CN114771866A - Dynamic event triggered long-endurance unmanned aerial vehicle fault detection method - Google Patents

Dynamic event triggered long-endurance unmanned aerial vehicle fault detection method Download PDF

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CN114771866A
CN114771866A CN202210378714.0A CN202210378714A CN114771866A CN 114771866 A CN114771866 A CN 114771866A CN 202210378714 A CN202210378714 A CN 202210378714A CN 114771866 A CN114771866 A CN 114771866A
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unmanned aerial
aerial vehicle
dynamic event
fault detection
event trigger
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盖文东
李珊珊
钟麦英
张婧
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Shandong University of Science and Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64F—GROUND OR AIRCRAFT-CARRIER-DECK INSTALLATIONS SPECIALLY ADAPTED FOR USE IN CONNECTION WITH AIRCRAFT; DESIGNING, MANUFACTURING, ASSEMBLING, CLEANING, MAINTAINING OR REPAIRING AIRCRAFT, NOT OTHERWISE PROVIDED FOR; HANDLING, TRANSPORTING, TESTING OR INSPECTING AIRCRAFT COMPONENTS, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention discloses a dynamic event triggered long-endurance unmanned aerial vehicle fault detection method, and belongs to the technical field of unmanned aerial vehicle fault detection. The method considers the unmanned aerial vehicle system with wind interference and faults, adopts a dynamic event trigger mechanism to reduce the occupation of unmanned aerial vehicle communication resources in a network environment, can realize the complete decoupling of residual errors and dynamic event trigger transmission errors, eliminates errors generated by event trigger, and can calculate the optimal solution on line by a designed fault filter.

Description

Dynamic event triggered long-endurance unmanned aerial vehicle fault detection method
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle fault detection, and particularly relates to a dynamic event-triggered long-endurance unmanned aerial vehicle fault detection method.
Background
The unmanned aerial vehicle with the unique advantages is increasingly widely applied to the fields of military affairs, transportation, wireless communication and the like. Along with the wide application of unmanned aerial vehicles, it is especially important to ensure safety and reliability of unmanned aerial vehicle flight control system, and quick fault detection is the important prerequisite of guaranteeing unmanned aerial vehicle system safety, reducing economic loss.
For a long-endurance flying unmanned aerial vehicle, data interaction with a ground station through a communication network is often required so as to implement a fault detection algorithm in a computer of the ground station, which is a typical networked control system. The communication network is needed to realize data transmission between the ground station and the unmanned aerial vehicle, and limited network resources must be wasted through continuous communication.
In the event-triggered fault detection process, errors exist between data at non-triggering time and actual system data, namely event transmission errors, and the performance of fault detection is bound to be influenced. Under a dynamic event trigger mechanism, it is important to avoid the influence of event trigger transmission errors on residual signals of a fault filter.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides the fault detection method of the long-endurance unmanned aerial vehicle triggered by the dynamic event, which is reasonable in design, overcomes the defects of the prior art and has a good effect.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for detecting faults of a long-endurance unmanned aerial vehicle triggered by dynamic events comprises the following steps:
step 1: establishing an unmanned aerial vehicle non-uniform sampling period model under a dynamic event trigger mechanism;
the model of the unmanned aerial vehicle nonlinear attitude discrete system considering the faults of the actuating mechanism is as follows:
Figure BDA0003591350800000011
where k denotes a discrete system sampling time, and x ═ ωx,ωy,ωz]T，y＝[γ,ψ,θ]T，u＝[δx,δy,δz]T，d(k)＝[wxg,wyg,wzg]，ωx，ωy，ωzRespectively the rolling angular velocity, the yaw angular velocity and the pitch angular velocity of the unmanned aerial vehicle in a body coordinate system; gamma, psi and theta are respectively a roll angle, a yaw angle and a pitch angle of the airplane; delta. for the preparation of a coatingxFor aileron deflection angle, deltayIs a directionRudder angle of deflection δzIs the elevator deflection angle; w is axg，wyg，wzgThe gradient of wind disturbance along the axis of the body;
