CN105427674A - System and method for real-time unmanned plane flight state evaluation early warning - Google Patents

System and method for real-time unmanned plane flight state evaluation early warning Download PDF

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CN105427674A
CN105427674A CN201510734491.7A CN201510734491A CN105427674A CN 105427674 A CN105427674 A CN 105427674A CN 201510734491 A CN201510734491 A CN 201510734491A CN 105427674 A CN105427674 A CN 105427674A
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unmanned plane
early warning
flight
real
data
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CN201510734491.7A
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CN105427674B (en
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李宗谕
刘俍
王万国
雍军
慕世友
傅孟潮
魏传虎
李建祥
赵金龙
田兵
李勇
吴观斌
许乃媛
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国网山东省电力公司电力科学研究院
山东鲁能智能技术有限公司
国家电网公司
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    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G5/00Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0047Navigation or guidance aids for a single aircraft
    • G08G5/0069Navigation or guidance aids for a single aircraft specially adapted for an unmanned aircraft

Abstract

The invention discloses a system and a method for real-time unmanned plane flight state evaluation early warning. The system comprises an evaluation early warning platform, an unmanned plane and a ground monitoring station, wherein the ground monitoring station is used for monitoring the flight state data of the unmanned plane and transmitting the data to the evaluation early warning platform, the evaluation early warning platform is used for reading the flight state information and flight control data of the unmanned plane, through the flight control data of the unmanned plane accumulated at each inspection, an unmanned plane flight state evaluation model is constructed, the flight state of the unmanned plane is determined, a flight instruction transmitted by a host computer is further received, and flight of the unmanned plane is adjusted. According to the method, the flight control data of the unmanned plane can be inspected through a power transmission line, through the combined time sequence analysis, clustering analysis and supporting vector machine analysis method, the flight state evaluation early warning model is constructed, and real-time flight state evaluation early warning of the unmanned plane in the power transmission line inspection process can be realized.

Description

A kind of unmanned plane during flying state real-time assessment early warning system and method

Technical field

The present invention relates to a kind of unmanned plane during flying state real-time assessment early warning system and method.

Background technology

Unmanned plane line data-logging plays more and more important effect in polling transmission line, unmanned plane inspection system partly can replace line walking workman and have man-machine inspection system, alleviate the working load of electrical power services personnel, reduce the probability of contingent personnel hazard, reduce the maintenance cost of power equipment, improve the safety and reliability of electrical network.

Patrol and examine in work for unmanned plane and produce a large amount of flight control data, there are pending analysis and excavation, but these data are substantially all scattered at present, be normally present in the computing machine of staff in the mode of file, lack effective Collection and analysis means.This just needs to patrol and examine flight control data by unmanned plane and collects and administrative skill, sets up unmanned plane during flying state estimation early warning platform, the Study and appliance that Develop Data is analyzed.

Application number is " the unmanned plane obstruction warning system " of " 201310480659.7 ", mainly for aloft obstruction warning, does not have the assessment of the state of flight of unmanned plane body own.Application number is " a kind of unmanned plane management system " of " 201510222453.3 ", achieves the parameter such as current location, state of flight of reception unmanned plane and shows in real time on map, mainly solves unmanned plane during flying monitoring problem." unmanned plane and state of flight auxiliary reminding method thereof " that application number is " 201410160639.6 " judges unmanned plane apparatus assembly exception by controller and points out, but not based on the forecast assessment of flying history data.

In sum, existing to polling transmission line unmanned plane during flying state estimation method for early warning, its effect is not very desirable, still has a lot of problem to need to solve.

How based on the analysis to a large amount of UAV Flight Control data accumulated in the work of patrolling and examining, realize the Efficient Evaluation to patrol unmanned machine equipment state and state of flight problem real-time early warning, ensure the safe and stable operation of unmanned plane power transmission line polling system, become problem demanding prompt solution.

Summary of the invention

The present invention is in order to solve the problem, propose a kind of unmanned plane during flying state real-time assessment early warning system and method, the present invention controls result data to the flight that unmanned plane is patrolled and examined and processes with the flight control data of patrolling and examining in process, make by analysis and evaluation unmanned plane equipment state, carry out real-time early warning prompting, ensure the safety of unmanned plane polling transmission line.

