CN107644148B - On-orbit satellite abnormal state monitoring method and system based on multi-parameter association - Google Patents
On-orbit satellite abnormal state monitoring method and system based on multi-parameter association Download PDFInfo
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Abstract
The invention belongs to the field of satellite state detection, and discloses an on-orbit satellite abnormal state monitoring method and system based on multi-parameter association to solve the problem that coupling of a plurality of telemetering parameters cannot be detected. The method comprises the following steps: collecting satellite telemetry parameter historical data and acquiring a corresponding scatter diagram; obtaining an incidence relation model on a telemetry parameter value space based on a maximum mutual information coefficient according to a scatter diagram corresponding to the telemetry parameter historical data of the satellite in a normal state; calculating mutual information coefficients according to scatter diagrams corresponding to the telemetry parameter historical data of the satellite in a normal state and an abnormal state in combination with an incidence relation model and determining distribution of the mutual information coefficients; receiving telemetering parameter data of the orbiting satellite, and calculating a mutual information coefficient between telemetering parameters according to the incidence relation model; and calculating the Mahalanobis distance according to the mutual information coefficient among the remote measurement parameters and the distribution of the mutual information coefficient, and judging whether the multi-parameter association state is abnormal or not according to the Mahalanobis distance.
Description
Technical Field
The invention relates to the field of satellite detection, in particular to an on-orbit satellite abnormal state monitoring method and system based on multi-parameter association.
Background
At present, the on-orbit abnormal state detection of the satellite based on the measured data mainly adopts a single-parameter threshold detection method and a multi-parameter combined threshold detection method in engineering, and in addition, some researches carry out abnormal detection by extracting signal characteristics of a single telemetering parameter or main components of a plurality of telemetering parameters and based on the extracted characteristic quantity. It is difficult to detect anomalies or faults caused by the coupling of multiple telemetry parameters using this method.
The method comprises the steps of monitoring various telemetering parameters in the in-orbit operation process of the satellite, detecting the in-orbit abnormal state of the satellite by utilizing measured data of the parameters, and early warning possible in-orbit abnormality or failure. As a multi-parameter complex control system, the correlation among a plurality of telemetric parameters in the in-orbit operation process of a satellite can be changed due to the influence of factors such as operation modes, abnormal states, component faults and the like. For example, when the current is stably changed within a predetermined range, the battery temperature is proportional to the square of the current; this square proportional relationship is destroyed when a short circuit fault occurs. Therefore, by detecting the change of the correlation among the telemetering parameters, the multi-parameter correlation abnormity of the multi-parameter complex system including the satellite can be detected and early warned.
Disclosure of Invention
The invention aims to provide a method for monitoring the abnormal state of an orbiting satellite based on multi-parameter association, so as to solve the problem that the abnormality or the fault caused by the coupling of a plurality of telemetering parameters cannot be detected.
In order to achieve the aim, the invention provides an on-orbit satellite abnormal state monitoring method based on multi-parameter association, which comprises the following steps of:
collecting satellite telemetry parameter historical data and acquiring a corresponding scatter diagram;
obtaining an incidence relation model between the telemetry parameters based on the maximum mutual information coefficient according to a scatter diagram corresponding to the telemetry parameter historical data of the satellite in a normal state;
calculating mutual information coefficients of the satellite in the normal state and the abnormal state according to scatter diagrams corresponding to the telemetry parameter historical data of the satellite in the normal state and the abnormal state and combining an incidence relation model between the telemetry parameters, and determining distribution of the mutual information coefficients;
receiving telemetering parameter data of the orbiting satellite, and calculating a mutual information coefficient between telemetering parameters according to the incidence relation model;
and calculating the Mahalanobis distance according to the mutual information coefficient between the telemetering parameters and the distribution of the mutual information coefficient of the satellite in the normal state and the abnormal state, and judging whether the multi-parameter association state is abnormal or not according to the Mahalanobis distance.