uf＝l1u+l2u is the control input, ufFor actual input, /)1Is a diagonal matrix,/2Indicating control plane deviation fault, f, gu，gdAs a non-linear function, C and DdA matrix of corresponding dimensions;
for discrete drone nonlinear systems, in time series k0，k1，...，ki，...]The dynamic event trigger is used for determining whether to transmit the sampled unmanned aerial vehicle control input u (k) and the measurement output y (k) to the ground station through the wireless network and storing a recently transmitted data packet; the ground station uses the data to complete fault detection, and a sampling time sequence is triggered by a dynamic event triggering mechanism (2) with the following formula:
Figure BDA0003591350800000021
in the formula, kiFor the most recent event trigger time, Ω ∈ Rq×qFor dynamic event-triggered weighting matrices, σ>0 is the threshold value of the event trigger,
Figure BDA0003591350800000022
is a parameter;
η (k) is a positive internal dynamic variable satisfying the following differential equation:
Figure BDA0003591350800000023
where φ is local Lipchitz continuous κ∞Function, η0Selecting R as a parameter to be designed;
y(ki) For the most recently transmitted measurement output, y (k) is the current sample data; if formula (2) holds, y (k) is y (k)i+1) And transmitting to the fault detection module; transmission input of fault detection moduleData y (k)i) Updating through a dynamic event trigger condition (2);
the nonlinear attitude system model (1) of the unmanned aerial vehicle is arranged
Figure BDA0003591350800000024
Where a taylor series expansion is performed and its higher order terms are omitted, the model (1) is written as:
Figure BDA0003591350800000025
wherein the content of the first and second substances,
Figure BDA0003591350800000026
according to the unmanned aerial vehicle linear attitude system model (4), at the event triggering moment ki+1Is expressed as the following equation (5):
Figure BDA0003591350800000027
restated equation (5) as:
Figure BDA0003591350800000028
in the formula (I), the compound is shown in the specification,
Figure BDA0003591350800000029
Figure BDA0003591350800000031
υ(ki)＝[υT(ki) υT(ki+1) ... υT(ki+1-1)]Tv represents w, ufAnd (d) a second step of,D d(k)＝[D d 0 ... 0]T；
and 2, step: design dynamic event trigger Hi/H∞A fault detection filter;
designing an eventTrigger time ki+1Is shown in equation (7):
Figure BDA0003591350800000032
in the formula (I), the compound is shown in the specification,u(ki)＝[uT(ki) uT(ki) ... uT(ki)]T，
Figure BDA0003591350800000033
is in a state x (k)i) Is determined by the estimated vector of (a),
Figure BDA0003591350800000034
is output y (k)i) Is estimated as the vector r (k)i) To generate a residual signal, L (k)i)∈Rn×qIs an observer gain matrix, W (k)i)∈Rq×qDynamic event triggered y (k) for post-filter weighting matrixi) Driving the residual generator to work;
an observer gain matrix L (k) is designed according to the following formulai) And a post-filter weighting matrix W (k)i)：
Figure BDA0003591350800000035
Figure BDA0003591350800000036
Figure BDA0003591350800000037
Figure BDA0003591350800000038
And step 3: for the residual r (k) generated in the previous stepi) Processing the data to obtainA fault detection result;
defining a residual evaluation function as in equation (12):
Figure BDA0003591350800000039
in the formula, kiTriggering time for the current event, wherein N is a moving time window;
determining a residual threshold value according to equation (13):
Figure BDA00035913508000000310
where i is 1,2, a, M, and M is the number of times of triggering of the dynamic event trigger condition (2), and is calculated according to the following formula
Figure BDA00035913508000000311
Mean and mean square error of (d):
Figure BDA0003591350800000041
threshold JthDetermined according to equation (14):
Figure BDA0003591350800000042
according to the formulas (12) and (14), the residual evaluation logic is expressed as the following formula (15):
Figure BDA0003591350800000043
when residual evaluation function JN(r(ki) Greater than a threshold J)thJudging that the unmanned aerial vehicle breaks down during flying and giving a signal indication; when residual evaluation function JN(r(ki) ) is less than or equal to the threshold value JthAnd judging the normal flight of the unmanned aerial vehicle.
