CN106773709A - A kind of immersion unmanned plane drives flight system - Google Patents

A kind of immersion unmanned plane drives flight system Download PDF

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Publication number
CN106773709A
CN106773709A CN201710022039.7A CN201710022039A CN106773709A CN 106773709 A CN106773709 A CN 106773709A CN 201710022039 A CN201710022039 A CN 201710022039A CN 106773709 A CN106773709 A CN 106773709A
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unmanned plane
fault diagnosis
information
processing unit
immersion
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不公告发明人
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Shenzhen Ming Automatic Control Technology Co Ltd
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Shenzhen Ming Automatic Control Technology Co Ltd
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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

Flight system, including airborne device, terrestrial information processing unit, unmanned plane failure detector are driven the invention provides a kind of immersion unmanned plane, airborne device, unmanned plane failure detector all pass through wireless connection with terrestrial information processing unit.The present invention realizes the virtual transplanting that unmanned plane drives visual angle, us are enable more intuitively to observe the flight visual angle of unmanned plane, the flight experience of immersion is obtained, and fault detect can be carried out to unmanned plane, so as to be taken measures in time when unmanned plane breaks down.

Description

A kind of immersion unmanned plane drives flight system
Technical field
The present invention relates to unmanned plane field, and in particular to a kind of immersion unmanned plane drives flight system.
Background technology
In correlation technique, the manipulation of unmanned plane still rely primarily on human eye original place distant surveillance unmanned plane during flying height with Attitude, and by remote control come control flight, because the influence of distance and unmanned plane speed is, it is necessary to manipulator's real-time control unmanned plane State of flight, the picture that thus cannot well control unmanned machine head to shoot simultaneously;And existing plane shows picture Flight impression when face can not reach real flight management, also easily because personal operational error occur air crash, aircraft bombing or The event hurted sb.'s feelings occurs.
The content of the invention
Regarding to the issue above, the present invention provides a kind of immersion unmanned plane and drives flight system.
The purpose of the present invention is realized using following technical scheme:
A kind of immersion unmanned plane drives flight system, including airborne device, terrestrial information processing unit, unmanned plane failure Detection means, airborne device, unmanned plane failure detector all pass through wireless connection with terrestrial information processing unit;The airborne dress Setting in the positional information and the image of surrounding environment that on unmanned plane, control unmanned plane during flying state and collection unmanned plane, and will Image and positional information give terrestrial information processing unit, and receive the head angle information of terrestrial information processing unit and control refers to Order;The unmanned plane failure detector is used to carry out unmanned plane fault detect, and failure detection result is sent to ground letter Breath processing unit;The positional information and image that the terrestrial information processing unit receives airborne device transmission are analyzed, by ground The computer augmented reality treatment of face information processor, the surrounding enviroment information of unmanned plane position, road are believed It is added to again in real world image spacing after breath, transport information, beacon information, landmark information analog simulation, by virtual existing Real glasses are presented to manipulator, and are alarmed accordingly according to failure detection result.
Beneficial effects of the present invention are:The virtual transplanting that unmanned plane drives visual angle is realized, us is more intuitively seen The flight visual angle of unmanned plane is observed, the flight experience of immersion is obtained, and fault detect can be carried out to unmanned plane, so as to Taken measures in time when unmanned plane breaks down.
Brief description of the drawings
Using accompanying drawing, the invention will be further described, but embodiment in accompanying drawing is not constituted to any limit of the invention System, for one of ordinary skill in the art, on the premise of not paying creative work, can also obtain according to the following drawings Other accompanying drawings.
Fig. 1 is structure connection diagram of the invention;
Fig. 2 is the structured flowchart of unmanned plane failure detector.
Reference:
Airborne device 1, terrestrial information processing unit 2, unmanned plane failure detector 3, historical data acquisition module 11, number Data preprocess module 12, characteristic extracting module 13, real-time fault diagnosis characteristic vector acquisition module 14, fault diagnosis model are set up Module 15, fault diagnosis identification module 16.
Specific embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, present embodiments provide a kind of immersion unmanned plane and drive flight system, including airborne device 1, ground Information processor 2, unmanned plane failure detector 3, airborne device 1, unmanned plane failure detector 3 all with terrestrial information at Reason device 2 passes through wireless connection;The airborne device 1 is located on unmanned plane, is controlled unmanned plane during flying state and is gathered unmanned plane The image of positional information and surrounding environment, and by image and positional information to terrestrial information processing unit 2, and receive terrestrial information The head angle information and control instruction of processing unit 2;The unmanned plane failure detector 3 is used to carry out failure inspection to unmanned plane Survey, and failure detection result is sent to terrestrial information processing unit 2;The terrestrial information processing unit 2 receives airborne device 1 and sends out The positional information and image sent are analyzed, and are processed by the computer augmented reality of terrestrial information processing unit 2, by nothing Folded again after surrounding enviroment information, road information, transport information, beacon information, the landmark information analog simulation of man-machine position It is added in real world image spacing, is presented to manipulator by virtual reality glasses, and phase is carried out according to failure detection result The alarm answered.