Based on the method, the invention also provides an on-orbit satellite abnormal state monitoring system based on multi-parameter association, which comprises the following steps:
a first module: the system is used for collecting satellite telemetry parameter historical data and acquiring a corresponding scatter diagram;
a second module: the method comprises the steps of obtaining an incidence relation model between telemetry parameters based on a maximum mutual information coefficient according to a scatter diagram corresponding to telemetry parameter historical data of a satellite in a normal state;
a third module: the system is used for calculating mutual information coefficients of the satellite in the normal state and the abnormal state according to scatter diagrams corresponding to the telemetry parameter historical data of the satellite in the normal state and the abnormal state and by combining an incidence relation model between the telemetry parameters and determining distribution of the mutual information coefficients;
a fourth module: the system comprises a correlation relation model, a data acquisition module and a data processing module, wherein the correlation relation model is used for acquiring correlation relation data of the telemetry parameters of the orbiting satellite;
a fifth module: and the system is used for calculating the Mahalanobis distance according to the mutual information coefficient among the telemetering parameters and the distribution of the mutual information coefficient of the satellite in the normal state and the abnormal state, and judging whether the multi-parameter association state is abnormal or not according to the Mahalanobis distance.
The invention has the following beneficial effects:
the on-orbit satellite anomaly monitoring method based on multi-parameter association can detect the change of the association relation between the telemetering parameters, thereby carrying out detection and early warning on the multi-parameter association anomaly of a multi-parameter complex system.
The invention tracks the change of nonlinear correlation or non-function incidence relation along with time through the telemetry data segmentation strategy, and solves the problem of slow calculation of related characteristic quantity aiming at the characteristics of high downlink speed of telemetry data, large data quantity and the like.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flowchart of an abnormal condition monitoring method for an orbiting satellite based on multi-parameter association according to a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of a meshing scheme of a preferred embodiment of the present invention;
FIG. 3 is a schematic diagram of mutual information coefficient recurrence calculation according to the preferred embodiment of the present invention;
FIG. 4 is a scatter plot of satellite telemetry parameter historical data in accordance with a preferred embodiment of the present invention;
fig. 5 is a scatter plot of satellite telemetry parameters over different time windows in accordance with a preferred embodiment of the present invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Referring to fig. 1, the invention provides a method for monitoring abnormal states of an orbiting satellite based on multi-parameter association, comprising the following steps:
s1: and collecting historical data of the satellite telemetry parameters and acquiring a corresponding scatter diagram.
Telemetric parameter of X1,X2,…,XPCollected Q satellites S1,S2,…,SQThe historical data of telemetry parameters of (a) is:
wherein N isp,qIs satellite S in normal stateqRemote measurement parameter X ofpHistory data of (D)p,qIs satellite S in abnormal stateqRemote measurement parameter X ofpThe history data of (a). The parameter data comprises telemetering parameters such as temperature, current and voltage, and the like, because the satellite needs to measure various physical quantities of components thereof in the in-orbit operation process, the physical quantities are telemetering parameters, and the obtained data are telemetering data.
S2: and obtaining an incidence relation model between the telemetry parameters based on the maximum mutual information coefficient according to a scatter diagram corresponding to the telemetry parameter historical data of the satellite in a normal state.
The maximum Mutual Information Coefficient (MIC), which is called maximum Information Coefficient (maxm), is a method for calculating the correlation between variables in a large data set, and can estimate the correlation between variables without making any assumption on the data distribution, and may also be called maximum Information Coefficient. Based on historical data of in-orbit satellite in normal state according to MIC (minimum integrated coefficient)p,q}p＝1,…,P,q＝1,…QCan determine the telemetering parameter X of the on-orbit satellite in the normal state1,X2,…,XPThe incidence relation model of (1) is a meshing scheme. The operation state of the satellite can be judged according to the collected diagnosis rules, but is actually determined by an operation management unit and a satellite development department. In the collected historical data, it is generally indicated that the satellite state is normal in those time periods, and the corresponding telemetry data is the normal telemetry data. Note that here normal is for the satellite state, not data.
S3: and calculating mutual information coefficients of the satellite in the normal state and the abnormal state according to scatter diagrams corresponding to the telemetry parameter historical data of the satellite in the normal state and the abnormal state and by combining an incidence relation model between the telemetry parameters, and determining the distribution of the mutual information coefficients.
And (4) putting scatter diagrams corresponding to the telemetry parameter historical data of all the satellites in the normal state and the abnormal state into the grids obtained in the step (S2), and calculating the mean vector and the covariance matrix of the multivariate normal distribution of the telemetry parameter historical data of each satellite in the normal state and the abnormal state, namely the distribution of the mutual information coefficients.