To achieve these goals, the present invention adopts following technical scheme:

A kind of unmanned plane during flying state real-time assessment early warning system, comprises assessment early warning platform, unmanned plane and ground monitoring station, wherein:

Described ground monitoring station, for obtaining the Flight Condition Data of unmanned plane, is passed to assessment early warning platform;

Described assessment early warning platform, for reading unmanned plane during flying status information and flight control data, the UAV Flight Control data accumulated by each patrol task, build unmanned plane during flying state estimation model, unmanned plane during flying state is judged, receive the flight directive of host computer transmission, the flight of adjustment unmanned plane simultaneously.

Described assessment early warning platform comprises state of flight assessment warning module, flight control data administration module, human-computer interaction module, communication module, wherein:

Described communication module, for communicating with ground monitoring station, receiving unmanned plane during flying status data, being transferred to flight control data administration module;

Described flight control data administration module, control historical data for patrolling and examining the corresponding flight of aerial mission management according to power transmission line unmanned machine, and accumulate UAV Flight Control data by communication module Real-time Obtaining;

Described state of flight assessment warning module, reads the UAV Flight Control data of patrolling and examining aerial mission accumulation in flight control data administration module, sets up unmanned plane during flying status safety Early-warning Model, judge unmanned plane during flying state, early warning;

Described human-computer interaction module, for obtaining and resolving external command, sends out the external command after parsing and is sent to unmanned plane execution corresponding flight adjustment instruction by ground monitoring station.

Described unmanned plane includes but not limited to the unmanned plane body of fixed-wing, rotor and many rotors form.

Described ground monitoring station, passes link by number and is connected with unmanned plane, adopt with unmanned plane the communication mode be connected one to one; Pass link by figure to be connected with unmanned plane, that reads unmanned plane patrols and examines video data.

Described state of flight assessment warning module, according to the UAV Flight Control data that each patrol task accumulates, operate time sequential analysis, clustering method and support vector machine set up unmanned plane during flying status safety Early-warning Model, simultaneously when unmanned plane performs polling transmission line aerial mission, by ground monitoring station Real-time Obtaining UAV Flight Control data, utilize unmanned plane during flying status safety Early-warning Model real-time judge unmanned plane during flying state, and early warning in time.

Described communication module is one-to-many connected mode with ground monitoring station, realizes the mutual of the data between ground monitoring station, reads the GPS location of unmanned plane, state of flight, flight control data data and patrol and examine process data from ground monitoring station.

Described communication module adopts and includes but not limited to current GSM, GPRS, CDMA, WCDMA, TD-SCDMA, LTE and WIFI communication standard wireless mobile communications.

A kind of patrol unmanned machine state of flight real-time assessment method for early warning, comprises the following steps:

(1) ground monitoring station obtains Flight Condition Data and the flight control data of unmanned plane, is transferred to assessment early warning platform;

(2) pre-service is carried out to the flight control information of unmanned plane, according to effect quantity and the influence amount structure unmanned plane safe early warning object set of state of flight, extract critical data as the input of mapping relations, determine the initial primary topology of mapping relations;

(3) in conjunction with the dynamic sample data that unmanned plane runs, the key building unmanned plane runs time series variation forecast model, carries out monitoring and forecast to the critical data that unmanned plane runs;

(4) using training error as the constraint condition of optimization problem, minimize as optimization aim using fiducial range value, build unmanned plane safe early warning model, the relation of Simulation and Prediction unmanned plane duty and influence factor;

(5) Forewarn evaluation result is exported, according to the flight directive of early warning judged result adjustment unmanned plane.

In described step (2), utilize cluster data analytical approach, pre-service is carried out to the flight control information of unmanned plane, extract the input of key component as mapping relations, thus determine the initial primary topology of mapping relations, described effect quantity and the influence amount information aggregate of unmanned plane by the attribute and property value formulating object, build unmanned plane safe early warning information table, build data matrix based on unmanned plane safe early warning information table, express the similarity degree of each variable in set.

