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

Info

Publication number
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
Authority
CN
China
Prior art keywords
unmanned plane
early warning
flight
real
data
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.)
Granted
Application number
CN201510734491.7A
Other languages
Chinese (zh)
Other versions
CN105427674B (en
Inventor
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
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Intelligent Technology Co Ltd
Original Assignee
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
Shandong Luneng Intelligence Technology Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd, Shandong Luneng Intelligence Technology Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN201510734491.7A priority Critical patent/CN105427674B/en
Publication of CN105427674A publication Critical patent/CN105427674A/en
Application granted granted Critical
Publication of CN105427674B publication Critical patent/CN105427674B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0047—Navigation or guidance aids for a single aircraft
    • G08G5/0069—Navigation or guidance aids for a single aircraft specially adapted for an unmanned aircraft

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

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 i-ω tθ (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.
CN201510734491.7A 2015-11-02 2015-11-02 A kind of unmanned plane during flying state assesses early warning system and method in real time Active CN105427674B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201510734491.7A CN105427674B (en) 2015-11-02 2015-11-02 A kind of unmanned plane during flying state assesses early warning system and method in real time

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201510734491.7A CN105427674B (en) 2015-11-02 2015-11-02 A kind of unmanned plane during flying state assesses early warning system and method in real time

Publications (2)

Publication Number Publication Date
CN105427674A true CN105427674A (en) 2016-03-23
CN105427674B CN105427674B (en) 2017-12-12

Family

ID=55505847

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201510734491.7A Active CN105427674B (en) 2015-11-02 2015-11-02 A kind of unmanned plane during flying state assesses early warning system and method in real time

Country Status (1)

Country Link
CN (1) CN105427674B (en)