S4: and receiving the telemetry parameter data of the orbiting satellite, and calculating a mutual information coefficient between the telemetry parameters according to the incidence relation model.
S5: and calculating the Mahalanobis distance according to the mutual information coefficient between the telemetering parameters and the distribution of the mutual information coefficient of the satellite in the normal state and the abnormal state, and judging whether the multi-parameter association state is abnormal or not according to the Mahalanobis distance.
Preferably, the determination of the correlation model between the telemetry parameters comprises the following steps:
s201: computing satellite S1First maximum mutual information coefficient between telemetry parameter data and satellite S2And determining a first meshing scheme corresponding to the first maximum mutual information coefficient and a second meshing scheme corresponding to the second maximum mutual information coefficient.
For satellite S1From telemetry parameter history Ni,1And Ni,1Calculating XiAnd XjMaximum Mutual Information Coefficient (MIC) of
The corresponding GRID division scheme when calculating the maximum Mutual Information Coefficient (MIC) is marked as GRIDi,j. For satellite S2From the historical data Ni,2And Ni,2Calculating XiAnd XjMaximum Mutual Information Coefficient (MIC) of
The corresponding mesh partition scheme is noted as
S202: satellite S2And the telemetering parameter data is put into the first meshing scheme to obtain a second mutual information coefficient.
For S2GRID according to a meshing schemei,jCalculating XiAnd XjIs recorded as the mutual information coefficient of
S203: if satellite S2If the difference between the second mutual information coefficient and the second maximum mutual information coefficient is greater than the rated error, the first meshing scheme and the second meshing scheme are combined to be used as a new first meshing scheme.
If it is
Then
Giving real numbers with allowable error greater than 0i,jAnd the error value range is set according to the precision of the actually required result.
S204: traverse all satellites SqAnd repeating the construction process of the first meshing scheme and taking the final first meshing scheme as an incidence relation model among the telemetry parameters.
Referring to fig. 2, fig. 2 shows a specific method of mesh merging, wherein GRID in fig. 2(a)1From XiAnd XjOn a plane
Defining; GRID in FIG. 2(b)2From XiAnd XjOn a plane
And (4) defining. The invention relates to two GRID division schemes GRID1And GRID2Refers to an operation defined by the rule GRID1 ∪ GRID2, GRID2
Definition, as shown in FIG. 2 (c). The obtained meshing scheme is the meshing scheme shared by all the satellites.
Preferably, the distribution of the telemetry parameter mutual information coefficients is as follows:
based on { GRIDi,j}i,j＝1,…PAnd historical data Np,q}p＝1,…,P,q＝1,…QCalculating sample data of correlation degree between telemetering parameters under normal state
Based on incidence relation { GRIDi,j}i,j＝1,…PAnd historical data { Dp,q}p＝1,…,P,q＝1,…QSample data for calculating correlation between telemetry parameters in abnormal state
Mean vector of telemetering parameter correlation degree under normal state
Sum covariance matrix
The following were used:
mean vector of telemetering parameter correlation degree in abnormal state
Sum covariance matrix
The following were used:
wherein Q is the number of satellites,
is X in the normal state of the qth satelliteiAnd XjThe mutual information coefficient of (a) is,
is X in abnormal state of the q-th satelliteiAnd XjThe mutual information coefficient of (2). Because the mean vector and the covariance matrix determined by normal distribution are adopted, the distribution of the correlation degree can be determined by simultaneous two formulas.
Preferably, the method for determining the telemetry parameter mutual information coefficient comprises the following steps:
s401: setting a time window [ t, t + L ], acquiring data of the telemetering parameters in the time window [ t, t + L ], wherein the data quantity is n, and recording
As shown in FIG. 3(a), wherein nh,lThe number of data in the ith row and the ith column of grids of the grid division scheme is nh,·＝Σlhh,l，n·,l＝Σhhh,lThen based on the time window [ t, t + L ]]Internal telemetry parameter calculation XiAnd XjHas a mutual information coefficient of
Wherein the mesh division scheme is characterized in that the h row and l column meshes are marked as meshes (h, l), wherein at，bt，ctThe method has no specific meaning, is an intermediate result adopted in the iterative computation process, and the values of the intermediate result change along with the change of time t.