In described step (2), unmanned plane safe early warning information table S is expressed as:

S=(U,R,V,F)

R=C∪D

In formula, U is nonempty finite set, is the set of object, U={x 1, x 2..., x n, n>=0; R=C ∪ D is nonempty finite set, is called community set, and subset C is called conditional attribute collection, namely affects quantity set, and subset D is called decision kind set, i.e. effect quantity set, C={c 1, c 2..., c m, m>=0; D={d 1, d 2..., d k, k>=0; V=∪ a ∈ Rv athe set of property value, V arepresent the range of attributes of attribute a, wherein a meets a ∈ R, and F is an information function, and it specifies the property value of each object x in U, i.e. V a=F (x, a), x ∈ U, a ∈ R.

In described step (2), build the data matrix based on safety information table, object-attribute matrix is as follows:

Wherein a ij(i=1 ..., n; J=1 ..., m) be the property value of a jth attribute of i-th sample.I-th sample A idescribed by the i-th row of matrix A, any two sample A kwith A lbetween similarity, distinguished by the line K in matrix A and the capable similarity degree of L; Any Two Variables a kwith a lbetween similarity, the similarity degree arranged with L by K row is distinguished.

In described step (2), effect quantity comprises motor speed, attitude, speed and directional angular velocity.

In described step (2), influence amount comprises temperature, state-of-charge, humidity, Distance geometry flight time.

In described step (3), concrete steps are: build time series models, adopt Time series analysis method and variable analysis method, and in conjunction with the dynamic sample data that unmanned plane runs, the key setting up unmanned plane runs the Time series forecasting model of variable.

Described critical data comprises motor speed, attitude, speed and acceleration.

In described step (4), application least square method supporting vector machine algorithm, using training error as the constraint condition of optimization problem, minimize as optimization aim using fiducial range value, UAS moving law is summarized from the safe operation primary monitoring data learning of unmanned plane, build unmanned plane safe early warning model, the relation of Simulation and Prediction unmanned plane duty and major influence factors.

In described step (4), concrete grammar is: adopt a Nonlinear Mapping to be the high-dimensional feature space of k from former spatial mappings to dimension by sample, then in high-dimensional feature space, linear regression is carried out, according to structural risk minimization principle, consider function complexity and error of fitting, regression function is looked back problem equivalent in minimizing cost functional, utilize Lagrange and KKT optimal condition, optimization problem is converted into the system of linear equations solved with least square method, obtains the regression model of unmanned plane safe early warning.

In described step (5), concrete grammar is: according to the influence amount of unmanned plane and the threshold values sample data of effect quantity, calculated by time sequence analysis algorithm and try to achieve the influence amount of unmanned plane and the threshold values of effect quantity, the threshold values of trying to achieve respective effects amount and effect quantity according to safe early warning model is done difference and ask absolute value, according to the relation of itself and standard deviation, unmanned plane running status is judged, if need to report to the police, then report to the police, and adjust unmanned plane during flying state, state of flight problem is uploaded to unmanned plane during flying state estimation Warning Service device.

In described step (5), method for early warning is specially:

When time, unmanned plane during flying is normal;

When time, think that unmanned plane during flying is normal, observed reading trendless changes;

When time, think that unmanned plane during flying is substantially abnormal, observed reading has tendency to change;

When 3 S < | y t - y ^ t | Time, report to the police;

When observed reading trendless changes, need to determine whether the effect quantity of this unmanned plane and influence amount exceed its threshold values, if exceeded, output abnormality and warning.

Wherein s is the standard deviation of model, for respective effects amount tried to achieve by safe early warning model, y tfor the threshold values of effect quantity.

Beneficial effect of the present invention is:

(1) method of the application of the invention, can in real time according to the flight control data of polling transmission line unmanned plane, by the analytical approach that time series analysis, clustering method and support vector machine combine, construct unmanned plane during flying state estimation Early-warning Model, realize the state of flight real-time assessment early warning of unmanned plane in polling transmission line process;

(2) simultaneously to the assessment of the state of flight of unmanned plane body own and the forecast assessment of flying history data, solve the real-time monitoring problem of unmanned plane during flying, ensure unmanned plane during flying safety, have broad application prospects.