Cited By (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105788394A (en) * 2016-04-16 2016-07-20 吉林医药学院 Maintenance detection simulated training system for unmanned plane
CN105825718A (en) * 2016-04-26 2016-08-03 广东容祺智能科技有限公司 Unmanned aerial vehicle comprehensive management platform system
CN106096207A (en) * 2016-06-29 2016-11-09 武汉中观自动化科技有限公司 A kind of rotor wing unmanned aerial vehicle wind resistance appraisal procedure based on multi-vision visual and system
CN106444572A (en) * 2016-07-21 2017-02-22 中交信通（天津）科技有限公司 Unmanned aerial vehicle safety monitoring and warning device based on Beidou system
CN106774411A (en) * 2016-12-31 2017-05-31 清华大学深圳研究生院 Unmanned plane middleware system based on PHM
CN106769089A (en) * 2016-12-19 2017-05-31 中国航空工业集团公司沈阳飞机设计研究所 Unmanned plane during flying performance evaluation and the flight quality integrated method for real-time monitoring of assessment
CN106971432A (en) * 2017-04-11 2017-07-21 中国人民解放军海军航空工程学院青岛校区 A kind of airplane data management record system and data presentation technique
CN107463161A (en) * 2016-06-02 2017-12-12 空中客车运营简化股份公司 Predict the method and system and monitoring system of the failure in aircraft
CN107516451A (en) * 2017-10-08 2017-12-26 景遥（上海）信息技术有限公司 Fixed-wing UAV Intelligent flight training system
CN107590878A (en) * 2017-09-13 2018-01-16 中国人民解放军火箭军工程大学 A kind of unmanned plane during flying safe prediction apparatus for evaluating and method
CN108259223A (en) * 2017-12-07 2018-07-06 中国航空工业集团公司西安航空计算技术研究所 The unmanned plane network system security Situation Awareness appraisal procedure for preventing GPS from cheating
WO2018214387A1 (en) * 2017-05-23 2018-11-29 深圳大学 Distributed mining system and method for aviation-oriented electronic data
CN110222375A (en) * 2019-05-13 2019-09-10 北京航空航天大学 A kind of safety monitoring method of carrier landing process
CN110321951A (en) * 2019-07-01 2019-10-11 青岛海科虚拟现实研究院 A kind of VR simulated flight device evaluation of training method
CN110568855A (en) * 2018-06-06 2019-12-13 上海资誉电子科技有限公司 Unmanned aerial vehicle visual flight management system and method based on big data calculation engine
CN111554286A (en) * 2020-04-26 2020-08-18 云知声智能科技股份有限公司 Method and equipment for controlling unmanned aerial vehicle based on voice
CN111862550A (en) * 2020-08-13 2020-10-30 深圳市高巨创新科技开发有限公司 Formation unmanned aerial vehicle group departure alarm method and system
CN111935811A (en) * 2020-06-28 2020-11-13 北京遥测技术研究所 Airborne swarm terminal adaptive power control method based on temperature sensor
CN111968267A (en) * 2020-08-28 2020-11-20 珠海欧比特宇航科技股份有限公司 Airborne flight safety real-time monitoring and intelligent early warning device
CN113449238A (en) * 2020-03-24 2021-09-28 丰翼科技(深圳)有限公司 Unmanned aerial vehicle safe operation method and device, electronic equipment and storage medium
CN113759968A (en) * 2021-09-01 2021-12-07 中国南方电网有限责任公司超高压输电公司贵阳局 Unmanned aerial vehicle-based power grid line patrol planning method and system
CN114201925A (en) * 2022-02-17 2022-03-18 佛山科学技术学院 Unmanned aerial vehicle cluster cooperative task planning method, electronic equipment and readable storage medium
CN114373238A (en) * 2021-12-06 2022-04-19 特金智能科技(上海)有限公司 Attendance checking method and device for unmanned aerial vehicle inspection flight, electronic equipment and storage medium
CN114545325A (en) * 2020-11-25 2022-05-27 北京国网信通埃森哲信息技术有限公司 Unmanned aerial vehicle intelligent inspection method based on state information prediction under transformer substation
CN114689315A (en) * 2022-03-18 2022-07-01 山东超晟光电科技有限公司 Unmanned aerial vehicle belt line patrol accurate positioning method based on audio
CN115164884A (en) * 2022-07-19 2022-10-11 中国民航大学 Unmanned aerial vehicle flight state on-line monitoring system
CN111582740B (en) * 2020-05-13 2023-05-23 电子科技大学 Multi-rotor unmanned aerial vehicle risk assessment system
CN116384695A (en) * 2023-04-11 2023-07-04 中国人民解放军陆军工程大学 Unmanned aerial vehicle operation monitoring method and system based on independent overruling and combined overruling
CN116523384A (en) * 2023-04-11 2023-08-01 中国人民解放军陆军工程大学 Unmanned aerial vehicle efficiency determining method and system based on independent overruling and combined overruling
CN118466368A (en) * 2024-07-09 2024-08-09 克拉玛依市远山石油科技有限公司 Working state remote monitoring system and method for unmanned aerial vehicle aeromagnetic equipment

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100198514A1 (en) * 2009-02-02 2010-08-05 Carlos Thomas Miralles Multimode unmanned aerial vehicle
US20130124020A1 (en) * 2004-06-18 2013-05-16 L-3 Unmanned Systems, Inc. Autonomous collision avoidance system for unmanned aerial vehicles
CN103824340A (en) * 2014-03-07 2014-05-28 山东鲁能智能技术有限公司 Intelligent inspection system and inspection method for electric transmission line by unmanned aerial vehicle
CN104597907A (en) * 2014-11-27 2015-05-06 国家电网公司 Method for accurately evaluating flight of UAV (unmanned aerial vehicle) inspection system of overhead transmission line
CN104765968A (en) * 2015-04-21 2015-07-08 合肥工业大学 Unmanned aerial vehicle system health status evaluation device
CN104820428A (en) * 2015-04-20 2015-08-05 余江 Memory type track reproduction method of unmanned aerial vehicle and device