S402: move the time window to [ t +1, t +1+ L]Let the data point at time t fall into the grid (h)1,l1) In (h), the data at time t +1+ L falls into the grid2,l2) In (1). Based on time window [ t +1, t +1+ L]Internal telemetry parameter calculation XiAnd XjThe rule for updating the number of data points in each grid is as follows:
if h1＝h2And l1＝l2Then XiAnd XjThe mutual information coefficient is:
ICt+1＝ICt
keeping the number of data points in each grid unchanged;
if h1≠h2Or l1≠l2Then XiAnd XjThe mutual information coefficient is:
wherein
nh,·＝Σlhh,l，n·,l＝Σhhh,l
And updating the number of data points in each grid: grid (h)1,l1) The number of inner data points is
Grid (h)2,l2) The number of inner data points is
Except that (h)1,l1)、(h2,l2) The number of data points in the other remaining grids is unchanged.
Where FIG. 3(b) is the data point distribution for the [ t +1, t +1+ L ] time window.
The mutual information coefficient is calculated by adopting the iterative method, the mutual information coefficient calculation is only carried out on the grids with the changed data points, the data in all the grids are not required to be recalculated, and the data calculation amount is greatly reduced.
Preferably, the detecting of the abnormal state includes:
s501: setting a time window [ t, t + L]Calculating any set of telemetry parameters XiAnd XjThe mutual information coefficient x of the measured data.
S502: and calculating the mahalanobis distance according to the mutual information coefficient x and the distribution of the telemetry parameter mutual information coefficient:
s503: if d is1(x)<d2(x) If the state is normal, setting t as t +1, and continuing to perform real-time detection; if d is1(x)≥d2(x) If the state is abnormal, the real-time detection is continuously carried out after the alarm is set to t + L.
Based on the method, the invention also provides an on-orbit satellite abnormal state monitoring system based on multi-parameter association, which comprises the following steps:
a first module: the system is used for collecting satellite telemetry parameter historical data and acquiring a corresponding scatter diagram;
a second module: the method comprises the steps of obtaining an incidence relation model between telemetry parameters based on a maximum mutual information coefficient according to a scatter diagram corresponding to telemetry parameter historical data of a satellite in a normal state;
a third module: the system is used for calculating mutual information coefficients of the satellite in the normal state and the abnormal state according to scatter diagrams corresponding to the telemetry parameter historical data of the satellite in the normal state and the abnormal state and by combining an incidence relation model between the telemetry parameters and determining distribution of the mutual information coefficients;
a fourth module: the system comprises a correlation relation model, a data acquisition module and a data processing module, wherein the correlation relation model is used for acquiring correlation relation data of the telemetry parameters of the orbiting satellite;
a fifth module: and the system is used for calculating the Mahalanobis distance according to the mutual information coefficient among the telemetering parameters and the distribution of the mutual information coefficient of the satellite in the normal state and the abnormal state, and judging whether the multi-parameter association state is abnormal or not according to the Mahalanobis distance.
Preferably, the determination of the type of correlation between the telemetry parameters comprises:
a first unit: for computing satellite S1First maximum mutual information coefficient between telemetry parameter data and satellite S2Determining a first meshing scheme corresponding to the first maximum mutual information coefficient and a second meshing scheme corresponding to the second maximum mutual information coefficient;
a second unit: for connecting satellites S2The telemetering parameter data is put into a first grid division scheme to obtain a second mutual information coefficient;
a third unit: using if satellite S2If the difference value between the second mutual information coefficient and the second maximum mutual information coefficient is larger than the rated error, combining the first meshing scheme and the second meshing scheme as a new first meshing scheme;
a fourth unit: repeating the above construction process of the first meshing scheme for searching the next satellite until all the comparison satellites are traversed.
Preferably, the elements of the mean vector and covariance matrix of the multivariate normal distribution of telemetry parameter mutual information coefficients are as follows:
wherein Q is the number of satellites,
is X in the normal state of the qth satelliteiAnd XjThe mutual information coefficient of (a) is,
is X in abnormal state of the q-th satelliteiAnd XjThe mutual information coefficient of (2).
Preferably, the method for determining the telemetry parameter mutual information coefficient comprises the following steps:
a fifth unit: is used for setting a time window [ t, t + L ], obtaining the data quantity n of the telemetering parameters in the time window [ t, t + L ], and recording
Wherein n ish,lThe number of data in the ith row and the ith column of grids (marked as grid (h, l)) of the grid division scheme is nh,·＝Σlhh,l，n·,l＝Σhhh,lThen based on the time window [ t, t + L ]]Internal telemetry parameter calculation XiAnd XjHas a mutual information coefficient of
Wherein, the mesh of the h row and the l column of the mesh division scheme is marked as mesh (h, l).