The invention has the following beneficial technical effects:
the method considers the unmanned aerial vehicle system with wind interference and faults, adopts a dynamic event trigger mechanism to reduce the occupation of unmanned aerial vehicle communication resources in a network environment, can realize the complete decoupling of residual errors and dynamic event trigger transmission errors, eliminates errors generated by event trigger, and can calculate the optimal solution on line by a designed fault filter.
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FIG. 1 is a schematic structural diagram of a method for detecting faults of an unmanned aerial vehicle triggered by dynamic events;
FIG. 2 is a flow chart of a method for dynamic event triggered unmanned aerial vehicle fault detection;
fig. 3 is a sequence diagram of dynamic event triggering of the drone system in an example of the present invention;
fig. 4 is a diagram of unmanned aerial vehicle system fault detection in an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following figures and embodiments:
as shown in fig. 1, a method for detecting a fault of a long-endurance unmanned aerial vehicle triggered by a dynamic event, which uses a controller, an actuator, an unmanned aerial vehicle, a sensor, a dynamic event triggering module, a wireless communication network and a fault detection module; the controller, the actuator, the unmanned aerial vehicle, the sensor and the dynamic event trigger module are sequentially connected through a line; the dynamic event trigger module is connected with the fault detection module through a wireless communication network; the controller, the actuator, the unmanned aerial vehicle and the sensor form a closed loop; one end of the dynamic event trigger module is connected to the common end of the controller and the actuator; the fault detection module is arranged at the ground station;
and a control input signal u (k) output by the controller and a measurement output signal y (k) output by the sensor are screened by the dynamic event triggering module and then transmitted to a fault detection module of the ground station through a wireless communication network.
The dynamic event trigger mechanism determines whether to transmit the sampled control inputs and measurement outputs to the ground station and stores the most recently transmitted data packets. Ground stationFault detection is accomplished using this data. Because the construction of the dynamic event trigger fault detection filter needs related information of system control input, the control input u (k) and the measurement output y (k) of the unmanned aerial vehicle attitude control system are packaged and transmitted to the ground station fault detection module through the dynamic event trigger module. The dynamic event triggered fault detection method proposed herein utilizes the available u (k)i) And y (k)i) And constructing a residual generator and a residual evaluation function, and eliminating the influence of event-triggered transmission errors on residual signals, wherein the process is shown in fig. 2.
Step 1: establishing an unmanned aerial vehicle non-uniform sampling period model under a dynamic event trigger mechanism;
the unmanned aerial vehicle nonlinear attitude discrete system model considering the faults of the actuating mechanism is as follows:
Figure BDA0003591350800000051
where k denotes a discrete system sampling time, and x ═ ωx,ωy,ωz]T，y＝[γ,ψ,θ]T，u＝[δx,δy,δz]T，d(k)＝[wxg,wyg,wzg]，ωx，ωy，ωzRespectively the rolling angular velocity, the yaw angular velocity and the pitch angular velocity of the unmanned aerial vehicle in a body coordinate system; gamma, psi and theta are respectively a roll angle, a yaw angle and a pitch angle of the airplane; deltaxFor aileron deflection angle, deltayIs rudder deflection angle, deltazIs the elevator deflection angle; w is axg，wyg，wzgIs the gradient of wind disturbances along the axis of the body.
uf＝l1u+l2U is a control input, ufFor actual input,/1Is a diagonal matrix,/1If I indicates no multiplicative fault occurs, if l1When the corresponding diagonal element is between (0, 1), the control efficiency loss fault of the corresponding control surface is represented as multiplicative fault, I2And indicating the control surface deviation fault as an additive fault.
f，gu，gdAs a non-linear function, C and DdIs a matrix of corresponding dimensions.