Accompanying drawing explanation

Fig. 1 is system architecture schematic diagram of the present invention;

Fig. 2 is schematic flow sheet of the present invention;

Fig. 3 is state of flight automatic evaluation schematic flow sheet of the present invention.

Embodiment:

Below in conjunction with accompanying drawing and embodiment, the invention will be further described.

As shown in Figure 1, the present invention is made up of unmanned plane during flying state estimation early warning platform, ground monitoring station and unmanned plane three part, wherein, unmanned plane during flying state estimation early warning platform is connected with one-to-many between ground monitoring station, for building unmanned plane during flying state estimation model, and read state of flight information and the flight control data data of unmanned plane, provide assessment early warning in real time; Ground monitoring station is connected one to one with between unmanned plane, for reading unmanned plane during flying status data, and passes to unmanned plane during flying state estimation early warning platform.

Unmanned plane during flying state estimation early warning platform, the UAV Flight Control data accumulated by each patrol task, the analytical approach that operate time sequential analysis, clustering method and support vector machine combine, set up unmanned plane during flying status safety Early-warning Model, simultaneously when unmanned plane performs polling transmission line aerial mission, by ground monitoring station Real-time Obtaining UAV Flight Control data, utilize unmanned plane during flying status safety Early-warning Model real-time judge unmanned plane during flying state, and early warning in time.

Unmanned plane can adopt the unmanned plane body of the forms such as fixed-wing, rotor, many rotors.

Ground monitoring station, passes link by number and is connected with unmanned plane, for reading the state of flight information of unmanned plane.Ground monitoring station also passes link by figure and is connected with unmanned plane, patrols and examines video data for what read unmanned plane.Ground monitoring station is connected with flight control data processing server by the mode of radio communication or wire communication, by unmanned plane during flying status information and flight control data data, is sent in unmanned plane during flying state estimation Warning Service device.

Human-computer interaction module adopts and comprises current unmanned plane controlling equipment or be selected from telepilot, keyboard, mouse, audio frequency apparatus, display, hyperchannel ring curtain stereo projection system, for obtaining and resolving external command, the external command after parsing is sent out and is sent to unmanned plane execution corresponding flight adjustment instruction by ground monitoring station.

Flight control data administration module, patrol and examine aerial mission according to power transmission line unmanned machine and manage corresponding flight respectively and control historical data, and accumulate UAV Flight Control data by communication module Real-time Obtaining.

State of flight assessment warning module, use the UAV Flight Control data of patrolling and examining aerial mission accumulation in flight control data administration module, the analytical approach that operate time sequential analysis, clustering method and support vector machine combine, sets up unmanned plane during flying status safety Early-warning Model.Simultaneously when unmanned plane performs polling transmission line aerial mission, to Real-time Obtaining UAV Flight Control data, unmanned plane during flying status safety Early-warning Model is utilized to judge unmanned plane during flying state, and real-time early warning.

Communication module, for realizing the mutual of data between ground monitoring station, from ground monitoring station, read the GPS location of unmanned plane, state of flight, flight control data data and patrol and examine the Monitoring Data such as process data, and Monitoring Data being delivered to unmanned plane during flying state estimation early warning platform.This communication module adopts and includes but not limited to current GSM, GPRS, CDMA, WCDMA, TD-SCDMA, LTE and WIFI communication standard wireless mobile communications.

As shown in Figure 2, for the unmanned plane during flying state real-time assessment method for early warning of polling transmission line, comprise the following steps:

1. obtain flight control data, unmanned plane during flying state estimation early warning platform is by ground monitoring station Real-time Obtaining UAV Flight Control data.

2. unmanned plane during flying state automatic evaluation, appraisal procedure as shown in Figure 3, comprises step as follows:

1) flight control data imports.

2) data prediction, utilizes cluster data analytical approach, carries out pre-service to the flight control information of unmanned plane, extracts the input of key component as mapping relations, thus determines the initial primary topology of mapping relations.

