CN116527060A - Information compression and anomaly detection method based on event trigger sampling - Google Patents
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Abstract
The invention discloses an information compression and anomaly detection method based on event trigger sampling, which comprises the following steps: s1, determining a triggering condition and a threshold value of a system data sampling event, and completing data sampling of a measurement variable; s2, sampling a measurement variable according to event triggering, and designing event triggering Kalman filter to reconstruct data; and S3, performing fault detection by using residual analysis data. By adopting the information compression and anomaly detection method based on event trigger sampling, the invention can reconstruct the sampled data on the basis of reducing the transmission pressure of a transmission channel, and perform fault detection of the system according to the reconstructed data, thereby ensuring the accuracy and safety of the subsequent system performance analysis.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an information compression and anomaly detection method based on event trigger sampling.
Background
At present, the data acquisition and monitoring in the field of industrial process control are mostly realized through periodic sampling, the periodic sampling samples signals at fixed time intervals, and as each sampling node is irrelevant to the current working state, the serious problem of communication resource waste exists. For example, a signal monitoring system of a satellite, a monitoring system of a power grid, a monitoring station of an earthquake and an environment quality signal acquisition system can generate a large amount of redundant data in long-term operation, so that communication resources of the system are wasted.
Overall, time-triggered sampling control systems are too conservative for sampling frequency. Specifically, since the update frequency of the measured variable is fixed, a relatively conservative, i.e., relatively small, sampling period is generally selected in order to compromise the worst possible estimation results. The disadvantage of this sampling is that: even when the estimator has obtained the ideal estimation accuracy, and no new measurement updates need to be sampled anymore, the measurement variables will still be updated at a faster frequency. Meanwhile, when the period sampling period is smaller, excessive transmission signals can cause the increase of data volume and the occupation of resources of a data transmission channel, and the requirements on the capacity of a memory and the bandwidth of data transmission are higher. For example, when the unmanned vehicle works in a ring shape on a complex terrain, monitoring quantities such as the residual electric quantity and the temperature of the unmanned vehicle do not need to be transmitted to a remote control system at any time, and as long as the electric quantity or the temperature of the unmanned vehicle changes within a normal range, the unmanned vehicle does not need to be transmitted at any time, so that the energy consumption of the unmanned vehicle can be saved, and the unmanned vehicle can work for a longer time.
Disclosure of Invention
Aiming at the problem of large data transmission of a process control system running for a long time, the invention provides an information compression and anomaly detection method of event trigger sampling, which realizes compression sampling of a large amount of sensor data and data recovery of an event trigger state estimator, reduces the number of times of sampling transmission update as far as possible under the premise of not obviously reducing the data validity, and realizes reconstruction of data signals through the event trigger state estimator, reduces the channel load and the energy consumed by signal transmission and can perform fault detection through the reconstructed data.
In order to achieve the above object, the present invention provides an information compression and anomaly detection method based on event triggered sampling, comprising the steps of:
s1, determining a triggering condition and a threshold value of a system data sampling event, and completing data sampling of a measurement variable;
s2, sampling a measurement variable according to event triggering, and designing event triggering Kalman filter to reconstruct data;
and S3, performing fault detection by using residual analysis data.
Preferably, the system data in step S1 includes a control signal and a measurement/control signal;
after the measurement/controlled signal is acquired by the sensor, whether transmission is performed or not is judged through the event triggering condition of the communication channel, and the event triggering condition of the communication channel is as follows:
wherein, gamma k A trigger variable of 0 or 1, which represents event trigger information of the kth sample; y is k Is the kth step measured variable measured by the sensor; y is k-1 Is the k-1 step measured variable measured by the sensor;
at this time, at the current measurement value y of the sensor k And the last transmitted measurement y k-1 The difference value exceeds the threshold delta, the condition is triggered, and the sensor samples and transmits the system monitoring data.
Preferably, the step of obtaining the threshold δ is as follows:
first sampling to obtain average communication rate
Wherein N is the length of the time series;
then, the estimated error is obtained by the event-triggered state estimator
In the method, in the process of the invention,representing a system measurement variable y acquired via an event triggered state estimator k Is a function of the estimated value of (2);
finally, changing the threshold value delta to obtain the average communication rate under different threshold values deltaError of estimation->Plotting average communication rate +.>Error of estimation->And (3) selecting an event triggering condition threshold delta from the balance curve between the two points or from the closest point of the balance curve.