The sensors pass the drone output y (k) to a dynamic event trigger. For a discrete drone system, in time series k0，k1，...，ki，...]The dynamic event trigger is used for determining whether to transmit the sampled unmanned aerial vehicle control input u (k) and the measurement output y (k) to the ground station through the wireless network and storing a recently transmitted data packet; the ground station uses the data to complete fault detection, and the sampling time sequence is triggered by the following formula dynamic event triggering mechanism (2):
Figure BDA0003591350800000052
in the formula, kiFor the most recent event trigger time, Ω ∈ Rq×qFor dynamic event-triggered weighting matrices, σ>0 is the threshold value of the event trigger,
Figure BDA0003591350800000053
is a parameter;
η (k) is a positive internal dynamic variable satisfying the following differential equation:
Figure BDA0003591350800000054
wherein phi is local Lipchitz continuous kappa∞Function, η0Selecting R as a parameter to be designed;
y(ki) For the most recently transmitted measurement output, y (k) is the current sample data; if formula (2) holds, y (k) is y (k)i+1) And transmitting to the fault detection module; transmission input data y (k) of fault detection modulei) Updating through a dynamic event trigger condition (2);
partial data can be lost under the trigger of dynamic event, so that the non-trigger time data of fault detection module and actual system data are different, and said data differenceHeterodyning as event transmission error ey(k)：
Figure BDA0003591350800000061
The nonlinear attitude system model (1) of the unmanned aerial vehicle is arranged
Figure BDA0003591350800000062
Performs taylor series expansion and omits its high-order terms:
Figure BDA0003591350800000063
Figure BDA0003591350800000064
Figure BDA0003591350800000065
order to
Figure BDA0003591350800000066
Then equation (1) is restated as:
Figure BDA0003591350800000067
according to the linear attitude system model (4) of the unmanned aerial vehicle, at the event triggering moment ki+1Is expressed as the following formula (5):
Figure BDA0003591350800000068
restated equation (5) as:
Figure BDA0003591350800000069
in the formula (I), the compound is shown in the specification,
Figure BDA00035913508000000610
Figure BDA00035913508000000611
υ(ki)＝[υT(ki) υT(ki+1) ... υT(ki+1-1)]Tv represents w, ufAnd (d) a second step of,D d(k)＝[D d 0 ... 0]T；
equation (6) represents the unmanned aerial vehicle fault model at the dynamic event trigger time [ k ]i，ki+1) The system model of (1).
Step 2: design dynamic event trigger Hi/H∞A fault detection filter;
design event trigger time ki+1The unmanned aerial vehicle system state estimation is shown in formula (7):
Figure BDA0003591350800000071
in the formula (I), the compound is shown in the specification,u(ki)＝[uT(ki) uT(ki) ... uT(ki)]T，
Figure BDA0003591350800000072
is in a state x (k)i) The estimated vector of (a) is calculated,
Figure BDA0003591350800000073
is output y (k)i) Is estimated as the vector r (k)i) To generate a residual signal, L (k)i)∈Rn×qIs an observer gain matrix, W (k)i)∈Rq×qDynamic event triggered y (k) for post-filter weighting matrixi) Driving the residual generator to work;
defining an estimated error vector
Figure BDA0003591350800000074
The state estimation error equation obtained by subtracting equation (6) from equation (7) is as follows:
Figure BDA0003591350800000075
in the formula (I), the compound is shown in the specification,F LC(ki)＝F(ki)-L(ki)C，
Figure BDA0003591350800000076
it follows that the fault detection filter implements the residual r (k)i) Transmission error e triggered by dynamic eventy(k) Is completely decoupled.
For i ∈ N, the key parameter L (k) of the fault detection filteri) And W (k)i) Respectively calculated by the following formulas:
Figure BDA0003591350800000077
Figure BDA0003591350800000078
in the formula, P (k)i)>0, obtained by recursive calculation of a formula Riccati equation;
Figure BDA0003591350800000079
based on unmanned aerial vehicle system equation (7) andF(ki)，G d(ki) The key parameter observer gain matrix L (k) of the fault detection filter is designed according to the following formulai) And a postfilter weighting matrix W (k)i)：
Figure BDA00035913508000000710
Figure BDA00035913508000000711
Figure BDA00035913508000000712
Figure BDA00035913508000000713
And 3, step 3: for the residual r (k) generated in the previous stepi) Carrying out data processing to obtain a fault detection result;
defining a residual evaluation function as in equation (12):
Figure BDA0003591350800000081
in the formula, kiTriggering time for the current event, wherein N is a moving time window;
determining a residual threshold according to equation (13):
Figure BDA0003591350800000082
where i is 1,2, a, M, and M is the number of times of triggering of the dynamic event trigger condition (2), and is calculated according to the following formula
Figure BDA0003591350800000083
Mean and mean square error of (d):
Figure BDA0003591350800000084
threshold JthDetermined according to equation (14):
Figure BDA0003591350800000085
according to the formulas (12) and (14), the residual evaluation logic is expressed as the following formula (15):
Figure BDA0003591350800000086
when residual evaluation function JN(r(ki) Is greater than a threshold value JthJudging that the unmanned aerial vehicle breaks down during flying and giving a signal indication; when residual evaluation function JN(r(ki) ) is equal to or less than a threshold value JthAnd judging that the unmanned aerial vehicle normally flies.