The unmanned plane safe early warning information be made up of effect quantity (motor speed, attitude, speed, directional angular velocity etc.) and influence amount (temperature, state-of-charge, humidity, distance, flight time etc.) represents the set of research object.The knowledge of these objects is described by the attribute (unmanned plane effect quantity and influence amount) of appointed object and their property value (Monitoring Data).

A unmanned plane safe early warning information table, represents when meeting some condition (influence amount), the situation of unmanned plane effect quantity.A unmanned plane safe early warning information table S can be expressed as:

S=(U, R, V, F) formula (1)

R=C ∪ D formula (2)

In formula, U is nonempty finite set, is the set of object, U={x 1, x 2..., x n, n>=0; R=C ∪ D is nonempty finite set, is called community set, and subset C is called conditional attribute collection (affecting quantity set), and subset D is called decision kind set (effect quantity set), C={c 1, c 2..., c m, m>=0; D={d 1, d 2..., d k, k>=0; V=∪ a ∈ Rv athe set of property value, V arepresent the range of attributes of attribute a, wherein a meets a ∈ R.F: be an information function, it specifies the property value of each object x in U, i.e. V a=F (x, a), x ∈ U, a ∈ R.

Table 1 is the unmanned plane safe early warning information table defined by formula (1), and a sample in its every a line representation theory territory, attribute and property value are shown in each list.

Form 1 unmanned plane safe early warning information table

We can build the data matrix based on safety information table thus, and object-attribute matrix is as follows:

formula (3)

Wherein a ij(i=1 ..., n; J=1 ..., m) be the property value of a jth attribute of i-th sample.Described by the i-th row that i-th sample Ai is matrix A, so the similarity between any two sample AK and AL, can be distinguished by the line K in matrix A and the capable similarity degree of L; Any Two Variables a kwith a lbetween similarity, the similarity degree that can be arranged with L by K row be distinguished.

3) time series models are built, adopt Time series analysis method and variable analysis method, in conjunction with the dynamic sample data that unmanned plane runs, the key setting up unmanned plane runs variable (motor speed, attitude, speed, acceleration etc.) Time series forecasting model.

4) SVM model construction, application least square method supporting vector machine algorithm, using training error as the constraint condition of optimization problem, minimize as optimization aim using fiducial range value, UAS moving law is summarized from the safe operation primary monitoring data learning of unmanned plane, thus the structure realized unmanned plane safe early warning model, realize the relation of effective Simulation and Prediction unmanned plane duty and major influence factors.

Suppose that training sample is { (x 1, y 2) ..., (x i, y i), wherein x i∈ X ∈ R m, X is called the input space, x ibe the input value of i-th learning sample, and be m dimensional vector, y i∈ Y ∈ R mfor the aim parameter of correspondence.Adopt a Nonlinear Mapping θ (c) to be the high-dimensional feature space F of k from former spatial mappings to dimension by sample, then in high-dimensional feature space, carry out linear regression.If regression function is:

F (x)=(ω, θ (x))+b formula (4)

In formula, ω is weight vector, ω ∈ R k, be the item of described function f (x) complexity; (ω, θ (x)) represents the inner product of ω and θ (x); θ is R mspace is to the Nonlinear Mapping in F space; B is constant, b ∈ R.

According to structural risk minimization principle, consider function complexity and error of fitting, above-mentioned function looks back problem equivalent in minimizing cost functional:

m i n 1 2 | | &omega; | | 2 + 1 2 C&Sigma; i = 1 l &epsiv; 2 Formula (5)

ε=y itθ (x i)-b, i=1,2 ..., 1 formula (6)

Wherein, ε is slack variable, ε >=0; C is punishment parameter, and C > 0, its effect gets compromise between empiric risk and model complexity.

Formula (9) uses the core concept of Statistical Learning Theory, and both controlled training errors, have Controlling model complexity, and object is to obtain a little expected risk.The generalization ability of model can be improved like this.

In order to solve above-mentioned optimization problem, set up Lagrangian function:

L ( &omega; , b , &epsiv; , &alpha; ) = 1 2 | | &omega; | | 2 + 1 2 C&Sigma; i = 1 l &epsiv; 2 + &Sigma; i = 1 l &alpha; i ( &omega; T &CenterDot; &theta; ( x i ) + b + &epsiv; - y i ) Formula (7)

In formula, α ifor Lagrange multiplier.