A sixth unit: for moving the time window to [ t +1, t +1+ L]Let the data point at time t fall into the grid (h)2,l2) In (h), the data at time t +1+ L falls into the grid2,l2) In, then based on the time window [ t +1, t +1+ L]Internal telemetry parameter calculation XiAnd XjThe rule for updating the number of data points in each grid is as follows:
if h1＝h2And l1＝l2Then XiAnd XjThe mutual information coefficient is:
ICt+1＝ICt
keeping the number of data points in each grid unchanged;
if h1≠h2Or l1≠l2Then XiAnd XjThe mutual information coefficient is:
wherein
nh,·＝∑lhh,l，n·,l＝∑hhh,l
A seventh unit: for updating the number of data points within each grid: grid (h)1,l1) The number of inner data points is
Grid (h)2,l2) The number of inner data points is
Except that (h)1,l1)、(h2,l2) The number of data points in the other remaining grids is unchanged.
Preferably, the detection of the abnormal state comprises:
an eighth unit: for setting a time window [ t, t + L]Calculating any set of telemetry parameters XiAnd XjThe mutual information coefficient x of the measured data;
a ninth unit: the method is used for calculating the Mahalanobis distance according to the mutual information coefficient x and the distribution of the telemetry parameter mutual information coefficient:
a tenth unit: for if d1(x)<d2(x) If the state is normal, setting t as t +1, and continuing to perform real-time detection; if d is1(x)≥d2(x) If the state is abnormal, the real-time detection is continuously carried out after the alarm is set to t + L.
The process of the present invention is further illustrated by the specific application below.
1. Historical data of two telemetry parameters of 10 satellites are collected, wherein the data comprise data in a normal state and data in an abnormal state. Referring to FIG. 4, FIG. 4(a) shows a satellite S1A scatter diagram of the historical data in the normal state, and FIG. 4(b) shows a satellite S1Historical data scatter plots under abnormal conditions.
2. According to the telemetry history data of 10 satellites in the normal state, the grid division scheme on the telemetry parameter value space in the normal state is determined by the method of the embodiment S2.
3. And calculating mutual information coefficients of the two telemetering parameters in the normal state and the abnormal state by combining a grid division scheme according to historical data of the two telemetering parameters in the normal state and the abnormal state of 10 satellites. Table 1 is a table of mutual information coefficients of the telemetry parameters, wherein 1-10 in table 1 represent 10 satellites respectively.
TABLE 1
Mean and variance of mutual information coefficients in normal and abnormal states:
4. and receiving the in-orbit measured data for the current satellite according to the time sequence. The time window width L is 500, the start time is T0, and fig. 5(a), (b), and (c) are scattergrams of measured data of the two telemetry parameters in the time windows [ T0, T0+ L ], [ T0+ L, T0+2L ], [ T0+37L, and T0+38L ], respectively. Based on the grid division scheme in the value space between the telemetering parameters under the normal state. And calculating mutual information coefficients of the two telemetry parameters of the current satellite. Calculations corresponding to the measured data in the three time windows shown in fig. 5 are shown in the "IC values" column of table 2.