Preferably, the airborne device 1 includes three axle heads, binocular camera, GPS.
Preferably, the terrestrial information processing unit 2 includes virtual reality glasses, computer and alarm, computer difference It is connected with virtual reality glasses, alarm;The computer is used for unmanned plane during flying height, attitude, the GPS location information that will be gathered It is analyzed, is processed by existing augmented reality, after the surrounding enviroment information simulation emulation of unmanned plane position In the real world image spacing that is added to, virtual reality glasses are then transferred to;The virtual reality glasses are used to show process Image information after computer superposition augmented reality effect;The alarm is used to represent that unmanned plane sends event in failure detection result Alarmed during barrier.
The above embodiment of the present invention realizes the virtual transplanting that unmanned plane drives visual angle, us is more intuitively observed The flight visual angle of unmanned plane, obtains the flight experience of immersion, and can carry out fault detect to unmanned plane, so as in nothing It is man-machine to take measures in time when breaking down.
Preferably, the unmanned plane failure detector 3 includes:
(1) historical data acquisition module 11, for gathering unmanned plane in normal state and various failures by sensor The historical vibration signal data of multiple measuring points when being run under state;
(2) data preprocessing module 12, for being pre-processed to the original historical vibration signal data for collecting;
(3) this feature extraction module 13, for extracting wavelet packet singular value from the historical vibration signal data after filtering Feature, and the wavelet packet singular value features that will be extracted are used as fault diagnosis characteristic vector sample;
(4) real-time fault diagnosis characteristic vector acquisition module 14, for obtain the real-time fault diagnosis feature of unmanned plane to Amount;
(5) fault diagnosis model sets up module 15, for setting up the fault diagnosis model based on improved SVMs, And fault diagnosis model is trained using fault diagnosis characteristic vector sample, calculate the optimal of fault diagnosis model parameter Solution, obtains the fault diagnosis model that training is completed;
(6) fault diagnosis identification module 16, for the real-time fault diagnosis characteristic vector of the unmanned plane to be input into training In the fault diagnosis model of completion, the diagnosis identification of unmanned plane failure is completed.
Preferably, the data preprocessing module 12 using digital filter according to filtering formula to the original history that collects Vibration signal data is pre-processed, and the filtering formula is:
Wherein, Ω is the historical vibration signal data obtained after filtering, and Ω ' is the original historical vibration signal number for collecting According to L is the number of measuring point, Ψ=1,2,3 ... L-1;τ is the constant determined by digital filter self-characteristic, and θ is sensing used The intrinsic frequency acquisition of device.
This preferred embodiment is pre-processed according to filtering formula using digital filter to data, being capable of self adaptation difference Vibration signal, and can eliminate the time domain waveform in original historical vibration signal data distortion, so as to improve data prediction Precision, it is ensured that the accuracy of Fault Identification is carried out to unmanned plane.
Preferably, when wavelet packet singular value features are extracted, this feature extraction module 13 is specifically performed:
(1) it is H to set unmanned plane and be in the historical vibration signal at the moment measured from measuring point M during state HM(Ω), M =1 ..., L, L are the number of measuring point, to HM(Ω) is carried outLayer scattering WAVELET PACKET DECOMPOSITION, extracts theIn layerIndividual resolving system Number, is reconstructed to all of decomposition coefficient, withRepresent theThe reconstruction signal of each node of layer, structure Build eigenmatrixWhereinValue combined according to historical experience and actual conditions and determine;
(2) to eigenmatrix T [HM(Ω)] singular value decomposition is carried out, obtain this feature matrix T [HM(Ω)] feature to Amount, wherein W1,W2,…,WvIt is by eigenmatrix T [HM(Ω)] decompose singular value, v is by eigenmatrix T [HM(Ω)] decompose Singular value number:
(3) H is definedM(Ω) corresponding fault diagnosis characteristic vectorFor:
Wherein,Represent characteristic vectorIn maximum singular value,Represent special Levy vectorIn minimum singular value;
(4) the fault diagnosis characteristic vector being calculated is screened, excludes underproof fault diagnosis characteristic vector, If the quantity of the underproof fault diagnosis characteristic vector for excluding is L ', then in the fixed time when unmanned plane is in state H Fault diagnosis characteristic vector sample be:
This preferred embodiment extracts wavelet packet singular value features as fault diagnosis characteristic vector, special compared to other are extracted Levy as fault diagnosis characteristic vector, with accuracy rate higher and shorter calculating time, it is possible to increase unmanned plane is entered The fault-tolerance of row diagnosis, so as to be advantageously implemented the Precise Diagnosis to unmanned plane failure.
Preferably, when the fault diagnosis characteristic vector that this feature extraction module 13 pairs is calculated is screened, specifically hold OK:
(1) using unmanned plane be in state H when the moment all fault diagnosis characteristic vectors being calculated as this The characteristic vector Screening Samples collection at moment, calculates the standard deviation sigma of this feature vector Screening Samples collectionHWith desired value μH
(2) if the fault diagnosis characteristic vector being calculatedIt is unsatisfactory for The fault diagnosis characteristic vector is then rejected, wherein,It is desired value μHMaximal possibility estimation,It is standard deviation sigmaHIt is maximum seemingly So estimate.
This preferred embodiment excludes underproof fault diagnosis characteristic vector using aforesaid way, and objective science improves The accuracy of fault diagnosis is carried out to unmanned plane.
Preferably, the underproof fault diagnosis characteristic vector rejected also is stored into one and faced by this feature extraction module 13 When data storage in, and in characteristic extracting module 13Value is further corrected, specially:If ThenValue combined according to original historical experience and actual conditions It is determined that on the basis of be revised asIfThenValue according to original history Experience and actual conditions are combined on the basis of determining and are revised asWherein, L is the number of measuring point, and L ' is underproof failure The quantity of diagnostic characteristic vector, D is the integer threshold values being manually set.
This preferred embodiment reduce further underproof fault diagnosis characteristic vector and carry out fault diagnosis to unmanned plane Influence.
Preferably, the fault diagnosis model that should be based on improved SVMs sets up as follows:
(1) using RBF as kernel function, using the kernel function by the fault diagnosis characteristic vector sample from original Space reflection is to higher dimensional space;
(2) fault diagnosis characteristic vector sample classification is realized in higher dimensional space construction optimal decision function, constructs optimal determining Plan function is:
In formula, x is the fault diagnosis characteristic vector sample of input, and ξ (x) is the fault diagnosis characteristic vector sample pair of input The output answered, J (x) represents RBF, and c is weight vectors, and e is deviation;Additionally,For introduce optimization because Son, wherein L are the number of measuring point, and L ' is the quantity of underproof fault diagnosis characteristic vector;
(3) object function of SVMs and the constraints of SVMs are defined, and solves the SVMs Object function, calculate weight vectors and deviation, the weight vectors and deviation that will be calculated substitute into optimal decision function i.e. It is the fault diagnosis model set up;The object function of wherein SVMs is defined as:
The constraints of SVMs is defined as:
yα≥1-εα, εα>=0, α=1 ..., M
In formula,It is the object function of SVMs, ψ*It is the penalty factor after optimization, εαTo introduce Error variance;M is the quantity of fault diagnosis characteristic vector sample;
In addition, xαIt is the α fault diagnosis characteristic vector sample of input, yαFor input the α fault diagnosis feature to The corresponding output of amount sample, c is weight vectors, and e is deviation.
This preferred embodiment reduces underproof fault diagnosis characteristic vector and unmanned plane is entered by introducing Optimization Factor The influence of row fault diagnosis, further increases the actual accuracy of the optimal decision function, is the foundation of fault diagnosis model Good functional foundations are provided, so as to build more accurate fault diagnosis model such that it is able to ensure to carry out unmanned plane event Hinder the accuracy of diagnosis.
Wherein, the value of the radius parameter of penalty factor and the kernel function is optimized in the following manner:
All fault diagnosis characteristic vector sample means are divided into the subset not included mutually, setting penalty factor and the core letter The span of the value of several radius parameters, the position vector to each particle carries out two-dimensional encoded, generation primary group;It is right The selected training set of the corresponding parameter of each particle carries out cross validation, and the forecast model classification accuracy for obtaining is corresponding as particle Target function value, is iterated to the particle in population;All particles are evaluated with target function value, when working as certain particle When preceding evaluation of estimate is better than its history evaluation value, as the optimal history evaluation of the particle, current particle optimal location is recorded Vector;Globally optimal solution is found, if its value is better than current history optimal solution, is updated, when reaching the stop criterion of setting, then Stop search, export the value of optimal penalty factor and the radius parameter of the kernel function, otherwise return to re-search for.
This preferred embodiment is optimized using aforesaid way to the value of penalty factor and the radius parameter of the kernel function, excellent The change time is relatively short, and effect of optimization is good, can obtain the SVMs of better performances, and further improve is carried out to unmanned plane The precision of fault diagnosis.
According to above-described embodiment, inventor has carried out a series of tests, the following is test the experimental data that obtains:
Above-mentioned experimental data shows that the present invention can carry out good flight view, obtains the flying body of immersion Test, and fault detect accurately can be carried out to unmanned plane, so as to prevent the generation of accident, it is ensured that can be obtained when unmanned plane breaks down To timely, safe maintenance, it can be seen that, the present invention in terms of unmanned plane during flying view and unmanned plane fault detect side Face generates the beneficial effect of highly significant.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected The limitation of scope is protected, although being explained to the present invention with reference to preferred embodiment, one of ordinary skill in the art should Work as understanding, technical scheme can be modified or equivalent, without deviating from the reality of technical solution of the present invention Matter and scope.