Preferably, the step S2 specifically includes the following steps:
s21, establishing a state space equation of the system;
s22, initializing a variable of the system;
s23, reconstructing a system measured value after performing state estimation by using an event-triggered Kalman filter.
Preferably, in step S21, control signals of the system are definedAs u= [ u ] 1 ,u 2 ,...,u n ]∈R n ，R n Is a real matrix of n dimensions; the measurement/controlled signal is defined as y= [ y ] 1 ,y 2 ,...,y m ]∈R m ，R m Is a real matrix of m dimension; and consider that the system measurement variable becomes discrete signal after sampling, combine with control signal u k And a measurement variable y obtained by sampling the measurement/controlled signal k Establishing a discrete linear time invariant system of the system:
wherein A is a system matrix, B is a control matrix, C is an observation matrix, and D is a direct transfer matrix; x is x k ∈R n Is a status signal/variable; u (u) 1 ,u 2 ,...,u n Is a deterministic input control signal that is known to the estimator; w (w) k And v k Respectively for representing process noise and measurement noise.
Preferably, the step S22 specifically includes the following steps:
s221, setting initial state variable x 0 ＝[a 1 ,a 2 ,…,a n ] T Process noiseMeasurement noise->Wherein a is 1 ,a 2 ,…,a n ,b 1 ,c 1 Are all non-negative constants;
s222, establishing an event trigger state estimator according to the established discrete linear time invariant system.
Preferably, the step S222 specifically includes the following steps:
s2221, initializing a state covariance matrix Prior_Sigma estimated in Prior and a state vector Prior_xhat estimated in Prior according to the normal working and running states of the system; and making a posterior estimateState vector master_xhat=x 0 State covariance matrix of posterior estimation master_sigma=w k ；
S2222, predicting prior estimated variables according to a system state space equation, a posterior estimated state vector Poster_xhat and a state covariance matrix Poster_xhat as follows:
Prior_xhat(:,k+1)＝A*Poster_xhat(:,k)+B*u k (5)
Prior_Sigma(:,k+1)＝A*Poster_Sigma(:,k)*A T +sigmw (6)
where Prior_xhat (: k+1) represents the a priori estimated state vector of the k+1 th step sample; poster_xhat (: k) represents the state vector of the posterior estimate of the kth sample; prior_Sigma (: k+1) represents the state covariance matrix of the Prior estimate of the k+1th step sample; poster_Sigma (: k) represents the state covariance matrix of the posterior estimate of the kth sample; sigmw represents the process noise variance;
s2223, triggering information gamma according to event k And (3) carrying out system state vector estimation update on the state vector Poster_xhat of posterior estimation and the state covariance matrix Poster_Sigma of posterior estimation in combination with an event-triggered Kalman filter.
The step S23 specifically includes the following steps:
after the state estimation is carried out by using the event-triggered Kalman filter, the system measurement variables are reconstructed, and the reconstruction variables of the system in the kth step can be obtained according to the state space equation of the system as follows:
preferably, step S2223 specifically includes the steps of:
when event triggering information gamma k When=1, the measured variable is measured and sampled by the system sensor to be in the event-triggered state estimator for state estimation, and at this time, the system state vector estimation updating step according to the predicted prior estimated variable and the measured variable is as follows:
step one, setting an observed noise covariance matrix: h=sigmv;
secondly, calculating an optimal event triggering Kalman filter gain matrix:
K(:,k+1)＝Prior_Sigma(:,k+1)*C T /(C*Prior_Sigma(:,k+1)*C T +H) (8)
wherein K (wherein, k+1) represents an event-triggered Kalman filter gain matrix of the (k+1) th step of sampling;
third, correcting the state vector of the posterior estimation by using the prior estimation value of the k+1 step and the difference value of the measured variable of the k+1 step:
Poster_xhat(:,k+1)＝Prior_xhat(:,k+1)+K(:,k+1)*(y k+1 -C*Prior_xhat(:,k+1)) (9)
wherein, K (: k+1): (y k+1 -C: k+1) represents the correction part between the a priori estimated state vector and the measured value, wherein K (: k+1) represents the k+1-th sampled event triggered kalman filter gain matrix, k+1-th sampled kalman gain matrix K (: k+1) as a weighting factor for balancing the effect of the measured value and the a priori estimated state vector in the updating;
fourth, updating the state covariance matrix of the posterior estimation:
Poster_Sigma(:,k+1)＝(I-K(:,k+1)*C)*Prior_Sigma(:,k+1) (10)；
when event triggering information gamma k Because the measured variable is not transmitted to the event-triggered state estimator through the measurement of the system sensor, the state estimation is performed by using the sampled value at the previous time, and the update procedure of the system state vector estimation is as follows:
step one, setting an observed noise covariance matrix: h=sigmv+1/Pi, which indicates that the magnitude of the observed noise covariance matrix is determined by the system noise and the probability false alarm rate Pi of the event-triggered state estimator, and the noise matrix is increased to compensate the estimation error due to lack of measurement variables;
secondly, calculating an optimal event triggering Kalman filter gain matrix:
K(:,k+1)＝Prior_Sigma(:,k+1)*C T /(C*Prior_Sigma(:,k+1)*C T +H) (11)；
thirdly, calculating a state vector measured value of posterior estimation:
Poster_xhat(:,k+1)＝Prior_xhat(:,k+1) (12)；
fourth, updating the state covariance matrix of the posterior estimation:
Poster_Sigma(:,k+1)＝(I-K(:,k+1)*C)*Prior_Sigma(:,k+1) (13)。
preferably, the step S3 specifically includes the following steps:
s31, carrying out residual analysis according to the reconstructed data and the measurement variables, wherein the residual Res is defined as follows:
s32, setting the alarm threshold value as the standard deviation S of the whole measurement variable y ：
Where M represents the length of the system sample data, y i The system measurement variable representing the ith sample,representing the average value of all the system sampling data;
s33, comparing the residual Res with an alarm threshold value: if residual Res exceeds alarm threshold S y Judging that the system fails at the moment; otherwise, judging that the system is running normally at the moment.
The invention has the following beneficial effects:
1. compared with the traditional data compression method based on periodic sampling, the sampling control period is not fixed, but triggered by a specific event, so that the sampling frequency and the traffic are obviously reduced.
2. Considering data reconstruction, utilizing implicit information of event-triggered sampling time, an event-triggered Kalman filter is provided, and when data is transmitted to the event-triggered Kalman filter, a receiving value is directly adopted; when the event-triggered Kalman filter does not receive data, the measurement variables implicit in the event-triggered conditions can be mined to reconstruct the data, so that the performance of the system is effectively improved.
3. After the data reconstruction is completed, fault detection of abnormal signals is carried out by utilizing residual analysis and a set alarm threshold value.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a flow chart of an information compression and anomaly detection method based on event triggered sampling according to the present invention;
FIG. 2 is a diagram of simulation results of a tacho signal event triggered sampling in accordance with an embodiment of the present invention;
FIG. 3 is a diagram of a simulation result of reconstructing rotational speed data according to an embodiment of the present invention;
fig. 4 is a diagram of fault detection simulation results of rotational speed reconstruction data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the detailed description and specific examples, while indicating the embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein. Examples of the embodiments are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements throughout or elements having like or similar functionality.
It should be noted that the terms "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present invention, it should be noted that, directions or positional relationships indicated by terms such as "upper", "lower", "inner", "outer", etc., are directions or positional relationships based on those shown in the drawings, or those that are conventionally put in use, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements to be referred to must have a specific direction, be constructed and operated in a specific direction, and thus should not be construed as limiting the present invention.
In the description of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
As shown in fig. 1, the method for compressing and detecting abnormality of information based on event triggered sampling includes the following steps:
s1, determining a triggering condition and a threshold value of a system data sampling event, and completing data sampling of a measurement variable;
in this embodiment, considering signals of a satellite signal monitoring system, a power grid monitoring system, an unmanned vehicle system and other similar systems (the following systems refer to the systems in particular), key data of the systems can be divided into two types: control signals and measurement/control signals (here/and others/all herein denote or). After the controlled signal is acquired by the sensor, whether transmission is performed or not needs to be judged through the event triggering condition of the communication channel. For example, sensor data acquisition systems for unmanned vehicle systems have selected deterministic event-triggered condition sampling to reduce unnecessary rotational speed data transmissions, since variables such as unmanned vehicle speed typically only change significantly when started or shifted.
