method for detecting abnormality and device based on multiple regression model | |
| CN103617337B (en) | A kind of aircraft maintenance interval aided analysis method | |
| CN204856924U (en) | Control load management system | |
| CN109299863A (en) | BTM appraisal of equipment system | |
| RU95153U1 (en) | SYSTEM OF TECHNICAL DIAGNOSTICS AND MONITORING | |
| Zhang et al. | A genetic algorithm-based BP neural network method for operational performance assessment of ATC sector |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160106 |
|
| RJ01 | Rejection of invention patent application after publication |