According to KKT (Karush-Kuhn-Tucker) optimal conditions:

&part; L &part; &omega; = &omega; - &Sigma; i = 1 l &alpha; i &theta; ( x i ) = 0 Formula (8)

&part; L &part; b = &Sigma; i = 1 l &alpha; i = 0 Formula (9)

&part; L &part; &epsiv; = C&Sigma; i = 1 l &epsiv; + &Sigma; i = 1 l &alpha; i = 0 Formula (10)

&part; L &part; &alpha; = &omega; T &CenterDot; &theta; ( x i ) + b + &epsiv; - y i = 0 Formula (11)

After cancellation ε and ω, obtain following system of linear equations:

0 e T e Q + I C b &alpha; = 0 y Formula (12)

In formula, e=[1,1 ..., 1] t; I is unit matrix; α=[α 1, α 2..., α l] t; Q ij=(x i) t; θ (x i)=K (x i, x j) be kernel function, i, j=1,2 ..., l.

Can be obtained by formula 12, optimization problem is converted into the system of linear equations solved with least square method by above-mentioned algorithm of support vector machine.Finally can obtain following regression model:

f ( x ) = &Sigma; i = 1 l &alpha; i K ( x , x i ) + b Formula (13)

5) calculate and export Forewarn evaluation result.

If a certain moment t, according to the influence amount of unmanned plane and the threshold values sample data of effect quantity, calculated the influence amount threshold values x such as motor temperature, state-of-charge, humidity, air route, distance, speed, flight time trying to achieve unmanned plane by time sequence analysis algorithm twith the threshold values y of the effect quantity such as temperature rise, unit consumption of energy, motor speed t.Respective effects amount is tried to achieve by safe early warning model will with the threshold values y of effect quantity tcompare, obtain according to Probability Statistics Theory, the probability falling into [0,2S] is 95.5%, and the probability falling into [0,3S] is 99.7%, and wherein S is the standard deviation of model.Accordingly, early warning can be carried out by following several situation to unmanned plane running status.

Normal: | y t - y ^ t | &le; 2 S ;

Normal: observed reading trendless changes;

Abnormal: observed reading has tendency to change;

Report to the police: 3 S < | y t - y ^ t | .

When generation the 3rd kind and the 4th kind of situation, need to analyze unmanned plane service data, determine whether the effect quantity of this unmanned plane and influence amount exceed its threshold values and output abnormality and warning.

3. judge whether existing problems, if existing problems enter 4, the flow process that do not have problems terminates.

4. unmanned plane during flying state issues early warning, may carry out early warning by Problems existing by unmanned plane.

5. judge whether to need problem to adjust, need adjustment to enter 6, do not need adjustment to enter 7;

6. state of flight adjustment instruction issues, and carries out state of flight adjustment by ground monitoring station real time down flight steering order to unmanned plane.

State of flight problem uploads to unmanned plane during flying state estimation Warning Service device, terminates unmanned plane during flying state real-time assessment early warning flow process.

By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various amendment or distortion that creative work can make still within protection scope of the present invention.

Claims (17)