Preferably, the system data in step S1 includes a control signal and a measurement/control signal;
after the measurement/controlled signal is acquired by the sensor, whether transmission is performed or not is judged through the event triggering condition of the communication channel, and the event triggering condition of the communication channel is as follows:
wherein, gamma k A trigger variable of 0 or 1, which represents event trigger information of the kth sample; y is k Is the kth step measured variable measured by the sensor; y is k-1 Is the k-1 step measured variable measured by the sensor;
at this time, at the current measurement value y of the sensor k And the last transmitted measurement y k-1 The difference value exceeds the threshold delta, the condition is triggered, and the sensor samples and transmits the system monitoring data.
The average communication rate of the system represents a redundancy problem for the sampled data, and is typicallyPositively correlated to a threshold delta in the event trigger mechanism. To reduce the communication rate of the system, this is typically accomplished by increasing the threshold delta in the event trigger condition. Preferably, the step of obtaining the threshold δ is as follows:
first sampling to obtain average communication rate
Wherein N is the length of the time series;
if delta is set too large, too little data is acquired at the system state estimator to facilitate subsequent data recovery and fault detection, thus defining-estimating errors
In the method, in the process of the invention,representing a system measurement variable y acquired via an event triggered state estimator k Is a function of the estimated value of (2);
finally, changing the threshold value delta to obtain the average communication rate under different threshold values deltaError of estimation->Plotting average communication rate +.>Error of estimation->And (3) selecting an event triggering condition threshold delta from the balance curve between the two points or from the closest point of the balance curve.
S2, sampling a measurement variable according to event triggering, and designing event triggering Kalman filter to reconstruct data;
preferably, the step S2 specifically includes the following steps:
s21, establishing a state space equation of the system;
preferably, in step S21, the control signal of the system is defined as u= [ u ] 1 ,u 2 ,...,u n ]∈R n ，R n Is a real matrix of n dimensions; the measurement/controlled signal is defined as y= [ y ] 1 ,y 2 ,...,y m ]∈R m ，R m Is a real matrix of m dimension; and consider that the system measurement variable becomes discrete signal after sampling, combine with control signal u k And a measurement variable y obtained by sampling the measurement/controlled signal k Establishing a discrete linear time invariant system of the system:
wherein A is a system matrix, B is a control matrix, C is an observation matrix, and D is a direct transfer matrix; x is x k ∈R n Is a status signal/variable; u (u) 1 ,u 2 ,...,u n Is a deterministic input control signal that is known to the estimator; w (w) k And v k Respectively for representing process noise and measurement noise.
S22, initializing a variable of the system;
preferably, the step S22 specifically includes the following steps:
s221, setting initial state variable x 0 ＝[a 1 ,a 2 ,…,a n ] T Process noiseMeasurement noise->Wherein a is 1 ,a 2 ,…,a n ,b 1 ,c 1 All have non-negative constants；
S222, establishing an event trigger state estimator according to the established discrete linear time invariant system.
Preferably, the step S222 specifically includes the following steps:
s2221, initializing a state covariance matrix Prior_Sigma estimated in Prior and a state vector Prior_xhat estimated in Prior according to the normal working and running states of the system; and let the posterior estimated state vector Poster_xhat=x 0 State covariance matrix of posterior estimation master_sigma=w k ；
S2222, predicting prior estimated variables according to a system state space equation, a posterior estimated state vector Poster_xhat and a state covariance matrix Poster_xhat as follows:
Prior_xhat(:,k+1)＝A*Poster_xhat(:,k)+B*u k (5)
Prior_Sigma(:,k+1)＝A*Poster_Sigma(:,k)*A T +sigmw (6)
where Prior_xhat (: k+1) represents the a priori estimated state vector of the k+1 th step sample; poster_xhat (: k) represents the state vector of the posterior estimate of the kth sample; prior_Sigma (: k+1) represents the state covariance matrix of the Prior estimate of the k+1th step sample; poster_Sigma (: k) represents the state covariance matrix of the posterior estimate of the kth sample; sigmw represents the process noise variance;
s2223, triggering information gamma according to event k And (3) carrying out system state vector estimation update on the state vector Poster_xhat of posterior estimation and the state covariance matrix Poster_Sigma of posterior estimation in combination with an event-triggered Kalman filter.