TABLE 2
5. And calculating the Mahalanobis distance according to the calculated mutual information coefficient values among the telemetry parameters and the distribution of the mutual information coefficients in normal and abnormal states, and judging whether the multi-parameter association state of the system is abnormal or not, wherein the result is shown in a result judgment column in a table 2.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. An on-orbit satellite abnormal state monitoring method based on multi-parameter association is characterized by comprising the following steps:
collecting satellite telemetry parameter historical data and acquiring a corresponding scatter diagram;
according to a scatter diagram corresponding to the telemetry parameter historical data of the satellite in a normal state, obtaining an incidence relation model on a telemetry parameter value space based on a maximum mutual information coefficient, wherein the incidence relation model on the telemetry parameter value space is determined in a mode comprising the following steps:
computing satellite S1First maximum mutual information coefficient between telemetry parameter data and satellite S2Determining a first meshing scheme corresponding to the first maximum mutual information coefficient and a second meshing scheme corresponding to the second maximum mutual information coefficient;
satellite S2The telemetering parameter data is put into a first grid division scheme to obtain a second mutual information coefficient;
if satellite S2If the difference value between the second mutual information coefficient and the second maximum mutual information coefficient is larger than the rated error, combining the first meshing scheme and the second meshing scheme as a new first meshing scheme;
traverse all satellites SqRepeating the construction process of the first meshing scheme and taking the final first meshing scheme as an incidence relation model among the telemetry parameters;
calculating mutual information coefficients of the satellite in the normal state and the abnormal state by combining a scatter diagram corresponding to the telemetry parameter historical data of the satellite in the normal state and the abnormal state with an incidence relation model and determining the distribution of the mutual information coefficients;
receiving telemetry parameter data of an orbiting satellite, and calculating mutual information coefficients among the telemetry parameters according to an incidence relation model, wherein the determination mode of the mutual information coefficients among the telemetry parameters comprises the following steps:
setting a time window [ t, t + L ], acquiring data of the telemetering parameters in the time window [ t, t + L ], wherein the data quantity is n, and recording
Wherein n ish，lThe number of data in the ith row and the ith column of grids of the grid division scheme is nh，·＝∑lhh，l，n·，l＝∑hhh，lThen based on the time window [ t, t + L ]]Internal telemetry parameter calculation XiAnd XjHas a mutual information coefficient of
Wherein, the incidence relation model, i.e. the ith row and the ith column of the grid division scheme is marked as grid (h, l);
move the time window to [ t +1, t +1+ L]Let the data point at time t fall into the grid (h)1，l1) In (h), the data at time t +1+ L falls into the grid2，l2) Based on the time window [ t +1, t +1+ L]Internal telemetry parameter calculation XiAnd XjThe rule for updating the number of data points in each grid is as follows:
if h1＝h2And l1＝l2Then XiAnd XjThe mutual information coefficient is:
ICt+1＝ICt，
keeping the number of data points in each grid unchanged;
if h1≠h2Or l1≠l2Then XiAnd XjThe mutual information coefficient is:
wherein the content of the first and second substances,
nh，·＝∑lhh，l，n·，l＝∑hhh，l
and updating the number of data points in each grid: grid (h)1，l1) The number of inner data points is
Grid (h)2，l2) The number of inner data points is
Except that (h)1，l1)、(h2，l2) The number of data points in the other grids is not changed;
and calculating the Mahalanobis distance according to the mutual information coefficient between the telemetering parameters and the distribution of the mutual information coefficient of the satellite in the normal state and the abnormal state, and judging whether the multi-parameter association state is abnormal or not according to the Mahalanobis distance.
2. The method for monitoring the abnormal state of the orbiting satellite based on the multi-parameter association as claimed in claim 1, wherein the distribution of the mutual information coefficients among the telemetry parameters is a multivariate normal distribution, and the elements of the mean vector and the covariance matrix of the distribution are as follows:
wherein Q is the number of satellites,
is X in the normal state of the qth satelliteiAnd XjThe mutual information coefficient of (a) is,
is X in abnormal state of the q-th satelliteiAnd XjThe mutual information coefficient of (2).
3. The method for monitoring the abnormal state of the orbiting satellite based on the multi-parameter association as claimed in claim 2, wherein the step of judging whether the multi-parameter association state is abnormal or not according to the mahalanobis distance comprises the following steps:
setting a time window [ t, t + L]Calculating any set of telemetry parameters XiAnd XjThe mutual information coefficient x of the measured data;
and calculating the mahalanobis distance according to the mutual information coefficient x and the distribution of the telemetry parameter mutual information coefficient:
if d is1(x)＜d2(x) If the state is normal, setting t as t +1, and continuing to perform real-time detection; if d is1(x)≥d2(x) If the state is abnormal, the real-time detection is continuously carried out after the alarm is set to t + L.