Wherein step 1, step 2 and step 3 are all accomplished at the ground station in this unmanned aerial vehicle fault detection device.
In step 1, a ground station constructs a non-uniform sampling model of the unmanned aerial vehicle by using information received by a wireless communication network;
on the basis, the step 2 and the step 3 complete the detection work of the fault in the fault detection module.
The process is easy to obtain, and the designed dynamic event trigger fault detection method can achieve better detection performance in a network-controlled long-endurance unmanned aerial vehicle system.
The method for detecting the fault of the long-endurance unmanned aerial vehicle triggered by the dynamic event provided by the invention is explained by combining experiments, and the effectiveness of the method provided by the invention is verified.
During the experiment: and taking the experimental step length as 90, and transmitting the control input and the measurement output of the unmanned aerial vehicle to a fault detection module of the ground station through a dynamic event trigger mechanism in Matlab software simulation.
By using the fault detection method provided by the invention, a dynamic event trigger sequence and a residual error evaluation function are generated by using Matlab software. Fig. 3 to 4 show the fault situation where a loss of control effectiveness of 10% occurs when the unmanned aerial vehicle elevator k is 60, and the fault detection method generates a dynamic event trigger sequence and a residual evaluation function.
Fig. 3 gives a dynamic event triggering time sequence for the drone system. Wherein, the stem and leaf graph containing the circle represents the triggering time, and the height of the stem and leaf represents the sampling period interval of two adjacent triggers. As can be seen from fig. 3, compared to the equal period sampling, the dynamic event triggering mechanism can effectively reduce the communication load, and can reduce the data transmission by about 42.2%.
Fig. 4 shows the residual evaluation function in the case of the fault, where a 10% loss of control effectiveness occurs when k is 60 elevators, and the dynamic event-triggered fault detection method can effectively detect the occurrence of the fault when k is 62.
In conclusion, the invention transmits the system information of the unmanned aerial vehicle through the dynamic event trigger mechanism, completes fault detection, not only can effectively reduce the occupation of network communication resources, but also can realize better fault detection effect.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make various changes, modifications, additions and substitutions within the spirit and scope of the present invention.

Claims (1)

1. A method for detecting faults of a long-endurance unmanned aerial vehicle triggered by dynamic events is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing an unmanned aerial vehicle non-uniform sampling period model under a dynamic event trigger mechanism;
the model of the unmanned aerial vehicle nonlinear attitude discrete system considering the faults of the actuating mechanism is as follows:
Figure FDA0003591350790000011
where k denotes a discrete system sampling time, and x ═ ωx,ωy,ωz]T，y＝[γ,ψ,θ]T，u＝[δx,δy,δz]T，d(k)＝[wxg,wyg,wzg]，ωx，ωy，ωzRespectively the rolling angular velocity, the yaw angular velocity and the pitch angular velocity of the unmanned aerial vehicle in a body coordinate system; gamma, psi and theta are respectively a rolling angle, a yaw angle and a pitch angle of the airplane; deltaxFor aileron deflection angle, deltayIs rudder deflection angle, deltazIs the elevator deflection angle; w is axg，wyg，wzgThe gradient of wind disturbance along the axis of the body;
uf＝l1u+l2u is a control input, ufFor actual input, /)1Is a diagonal matrix,/2Indicating control plane deviation fault, f, gu，gdAs a non-linear function, C and DdA matrix of corresponding dimensions;
for discrete drone nonlinear systems, in time series k0，k1，...，ki，…]The dynamic event trigger is used for determining whether to transmit the sampled unmanned aerial vehicle control input u (k) and the measurement output y (k) to the ground station through the wireless network and storing a recently transmitted data packet; the ground station uses the data to complete fault detection, and the sampling time sequence is triggered by the following formula dynamic event triggering mechanism (2):