Preferably, step S2223 specifically includes the steps of:
when event triggering information gamma k When=1, the measured variable is measured and sampled by the system sensor to be in the event-triggered state estimator for state estimation, and at this time, the system state vector estimation updating step according to the predicted prior estimated variable and the measured variable is as follows:
step one, setting an observed noise covariance matrix: h=sigmv;
secondly, calculating an optimal event triggering Kalman filter gain matrix:
K(:,k+1)＝Prior_Sigma(:,k+1)*C T /(C*Prior_Sigma(:,k+1)*C T +H) (8)
wherein K (wherein, k+1) represents an event-triggered Kalman filter gain matrix of the (k+1) th step of sampling;
third, correcting the state vector of the posterior estimation by using the prior estimation value of the k+1 step and the difference value of the measured variable of the k+1 step:
Poster_xhat(:,k+1)＝Prior_xhat(:,k+1)+K(:,k+1)*(y k+1 -C*Prior_xhat(:,k+1)) (9)
wherein, K (: k+1): (y k+1 -C: k+1) represents the correction part between the a priori estimated state vector and the measured value, wherein K (: k+1) represents the k+1-th sampled event triggered kalman filter gain matrix, k+1-th sampled kalman gain matrix K (: k+1) as a weighting factor for balancing the effect of the measured value and the a priori estimated state vector in the updating;
fourth, updating the state covariance matrix of the posterior estimation:
Poster_Sigma(:,k+1)＝(I-K(:,k+1)*C)*Prior_Sigma(:,k+1) (10)；
when event triggering information gamma k Because the measured variable is not transmitted to the event-triggered state estimator through the measurement of the system sensor, the state estimation is performed by using the sampled value at the previous time, and the update procedure of the system state vector estimation is as follows:
step one, setting an observed noise covariance matrix: h=sigmv+1/Pi, which indicates that the magnitude of the observed noise covariance matrix is determined by the system noise and the probability false alarm rate Pi of the event-triggered state estimator, and the noise matrix is increased to compensate the estimation error due to lack of measurement variables;
secondly, calculating an optimal event triggering Kalman filter gain matrix:
K(:,k+1)＝Prior_Sigma(:,k+1)*C T /(C*Prior_Sigma(:,k+1)*C T +H) (11)；
thirdly, calculating a state vector measured value of posterior estimation:
Poster_xhat(:,k+1)＝Prior_xhat(:,k+1) (12)；
fourth, updating the state covariance matrix of the posterior estimation:
Poster_Sigma(:,k+1)＝(I-K(:,k+1)*C)*Prior_Sigma(:,k+1) (13)。
s23, reconstructing a system measured value after performing state estimation by using an event-triggered Kalman filter;
the step S23 specifically includes the following steps:
after the state estimation is carried out by using the event-triggered Kalman filter, the system measurement variables are reconstructed, and the reconstruction variables of the system in the kth step can be obtained according to the state space equation of the system as follows:
it can be known that the event-triggered kalman filter can obtain a more accurate state estimation value by continuously estimating and updating the state of the system, so that a reference value or a reference value for an observation signal can be provided.
And S3, performing fault detection by using residual analysis data.
Preferably, the step S3 specifically includes the following steps:
s31, carrying out residual analysis according to the reconstructed data and the measurement variables, wherein the residual Res is defined as follows:
s32, setting an alarm threshold value as a standard deviation S of an overall measurement variable according to an actual measurement value of the system in order to reflect the overall fluctuation degree of the system data y ：
Where M represents the length of the system sample data, y i The system measurement variable representing the ith sample,representing the average value of all the system sampling data;
s33, comparing the residual Res with an alarm threshold value: if residual Res exceeds alarm threshold S y Judging that the system fails at the moment; otherwise, judging that the system is running normally at the moment.
As shown in fig. 2, 3 and 4, simulation results of an embodiment of the present invention are shown. Fig. 2 shows the event-triggered sampling effect on the rotation speed signal, and it can be seen that under the sampling method proposed by the invention, the sampled data points are far less than the original data, so that the problem of redundant processing of the data is realized, and the resource occupation of the data transmission channel can be effectively saved. Fig. 3 shows the effect of reconstructing the rotation speed data of the unmanned vehicle motor through the event-triggered state estimator, and can be seen that the reconstruction data is basically overlapped with the original data through the proposed event-triggered kalman filter reconstruction method, so that an important basis is provided for fault detection of subsequent signals. And in the figure 4, residual analysis is carried out on the reconstructed unmanned vehicle rotating speed data according to the event-triggered Kalman filter, and then fault detection is carried out by comparing the residual with an alarm threshold value, so that the mining of hidden information of a system sampling signal is realized, and the safe operation of the unmanned vehicle system is ensured.