4. An on-orbit satellite abnormal state monitoring system based on multi-parameter association, which is characterized by comprising:
a first module: the system is used for collecting satellite telemetry parameter historical data and acquiring a corresponding scatter diagram;
a second module: the method is used for obtaining an incidence relation model between the telemetry parameters based on the maximum mutual information coefficient according to a scatter diagram corresponding to the telemetry parameter historical data of the satellite in a normal state, and the incidence relation model determining mode on the telemetry parameter value space comprises the following steps:
a first unit: computing satellite S1First maximum mutual information coefficient between telemetry parameter data and satellite S2Determining a first meshing scheme corresponding to the first maximum mutual information coefficient and a second meshing scheme corresponding to the second maximum mutual information coefficient;
a second unit: satellite S2The telemetering parameter data is put into a first grid division scheme to obtain a second mutual information coefficient;
a third unit: if satellite S2If the difference value between the second mutual information coefficient and the second maximum mutual information coefficient is larger than the rated error, combining the first meshing scheme and the second meshing scheme as a new first meshing scheme;
a fourth unit: traverse all satellites SqRepeating the construction process of the first meshing scheme and taking the final first meshing scheme as an incidence relation model among the telemetry parameters;
a third module: the system is used for calculating mutual information coefficients of the satellite in the normal state and the abnormal state according to scatter diagrams corresponding to the telemetry parameter historical data of the satellite in the normal state and the abnormal state and by combining an incidence relation model between the telemetry parameters and determining distribution of the mutual information coefficients;
a fourth module: the method is used for receiving telemetry parameter data of an orbiting satellite, and calculating mutual information coefficients among telemetry parameters according to an association relation model, wherein the determination mode of the mutual information coefficients among the telemetry parameters comprises the following steps:
a fifth unit: setting a time window [ t, t + L ], acquiring data of the telemetering parameters in the time window [ t, t + L ], recording the data quantity as n
Wherein n ish，lThe number of data in the ith row and the ith column of grids of the grid division scheme is nh，·＝∑lhh，l，n·，l＝∑hhh，lThen based on the time window [ t, t + L ]]Internal telemetry parameter calculation XiAnd XjThe mutual information coefficient is:
wherein, the mesh division scheme is characterized in that the ith row and the ith column of meshes are marked as meshes (h, l);
a sixth unit: for moving the time window to [ t +1, t +1+ L]Let the data point at time t fall into the grid (h)2，l2) In (h), the data at time t +1+ L falls into the grid2，l2) In, then based on the time window [ t +1, t +1+ L]Internal telemetry parameter calculation XiAnd XjThe rule for updating the number of data points in each grid is as follows:
if h1＝h2And l1＝l2Then XiAnd XjThe mutual information coefficient is:
ICt+1＝ICt，
keeping the number of data points in each grid unchanged;
if h1≠h2Or l1≠l2Then XiAnd XjThe mutual information coefficient is:
wherein
nh，·＝∑lhh，l，n·，l＝∑hhh，l
A seventh unit: for updating the number of data points within each grid: grid (h)1，l1) The number of inner data points is
Grid (h)2，l2) The number of inner data points is
Except that (h)1，l1)、(h2，l2) The number of data points in the other grids is not changed;
a fifth module: and the system is used for calculating the Mahalanobis distance according to the mutual information coefficient among the telemetering parameters and the distribution of the mutual information coefficient of the satellite in the normal state and the abnormal state, and judging whether the multi-parameter association state is abnormal or not according to the Mahalanobis distance.
5. The system for monitoring the abnormal state of the orbiting satellite based on the multi-parameter association as claimed in claim 4, wherein the distribution of the mutual information coefficients among the telemetry parameters is a normal distribution, and the elements of the mean vector and the covariance matrix of the distribution are as follows:
wherein Q is the number of satellites,
is X in the normal state of the qth satelliteiAnd XjThe mutual information coefficient of (a) is,
is X in abnormal state of the q-th satelliteiAnd XjThe mutual information coefficient of (2).
6. The system for monitoring the abnormal state of the orbiting satellite based on the multi-parameter association as claimed in claim 5, wherein the determining whether the multi-parameter association state is abnormal according to the mahalanobis distance comprises:
an eighth unit: for setting a time window [ t, t + L]Calculating any set of telemetry parameters XiAnd XjThe mutual information coefficient x of the measured data;
a ninth unit: the method is used for calculating the Mahalanobis distance according to the mutual information coefficient x and the distribution of the telemetry parameter mutual information coefficient:
a tenth unit: for if d1(x)＜d2(x) If the state is normal, setting t as t +1, and continuing to perform real-time detection; if d is1(x)≥d2(x) If the state is abnormal, the real-time detection is continuously carried out after the alarm is set to t + L.
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