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130124020A1 (en) * 2004-06-18 2013-05-16 L-3 Unmanned Systems, Inc. Autonomous collision avoidance system for unmanned aerial vehicles
US20100198514A1 (en) * 2009-02-02 2010-08-05 Carlos Thomas Miralles Multimode unmanned aerial vehicle
CN103824340A (en) * 2014-03-07 2014-05-28 山东鲁能智能技术有限公司 Intelligent inspection system and inspection method for electric transmission line by unmanned aerial vehicle
CN104597907A (en) * 2014-11-27 2015-05-06 国家电网公司 Method for accurately evaluating flight of UAV (unmanned aerial vehicle) inspection system of overhead transmission line
CN104820428A (en) * 2015-04-20 2015-08-05 余江 Memory type track reproduction method of unmanned aerial vehicle and device
CN104765968A (en) * 2015-04-21 2015-07-08 合肥工业大学 Unmanned aerial vehicle system health status evaluation device

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王洋 等: "跟踪地面目标小型无人机地面显控平台的设计与实现", 《遥测遥控》 *

Cited By (42)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105788394A (en) * 2016-04-16 2016-07-20 吉林医药学院 Maintenance detection simulated training system for unmanned plane
CN105825718A (en) * 2016-04-26 2016-08-03 广东容祺智能科技有限公司 Unmanned aerial vehicle comprehensive management platform system
CN107463161B (en) * 2016-06-02 2021-10-12 空中客车运营简化股份公司 Method and system for predicting a fault in an aircraft and monitoring system
CN107463161A (en) * 2016-06-02 2017-12-12 空中客车运营简化股份公司 Predict the method and system and monitoring system of the failure in aircraft
CN106096207A (en) * 2016-06-29 2016-11-09 武汉中观自动化科技有限公司 A kind of rotor wing unmanned aerial vehicle wind resistance appraisal procedure based on multi-vision visual and system
CN106096207B (en) * 2016-06-29 2019-06-07 武汉中观自动化科技有限公司 A kind of rotor wing unmanned aerial vehicle wind resistance appraisal procedure and system based on multi-vision visual
CN106444572A (en) * 2016-07-21 2017-02-22 中交信通（天津）科技有限公司 Unmanned aerial vehicle safety monitoring and warning device based on Beidou system
CN106769089B (en) * 2016-12-19 2019-04-23 中国航空工业集团公司沈阳飞机设计研究所 Unmanned plane during flying performance evaluation method for real-time monitoring integrated with flight quality assessment
CN106769089A (en) * 2016-12-19 2017-05-31 中国航空工业集团公司沈阳飞机设计研究所 Unmanned plane during flying performance evaluation and the flight quality integrated method for real-time monitoring of assessment
CN106774411A (en) * 2016-12-31 2017-05-31 清华大学深圳研究生院 Unmanned plane middleware system based on PHM
CN106774411B (en) * 2016-12-31 2020-03-03 清华大学深圳研究生院 Unmanned aerial vehicle middleware system based on PHM
CN106971432A (en) * 2017-04-11 2017-07-21 中国人民解放军海军航空工程学院青岛校区 A kind of airplane data management record system and data presentation technique
WO2018214387A1 (en) * 2017-05-23 2018-11-29 深圳大学 Distributed mining system and method for aviation-oriented electronic data
CN107590878A (en) * 2017-09-13 2018-01-16 中国人民解放军火箭军工程大学 A kind of unmanned plane during flying safe prediction apparatus for evaluating and method
CN107516451A (en) * 2017-10-08 2017-12-26 景遥（上海）信息技术有限公司 Fixed-wing UAV Intelligent flight training system
CN108259223A (en) * 2017-12-07 2018-07-06 中国航空工业集团公司西安航空计算技术研究所 The unmanned plane network system security Situation Awareness appraisal procedure for preventing GPS from cheating
CN108259223B (en) * 2017-12-07 2021-03-26 中国航空工业集团公司西安航空计算技术研究所 Unmanned aerial vehicle network system security situation perception evaluation method for preventing GPS deception