Therefore, the method for compressing and detecting the abnormality of the information based on the event-triggered sampling is adopted, and the event-triggered state estimator triggered by the event is arranged to mine the measurement variable hidden in the event-triggered sampling data, so that the performance of the system is effectively improved.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting it, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that: the technical scheme of the invention can be modified or replaced by the same, and the modified technical scheme cannot deviate from the spirit and scope of the technical scheme of the invention.
Claims (10)
1. The information compression and anomaly detection method based on event trigger sampling is characterized by comprising the following steps of: the method comprises the following steps:
s1, determining a triggering condition and a threshold value of a system data sampling event, and completing data sampling of a measurement variable;
s2, sampling a measurement variable according to event triggering, and designing event triggering Kalman filter to reconstruct data;
and S3, performing fault detection by using residual analysis data.
2. The event triggered sampling based information compression and anomaly detection method of claim 1, wherein: the system data described in step S1 includes control signals and measurement/control signals;
after the measurement/controlled signal is acquired by the sensor, whether transmission is performed or not is judged through the event triggering condition of the communication channel, and the event triggering condition of the communication channel is as follows:
wherein, gamma k A trigger variable of 0 or 1, which represents event trigger information of the kth sample; y is k Is the kth step measured variable measured by the sensor; y is k-1 Is the k-1 step measured variable measured by the sensor;
at this time, at the current measurement value y of the sensor k And the last transmitted measurement y k-1 The difference value exceeds the threshold delta, the condition is triggered, and the sensor samples and transmits the system monitoring data.
3. The event triggered sampling based information compression and anomaly detection method of claim 2, wherein: the step of obtaining the threshold delta is as follows:
first sampling to obtain average communication rate
Wherein N is the length of the time series;
then, the estimated error is obtained by the event-triggered state estimator
In the method, in the process of the invention,representing a system measurement variable y acquired via an event triggered state estimator k Is a function of the estimated value of (2);
finally, changing the threshold value delta to obtain the average communication rate under different threshold values deltaError of estimation->Plotting average communication rateError of estimation->And (3) selecting an event triggering condition threshold delta from the balance curve between the two points or from the closest point of the balance curve.
4. The event triggered sampling based information compression and anomaly detection method of claim 1, wherein: the step S2 specifically comprises the following steps:
s21, establishing a state space equation of the system;
s22, initializing a variable of the system;
s23, reconstructing a system measured value after performing state estimation by using an event-triggered Kalman filter.
5. The event triggered sampling based information compression and anomaly detection method of claim 4, wherein: in step S21, the control signal of the system is defined as u= [ u ] 1 ,u 2 ,...,u n ]∈R n ，R n Is a real matrix of n dimensions; the measurement/controlled signal is defined as y= [ y ] 1 ,y 2 ,...,y m ]∈R m ，R m Is a real matrix of m dimension; and consider that the system measurement variable becomes discrete signal after sampling, combine with control signal u k And a measurement variable y obtained by sampling the measurement/controlled signal k Establishing a discrete linear time invariant system of the system:
wherein A is a system matrix, B is a control matrix, C is an observation matrix, and D is a direct transfer matrix; x is x k ∈R n Is a status signal/variable; u (u) 1 ,u 2 ,...,u n Is a deterministic input control signal that is known to the estimator; w (w) k And v k Respectively for representing process noise and measurement noise.
6. The event triggered sampling based information compression and anomaly detection method of claim 4, wherein: the step S22 specifically includes the following steps:
s221, setting initial state variable x 0 ＝[a 1 ,a 2 ,…,a n ] T Process noiseMeasurement noise->Wherein a is 1 ,a 2 ,…,a n ,b 1 ,c 1 Are all non-negative constants;
s222, establishing an event trigger state estimator according to the established discrete linear time invariant system.