1. a unmanned plane during flying state real-time assessment early warning system, is characterized in that: comprise assessment early warning platform, unmanned plane and ground monitoring station, wherein:
Described ground monitoring station, for obtaining the Flight Condition Data of unmanned plane, is passed to assessment early warning platform;
Described assessment early warning platform, for reading unmanned plane during flying status information and flight control data, the UAV Flight Control data accumulated by each patrol task, build unmanned plane during flying state estimation model, unmanned plane during flying state is judged, receive the flight directive of host computer transmission, the flight of adjustment unmanned plane simultaneously.
2. a kind of unmanned plane during flying state real-time assessment early warning system as claimed in claim 1, is characterized in that: described assessment early warning platform comprises state of flight assessment warning module, flight control data administration module, human-computer interaction module, communication module, wherein:
Described communication module, for communicating with ground monitoring station, receiving unmanned plane during flying status data, being transferred to flight control data administration module;
Described flight control data administration module, control historical data for patrolling and examining the corresponding flight of aerial mission management according to power transmission line unmanned machine, and accumulate UAV Flight Control data by communication module Real-time Obtaining;
Described state of flight assessment warning module, reads the UAV Flight Control data of patrolling and examining aerial mission accumulation in flight control data administration module, sets up unmanned plane during flying status safety Early-warning Model, judge unmanned plane during flying state, early warning;
Described human-computer interaction module, for obtaining and resolving external command, sends out the external command after parsing and is sent to unmanned plane execution corresponding flight adjustment instruction by ground monitoring station.
3. a kind of unmanned plane during flying state real-time assessment early warning system as claimed in claim 1, is characterized in that: described ground monitoring station, passes link and is connected with unmanned plane, adopt with unmanned plane the communication mode be connected one to one by number; Pass link by figure to be connected with unmanned plane, that reads unmanned plane patrols and examines video data.
4. a kind of unmanned plane during flying state real-time assessment early warning system as claimed in claim 1, it is characterized in that: described state of flight assessment warning module, according to the UAV Flight Control data that each patrol task accumulates, operate time sequential analysis, clustering method and support vector machine set up unmanned plane during flying status safety Early-warning Model, simultaneously when unmanned plane performs polling transmission line aerial mission, by ground monitoring station Real-time Obtaining UAV Flight Control data, utilize unmanned plane during flying status safety Early-warning Model real-time judge unmanned plane during flying state, and early warning in time.
5. a kind of unmanned plane during flying state real-time assessment early warning system as claimed in claim 1, it is characterized in that: described communication module, be one-to-many connected mode with ground monitoring station, realize the mutual of the data between ground monitoring station, from ground monitoring station, read the GPS location of unmanned plane, state of flight, flight control data data and patrol and examine process data.
6. a patrol unmanned machine state of flight real-time assessment method for early warning, is characterized in that: comprise the following steps:
(1) ground monitoring station obtains Flight Condition Data and the flight control data of unmanned plane, is transferred to assessment early warning platform;
(2) pre-service is carried out to the flight control information of unmanned plane, according to effect quantity and the influence amount structure unmanned plane safe early warning object set of state of flight, extract critical data as the input of mapping relations, determine the initial primary topology of mapping relations;
(3) in conjunction with the dynamic sample data that unmanned plane runs, the key building unmanned plane runs time series variation forecast model, carries out monitoring and forecast to the critical data that unmanned plane runs;
(4) using training error as the constraint condition of optimization problem, minimize as optimization aim using fiducial range value, build unmanned plane safe early warning model, the relation of Simulation and Prediction unmanned plane duty and influence factor;
(5) Forewarn evaluation result is exported, according to the flight directive of early warning judged result adjustment unmanned plane.
7. a kind of patrol unmanned machine state of flight real-time assessment method for early warning as claimed in claim 6, it is characterized in that: in described step (2), utilize cluster data analytical approach, pre-service is carried out to the flight control information of unmanned plane, extract the input of key component as mapping relations, thus determine the initial primary topology of mapping relations, effect quantity and the influence amount information aggregate of unmanned plane are described by the attribute and property value formulating object, build unmanned plane safe early warning information table, data matrix is built based on unmanned plane safe early warning information table, express the similarity degree of each variable in set.