CN110568855A (en) * 2018-06-06 2019-12-13 上海资誉电子科技有限公司 Unmanned aerial vehicle visual flight management system and method based on big data calculation engine
CN110222375A (en) * 2019-05-13 2019-09-10 北京航空航天大学 A kind of safety monitoring method of carrier landing process
CN110321951A (en) * 2019-07-01 2019-10-11 青岛海科虚拟现实研究院 A kind of VR simulated flight device evaluation of training method
CN110321951B (en) * 2019-07-01 2021-03-16 青岛海科虚拟现实研究院 VR simulated aircraft training evaluation method
CN113449238B (en) * 2020-03-24 2023-04-25 丰翼科技(深圳)有限公司 Unmanned aerial vehicle safe operation method and device, electronic equipment and storage medium
CN113449238A (en) * 2020-03-24 2021-09-28 丰翼科技(深圳)有限公司 Unmanned aerial vehicle safe operation method and device, electronic equipment and storage medium
CN111554286A (en) * 2020-04-26 2020-08-18 云知声智能科技股份有限公司 Method and equipment for controlling unmanned aerial vehicle based on voice
CN111582740B (en) * 2020-05-13 2023-05-23 电子科技大学 Multi-rotor unmanned aerial vehicle risk assessment system
CN111935811A (en) * 2020-06-28 2020-11-13 北京遥测技术研究所 Airborne swarm terminal adaptive power control method based on temperature sensor
CN111862550B (en) * 2020-08-13 2021-11-30 深圳市高巨创新科技开发有限公司 Formation unmanned aerial vehicle group departure alarm method and system
CN111862550A (en) * 2020-08-13 2020-10-30 深圳市高巨创新科技开发有限公司 Formation unmanned aerial vehicle group departure alarm method and system
CN111968267A (en) * 2020-08-28 2020-11-20 珠海欧比特宇航科技股份有限公司 Airborne flight safety real-time monitoring and intelligent early warning device
CN114545325A (en) * 2020-11-25 2022-05-27 北京国网信通埃森哲信息技术有限公司 Unmanned aerial vehicle intelligent inspection method based on state information prediction under transformer substation
CN113759968A (en) * 2021-09-01 2021-12-07 中国南方电网有限责任公司超高压输电公司贵阳局 Unmanned aerial vehicle-based power grid line patrol planning method and system
CN114373238A (en) * 2021-12-06 2022-04-19 特金智能科技(上海)有限公司 Attendance checking method and device for unmanned aerial vehicle inspection flight, electronic equipment and storage medium
CN114201925A (en) * 2022-02-17 2022-03-18 佛山科学技术学院 Unmanned aerial vehicle cluster cooperative task planning method, electronic equipment and readable storage medium
CN114689315A (en) * 2022-03-18 2022-07-01 山东超晟光电科技有限公司 Unmanned aerial vehicle belt line patrol accurate positioning method based on audio
CN115164884A (en) * 2022-07-19 2022-10-11 中国民航大学 Unmanned aerial vehicle flight state on-line monitoring system
CN115164884B (en) * 2022-07-19 2024-05-03 中国民航大学 Unmanned aerial vehicle flight state on-line monitoring system
CN116384695A (en) * 2023-04-11 2023-07-04 中国人民解放军陆军工程大学 Unmanned aerial vehicle operation monitoring method and system based on independent overruling and combined overruling
CN116523384A (en) * 2023-04-11 2023-08-01 中国人民解放军陆军工程大学 Unmanned aerial vehicle efficiency determining method and system based on independent overruling and combined overruling
CN116384695B (en) * 2023-04-11 2024-01-26 中国人民解放军陆军工程大学 Unmanned aerial vehicle operation monitoring method and system based on independent overruling and combined overruling
CN116523384B (en) * 2023-04-11 2024-05-14 中国人民解放军陆军工程大学 Unmanned aerial vehicle efficiency determining method and system based on independent overruling and combined overruling
CN118466368A (en) * 2024-07-09 2024-08-09 克拉玛依市远山石油科技有限公司 Working state remote monitoring system and method for unmanned aerial vehicle aeromagnetic equipment
CN118466368B (en) * 2024-07-09 2024-10-18 克拉玛依市远山石油科技有限公司 Working state remote monitoring system and method for unmanned aerial vehicle aeromagnetic equipment