Claims (5)

1. a kind of immersion unmanned plane drives flight system, it is characterized in that, including airborne device, terrestrial information processing unit, nothing Man machine fault's detection means, airborne device, unmanned plane failure detector all pass through wireless connection with terrestrial information processing unit; The airborne device is located on unmanned plane, the figure of the positional information and surrounding environment of control unmanned plane during flying state and collection unmanned plane Picture, and terrestrial information processing unit is given by image and positional information, and receive the head angle information of terrestrial information processing unit And control instruction;The unmanned plane failure detector is used to carry out fault detect to unmanned plane, and failure detection result is sent To terrestrial information processing unit;The positional information and image that the terrestrial information processing unit receives airborne device transmission are divided Analysis, is processed by the computer augmented reality of terrestrial information processing unit, and the surrounding enviroment of unmanned plane position are believed It is added to again in real world image spacing after breath, road information, transport information, beacon information, landmark information analog simulation, is led to Cross virtual reality glasses and be presented to manipulator, and alarmed accordingly according to failure detection result.
2. a kind of immersion unmanned plane according to claim 1 drives flight system, it is characterized in that, the airborne device includes Three axle heads, binocular camera, GPS.
3. a kind of immersion unmanned plane according to claim 2 drives flight system, it is characterized in that, terrestrial information treatment Device includes virtual reality glasses, computer and alarm, and computer is connected with virtual reality glasses, alarm respectively;The meter Calculation machine is used to be analyzed the unmanned plane during flying height of collection, attitude, GPS location information, by existing augmented reality skill Art treatment, will be added in real world image spacing, so after the surrounding enviroment information simulation emulation of unmanned plane position After be transferred to virtual reality glasses;The virtual reality glasses are used to show the image by after computer superposition augmented reality effect Information;The alarm is used to be alarmed when failure detection result represents that unmanned plane sends failure.
4. a kind of immersion unmanned plane according to claim 3 drives flight system, it is characterized in that, unmanned plane failure inspection Surveying device includes:
(1) historical data acquisition module 11, for gathering unmanned plane in normal state and various malfunctions by sensor The historical vibration signal data of multiple measuring points during lower operation;,
(2) data preprocessing module 12, for being pre-processed to the original historical vibration signal data for collecting;
(3) this feature extraction module 13, for extracting wavelet packet singular value features from the historical vibration signal data after filtering, And the wavelet packet singular value features that will be extracted are used as fault diagnosis characteristic vector sample;
(4) real-time fault diagnosis characteristic vector acquisition module 14, the real-time fault diagnosis characteristic vector for obtaining unmanned plane;
(5) fault diagnosis model sets up module 15, for setting up the fault diagnosis model based on improved SVMs, and makes Fault diagnosis model is trained with fault diagnosis characteristic vector sample, calculates the optimal solution of fault diagnosis model parameter, Obtain the fault diagnosis model that training is completed;
(6) fault diagnosis identification module 16, completes for the real-time fault diagnosis characteristic vector of the unmanned plane to be input into training Fault diagnosis model in, complete unmanned plane failure diagnosis identification.
5. a kind of immersion unmanned plane according to claim 4 drives flight system, it is characterized in that, the data prediction mould Block 12 is pre-processed according to filtering formula using digital filter to the original historical vibration signal data for collecting, the filtering Formula is:
Ω = Σ Ψ = 1 L - 1 ( L 2 - Ψ 2 L 2 + Ψ 2 2 × τ 2 θ × Ω ′ )
Wherein, Ω is the historical vibration signal data obtained after filtering, and Ω ' is the original historical vibration signal data for collecting, L It is the number of measuring point, Ψ=1,2,3 ... L-1;τ is the constant determined by digital filter self-characteristic, and θ is sensor used Intrinsic frequency acquisition.
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US11074730B1 (en) 2020-01-23 2021-07-27 Netapp, Inc. Augmented reality diagnostic tool for data center nodes

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Application publication date: 20170531