7. The event triggered sampling based information compression and anomaly detection method of claim 6, wherein: step S222 specifically includes the following steps:
s2221, initializing a state covariance matrix Prior_Sigma estimated in Prior and a state vector Prior_xhat estimated in Prior according to the normal working and running states of the system; and let the posterior estimated state vector Poster_xhat=x 0 State covariance matrix of posterior estimation master_sigma=w k ；
S2222, predicting prior estimated variables according to a system state space equation, a posterior estimated state vector Poster_xhat and a state covariance matrix Poster_xhat as follows:
Prior_xhat(:,k+1)＝A*Poster_xhat(:,k)+B*u k (5)
Prior_Sigma(:,k+1)＝A*Poster_Sigma(:,k)*A T +sigmw (6)
where Prior_xhat (: k+1) represents the a priori estimated state vector of the k+1 th step sample; poster_xhat (: k) represents the state vector of the posterior estimate of the kth sample; prior_Sigma (: k+1) represents the state covariance matrix of the Prior estimate of the k+1th step sample; poster_Sigma (: k) represents the state covariance matrix of the posterior estimate of the kth sample; sigmw represents the process noise variance;
s2223, triggering information gamma according to event k And (3) carrying out system state vector estimation update on the state vector Poster_xhat of posterior estimation and the state covariance matrix Poster_Sigma of posterior estimation in combination with an event-triggered Kalman filter.
8. The event triggered sampling based information compression and anomaly detection method of claim 6, wherein: the step S23 specifically includes the following steps:
after the state estimation is carried out by using the event-triggered Kalman filter, the system measurement variables are reconstructed, and the reconstruction variables of the system in the kth step can be obtained according to the state space equation of the system as follows:
9. the event triggered sampling based information compression and anomaly detection method of claim 7, wherein: step S2223 specifically includes the steps of:
when event triggering information gamma k When=1, the measured variable is measured and sampled by the system sensor to be in the event-triggered state estimator for state estimation, and at this time, the system state vector estimation updating step according to the predicted prior estimated variable and the measured variable is as follows:
step one, setting an observed noise covariance matrix: h=sigmv;
secondly, calculating an optimal event triggering Kalman filter gain matrix:
K(:,k+1)＝Prior_Sigma(:,k+1)*C T /(C*Prior_Sigma(:,k+1)*C T +H) (8)
wherein K (wherein, k+1) represents an event-triggered Kalman filter gain matrix of the (k+1) th step of sampling;
third, correcting the state vector of the posterior estimation by using the prior estimation value of the k+1 step and the difference value of the measured variable of the k+1 step:
Poster_xhat(:,k+1)＝Prior_xhat(:,k+1)+K(:,k+1)*(y k+1 -C*Prior_xhat(:,k+1)) (9)
wherein, K (: k+1): (y k+1 -C: k+1) represents the correction part between the a priori estimated state vector and the measured value, wherein K (: k+1) represents the k+1-th sampled event triggered kalman filter gain matrix, k+1-th sampled kalman gain matrix K (: k+1) as a weighting factor for balancing the effect of the measured value and the a priori estimated state vector in the updating;
fourth, updating the state covariance matrix of the posterior estimation:
Poster_Sigma(:,k+1)＝(I-K(:,k+1)*C)*Prior_Sigma(:,k+1) (10)；
when event triggering information gamma k Because the measured variable is not transmitted to the event-triggered state estimator through the measurement of the system sensor, the state estimation is performed by using the sampled value of the previous step, and the update procedure of the system state vector estimation is as follows:
step one, setting an observed noise covariance matrix: h=sigmv+1/Pi, which indicates that the magnitude of the observed noise covariance matrix is determined by the system noise and the probability false alarm rate Pi of the event-triggered state estimator, and the noise matrix is increased to compensate the estimation error due to lack of measurement variables;
secondly, calculating an optimal event triggering Kalman filter gain matrix:
K(:,k+1)＝Prior_Sigma(:,k+1)*C T /(C*Prior_Sigma(:,k+1)*C T +H) (11)；
thirdly, calculating a state vector measured value of posterior estimation:
Poster_xhat(:,k+1)＝Prior_xhat(:,k+1) (12)；
fourth, updating the state covariance matrix of the posterior estimation:
Poster_Sigma(:,k+1)＝(I-K(:,k+1)*C)*Prior_Sigma(:,k+1) (13)。
10. the event triggered sampling based information compression and anomaly detection method of claim 9, wherein: the step S3 specifically comprises the following steps:
s31, carrying out residual analysis according to the reconstructed data and the measurement variables, wherein the residual Res is defined as follows:
s32, setting the alarm threshold value as the standard deviation S of the whole measurement variable y ：
Where M represents the length of the system sample data, y i The system measurement variable representing the ith sample,representing the average value of all the system sampling data;
s33, comparing the residual Res with an alarm threshold value: if residual Res exceeds alarm threshold S y Judging that the system fails at the moment; otherwise, judging that the system is running normally at the moment.
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