8. a kind of patrol unmanned machine state of flight real-time assessment method for early warning as claimed in claim 6, it is characterized in that: in described step (2), unmanned plane safe early warning information table S is expressed as:
S=(U,R,V,F)
R=C∪D
In formula, U is nonempty finite set, is the set of object, U={x 1, x 2..., x n, n>=0; R=C ∪ D is nonempty finite set, is called community set, and subset C is called conditional attribute collection, namely affects quantity set, and subset D is called decision kind set, i.e. effect quantity set, C={c 1, c 2..., c m, m>=0; D={d 1, d 2..., d k, k>=0; V=∪ a ∈ Rv athe set of property value, V arepresent the range of attributes of attribute a, wherein a meets a ∈ R, and F is an information function, and it specifies the property value of each object x in U, i.e. V a=F (x, a), x ∈ U, a ∈ R.
9. a kind of patrol unmanned machine state of flight real-time assessment method for early warning as claimed in claim 6, is characterized in that: in described step (2), build the data matrix based on safety information table, object-attribute matrix is as follows:
Wherein a ij(i=1 ..., n; J=1 ..., m) be the property value of a jth attribute of i-th sample.I-th sample A idescribed by the i-th row of matrix A, any two sample A kwith A lbetween similarity, distinguished by the line K in matrix A and the capable similarity degree of L; Any Two Variables a kwith a lbetween similarity, the similarity degree arranged with L by K row is distinguished.
10. a kind of patrol unmanned machine state of flight real-time assessment method for early warning as claimed in claim 6, is characterized in that: in described step (2), effect quantity comprises motor speed, attitude, speed and directional angular velocity.
11. a kind of patrol unmanned machine state of flight real-time assessment method for early warning as claimed in claim 6, it is characterized in that: in described step (2), influence amount comprises temperature, state-of-charge, humidity, Distance geometry flight time.
12. a kind of patrol unmanned machine state of flight real-time assessment method for early warning as claimed in claim 6, it is characterized in that: in described step (3), concrete steps are: build time series models, adopt Time series analysis method and variable analysis method, in conjunction with the dynamic sample data that unmanned plane runs, the key setting up unmanned plane runs the Time series forecasting model of variable.
13. a kind of patrol unmanned machine state of flight real-time assessment method for early warning as claimed in claim 6, is characterized in that: described critical data comprises motor speed, attitude, speed and acceleration.
14. a kind of patrol unmanned machine state of flight real-time assessment method for early warning as claimed in claim 6, it is characterized in that: in described step (4), application least square method supporting vector machine algorithm, using training error as the constraint condition of optimization problem, minimize as optimization aim using fiducial range value, UAS moving law is summarized from the safe operation primary monitoring data learning of unmanned plane, build unmanned plane safe early warning model, the relation of Simulation and Prediction unmanned plane duty and major influence factors.
15. a kind of patrol unmanned machine state of flight real-time assessment method for early warning as claimed in claim 6, it is characterized in that: in described step (4), concrete grammar is: adopt a Nonlinear Mapping to be the high-dimensional feature space of k from former spatial mappings to dimension by sample, then in high-dimensional feature space, linear regression is carried out, according to structural risk minimization principle, consider function complexity and error of fitting, regression function is looked back problem equivalent in minimizing cost functional, utilize Lagrange and KKT optimal condition, optimization problem is converted into the system of linear equations solved with least square method, obtain the regression model of unmanned plane safe early warning.
16. a kind of patrol unmanned machine state of flight real-time assessment method for early warning as claimed in claim 6, it is characterized in that: in described step (5), concrete grammar is: according to the influence amount of unmanned plane and the threshold values sample data of effect quantity, calculated by time sequence analysis algorithm and try to achieve the influence amount of unmanned plane and the threshold values of effect quantity, the threshold values of trying to achieve respective effects amount and effect quantity according to safe early warning model is done difference and ask absolute value, according to the relation of itself and standard deviation, unmanned plane running status is judged, if need to report to the police, then report to the police, and adjust unmanned plane during flying state, state of flight problem is uploaded to unmanned plane during flying state estimation Warning Service device.
17. a kind of patrol unmanned machine state of flight real-time assessment method for early warning as claimed in claim 6, is characterized in that: in described step (5), method for early warning is specially:
When time, unmanned plane during flying is normal;
When time, think that unmanned plane during flying is normal, observed reading trendless changes;
When time, think that unmanned plane during flying is substantially abnormal, observed reading has tendency to change;
When time, report to the police;
When observed reading trendless changes, need to determine whether the effect quantity of this unmanned plane and influence amount exceed its threshold values, if exceeded, output abnormality and warning;
Wherein s is the standard deviation of model, for respective effects amount tried to achieve by safe early warning model, y tfor the threshold values of effect quantity.
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