Also Published As

Publication number Publication date
CN105427674B (en) 2017-12-12

Similar Documents

Publication Publication Date Title
CN105427674A (en) System and method for real-time unmanned plane flight state evaluation early warning
CN108011948B (en) Industrial equipment integration monitored control system based on edge calculation
Kong et al. RETRACTED ARTICLE: Intelligent manufacturing model of construction industry based on Internet of Things technology
CN108279003A (en) It is a kind of based on the unmanned plane high accuracy positioning cruising inspection system used suitable for substation
CN109490713A (en) A kind of method and system moving inspection and interactive diagnosis for cable run
CN102024344A (en) Civil airport surface monitoring system
CN102923538A (en) Elevator health management and maintenance system based on Internet of things and collection and assessment method
CN107194565B (en) Power grid scheduling optimization method and system based on cloud decision
CN201984330U (en) Intelligent building control device based on Internet of things
CN103560590B (en) Electric network intelligent scheduling framework and its implementation
CN213661639U (en) Full-frequency-band detection and counter-control automatic management and control system of unmanned aerial vehicle
CN104238522A (en) Substation equipment fault locating system based on GPS
CN109067871B (en) Electric power ubiquitous intelligent cloud architecture
CN212785420U (en) Unmanned aerial vehicle system of patrolling and examining based on cloud limit is in coordination with 5G network
CN103024070A (en) Intelligent mine remote monitoring cloud system
CN203027297U (en) Intelligent remote monitoring system for mine based on Wi-FI (wireless fidelity) wireless sensor network
CN112821456B (en) Distributed source-storage-load matching method and device based on transfer learning
CN205485629U (en) Unmanned vehicles identification system
Yue et al. A Channel Knowledge Map-Aided Personalized Resource Allocation Strategy in Air-Ground Integrated Mobility
Knoblock et al. Intelligent spectrum management for future aeronautical communications
Wang et al. Multi-UAV route planning for data collection from heterogeneous IoT devices
CN203135932U (en) IoT monitoring management system
CN113316085B (en) Method, device and system for giving alarm for detention in closed space
KR20240001975A (en) Systems for predicting and monitoring solar power generation using artificial intelligence
Liu et al. Flying path optimization of UAV for wireless power transfer systems: A spectral-clustering-enabled approach

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CP01 Change in the name or title of a patent holder

Address after: Wang Yue Central Road Ji'nan City, Shandong province 250002 City No. 2000

Co-patentee after: National Network Intelligent Technology Co., Ltd.

Patentee after: Electric Power Research Institute of State Grid Shandong Electric Power Company

Co-patentee after: State Grid Corporation of China

Address before: Wang Yue Central Road Ji'nan City, Shandong province 250002 City No. 2000

Co-patentee before: Shandong Luneng Intelligent Technology Co., Ltd.

Patentee before: Electric Power Research Institute of State Grid Shandong Electric Power Company

Co-patentee before: State Grid Corporation of China

CP01 Change in the name or title of a patent holder
TR01 Transfer of patent right

Effective date of registration: 20201029

Address after: 250101 Electric Power Intelligent Robot Production Project 101 in Jinan City, Shandong Province, South of Feiyue Avenue and East of No. 26 Road (ICT Industrial Park)

Patentee after: National Network Intelligent Technology Co.,Ltd.

Address before: Wang Yue Central Road Ji'nan City, Shandong province 250002 City No. 2000

Patentee before: ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER Co.

Patentee before: National Network Intelligent Technology Co.,Ltd.

Patentee before: STATE GRID CORPORATION OF CHINA

TR01 Transfer of patent right