Figure FDA0003591350790000012
in the formula, kiFor the most recent event trigger time, Ω ∈ Rq×qFor dynamic event-triggered weighting matrices, σ>0 is the threshold value of the event trigger,
Figure FDA0003591350790000013
is a parameter;
η (k) is a positive internal dynamic variable satisfying the following differential equation:
Figure FDA0003591350790000014
wherein phi is local Lipchitz continuous kappa∞Function η0E, taking R as a parameter to be designed;
y(ki) For the most recently transmitted measurement output, y (k) is the current sample data; if formula (2) holds, y (k) is y (k)i+1) And transmitting to the fault detection module; transmission input data y (k) of fault detection modulei) Updating through a dynamic event trigger condition (2);
the nonlinear attitude system model (1) of the unmanned aerial vehicle is arranged
Figure FDA0003591350790000015
Where a taylor series expansion is performed and its higher order terms are omitted, the model (1) is written as:
Figure FDA0003591350790000021
wherein the content of the first and second substances,
Figure FDA0003591350790000022
according to the linear attitude system model (4) of the unmanned aerial vehicle, at the event triggering moment ki+1Is expressed as the following formula (5):
Figure FDA0003591350790000023
restated equation (5) as:
Figure FDA0003591350790000024
in the formula (I), the compound is shown in the specification,
Figure FDA0003591350790000025
Figure FDA0003591350790000026
υ(ki)＝[υT(ki)υT(ki+1)…υT(ki+1-1)]Tv represents w, ufAnd (d) a second step of,D d(k)＝[Dd 0 … 0]T；
step 2: design dynamic event trigger Hi/H∞A fault detection filter;
design event trigger time ki+1Is shown in equation (7):
Figure FDA0003591350790000027
in the formula (I), the compound is shown in the specification,u(ki)＝[uT(ki) uT(ki) … uT(ki)]T，
Figure FDA0003591350790000028
is in a state x (k)i) The estimated vector of (a) is calculated,
Figure FDA0003591350790000029
is output y (k)i) Estimated vector of r (k)i) To generate a residual signal, L (k)i)∈Rn×qIs an observer gain matrix, W (k)i)∈Rq×qY (k) of dynamic event trigger for post-filter weighting matrixi) Driving the residual generator to work;
an observer gain matrix L (k) is designed according to the following formulai) And a postfilter weighting matrix W (k)i)：
Figure FDA00035913507900000210
Figure FDA00035913507900000211
Figure FDA0003591350790000031
Figure FDA0003591350790000032
And 3, step 3: for the residual r (k) generated in the previous stepi) Performing data processing to obtain a fault detection result;
defining a residual evaluation function as formula (12):
Figure FDA0003591350790000033
in the formula, kiTriggering time for the current event, wherein N is a moving time window;
determining a residual threshold value according to equation (13):
Figure FDA0003591350790000034
where i is 1,2, a, M, and M is the number of times of triggering of the dynamic event trigger condition (2), and is calculated according to the following formula
Figure FDA0003591350790000038
Mean and mean square error of (d):
Figure FDA0003591350790000035
threshold JthDetermined according to equation (14):
Figure FDA0003591350790000036
according to the formulas (12) and (14), the residual evaluation logic is expressed as the following formula (15):
Figure FDA0003591350790000037
when residual evaluation function JN(r(ki) Greater than a threshold J)thJudging that the unmanned aerial vehicle breaks down during flying and giving a signal indication; when residual evaluation function JN(r(ki) ) is less than or equal to the threshold value JthAnd judging that the unmanned aerial vehicle normally flies.
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CN116527060A (en) * 2023-05-29 2023-08-01 北京理工大学 Information compression and anomaly detection method based on event trigger sampling

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115629547A (en) * 2022-12-08 2023-01-20 西北工业大学 Airplane airborne fault-tolerant control method and system for control plane fault
CN116527060A (en) * 2023-05-29 2023-08-01 北京理工大学 Information compression and anomaly detection method based on event trigger sampling
CN116527060B (en) * 2023-05-29 2024-01-05 北京理工大学 Information compression and anomaly detection method based on event trigger sampling

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