CN116305531B - Spacecraft health evolution model modeling method, device, equipment and medium - Google Patents
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Abstract
The invention relates to the technical field of spacecraft monitoring, in particular to a modeling method, a device, equipment and a medium for a spacecraft health evolution model, wherein the method comprises the following steps: acquiring telemetry data of spacecrafts at different times and preprocessing the telemetry data; performing feature extraction based on the preprocessed spacecraft telemetry data; based on all the characteristic quantities, carrying out fusion processing to obtain one-dimensional characteristics corresponding to time; performing curve fitting on the one-dimensional characteristics according to the time sequence to obtain a fitted curve, and taking the fitting value of the fitted curve as a health factor corresponding to time; screening all the characteristic quantities based on the correlation degree between the health factors and the characteristic quantities, and selecting the characteristic quantities with high correlation degree; and constructing a fuzzy neural network, taking the selected characteristic quantity as input and the selected health factor as output, and training the fuzzy neural network to obtain the spacecraft health evolution model. The invention can obtain the evolution model for evaluating the health of the spacecraft.
Description
Technical Field
The invention relates to the technical field of spacecraft monitoring, in particular to a modeling method and device for a spacecraft health evolution model, electronic equipment and a storage medium.
Background
The spacecraft, such as a satellite platform, has complex functional mechanisms and severe operating environment, and is inevitably in-orbit to fail, so that the health state of the spacecraft is monitored, and the method has important significance for ensuring the safe and stable operation of the spacecraft.
At present, in the prior art, the fault evolution rule is generally estimated only by the change rule of a few parameters in the telemetry data of the spacecraft, the internal relation between the multisource high-dimensional data cannot be mined, the dynamic analysis under the conditions of long time sequence and variable working conditions is difficult to realize, and the evolution model is inaccurate and the fault prediction has larger error.
Disclosure of Invention
In order to effectively monitor the health state of a spacecraft, the embodiment of the invention provides a modeling method, a modeling device, electronic equipment and a storage medium of a health evolution model of the spacecraft, which can establish a mapping relation between related characteristic quantities and health factors to obtain the evolution model for evaluating the health of the spacecraft so as to realize measurement and calculation of the health state of the spacecraft.
In a first aspect, an embodiment of the present invention provides a modeling method for a health evolution model of a spacecraft, including:
acquiring telemetry data of spacecrafts at different times, and preprocessing; the spacecraft telemetry data comprise data of a plurality of working parameters of the spacecraft;
performing feature extraction based on the preprocessed spacecraft telemetry data to obtain feature quantities corresponding to all working parameters;
based on all the characteristic quantities, carrying out fusion processing to obtain one-dimensional characteristics corresponding to time;
performing curve fitting on the obtained one-dimensional characteristics according to the time sequence to obtain a fitted curve, and taking the fitting value of the fitted curve as a health factor corresponding to time;
screening all the characteristic quantities based on the obtained correlation degree between the health factors and the characteristic quantities, and selecting the characteristic quantities with high correlation degree;
and constructing a fuzzy neural network, taking the selected characteristic quantity as input and the corresponding health factor as output, and training the constructed fuzzy neural network to obtain a spacecraft health evolution model.
Optionally, the preprocessing includes:
and carrying out noise reduction and outlier rejection processing on the acquired telemetry data of the spacecraft.
Optionally, the feature extraction based on the preprocessed telemetry data of the spacecraft includes:
and respectively calculating the time domain characteristics or the frequency domain characteristics of each working parameter in a time-sharing manner based on the preprocessed spacecraft telemetry data and a preset sliding window.
Optionally, the fusing processing is performed based on all feature quantities to obtain a one-dimensional feature corresponding to time, including:
performing dimension reduction processing on all characteristic quantities of each working parameter to obtain one-dimensional fusion characteristics corresponding to each working parameter and time;
and performing dimension reduction treatment on the one-dimensional fusion characteristics of all the working parameters to obtain one-dimensional characteristics corresponding to time.
Optionally, the dimension reduction process adopts principal component analysis.
Optionally, performing curve fitting on the obtained one-dimensional feature according to time sequence to obtain a fitted curve, including:
based on the one-dimensional characteristics, carrying out standardization processing on the obtained one-dimensional characteristics according to the time sequence change trend to obtain one-dimensional standard characteristics; the change trend comprises an ascending trend and a descending trend;
and performing curve fitting on the one-dimensional standard characteristic by adopting a least square method to obtain a fitted curve.
Optionally, the screening all the feature quantities based on the correlation degree between the obtained health factor and each feature quantity includes:
based on the obtained health factor and all the characteristic quantities, calculating the health factor and the pearson correlation coefficient of each characteristic quantity respectively;
and screening all the characteristic quantities based on a preset correlation coefficient threshold and the pearson correlation coefficient corresponding to each characteristic quantity, and selecting the characteristic quantity with the pearson correlation coefficient larger than the correlation coefficient threshold.
In a second aspect, an embodiment of the present invention further provides a modeling apparatus for a spacecraft health evolution model, including:
the acquisition module is used for acquiring the remote measurement data of the spacecraft at different times and preprocessing the remote measurement data; the spacecraft telemetry data comprise data of a plurality of working parameters of the spacecraft;
the feature extraction module is used for carrying out feature extraction based on the preprocessed spacecraft telemetry data to obtain feature quantities corresponding to all working parameters;
the feature fusion module is used for carrying out fusion processing based on all the feature quantities to obtain one-dimensional features corresponding to time;
the fitting module is used for performing curve fitting on the obtained one-dimensional characteristics according to the time sequence to obtain a fitting curve, and taking the fitting value of the fitting curve as a health factor corresponding to time;
the feature screening module is used for screening all the feature quantities based on the obtained correlation degree between the health factors and the feature quantities, and selecting the feature quantities with high correlation degree;
the model training module is used for constructing a fuzzy neural network, taking the selected characteristic quantity as input and the corresponding health factor as output, and training the constructed fuzzy neural network to obtain the spacecraft health evolution model.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the method for modeling a health evolution model of a spacecraft according to any embodiment of the present specification is implemented.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, on which a computer program is stored, where the computer program, when executed in a computer, causes the computer to execute the modeling method for a health evolution model of a spacecraft according to any embodiment of the present specification.
The embodiment of the invention provides a modeling method, a modeling device, electronic equipment and a storage medium for a spacecraft health evolution model, which are used for acquiring characteristic quantities corresponding to a plurality of working parameters, carrying out fusion and fitting, determining health factors for reflecting the health state of the spacecraft by synthesizing multi-source high-dimensional data, screening each characteristic quantity based on the correlation degree, and establishing a mapping relation between the characteristic quantity with high correlation degree and the health factors based on a fuzzy neural network to obtain the spacecraft health evolution model, and can be used for analyzing the spacecraft evolution process and realizing the measurement and calculation of the health state of the spacecraft.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a modeling method for a health evolution model of a spacecraft according to an embodiment of the invention;
FIG. 2 is a schematic structural diagram of a health evolution model of a spacecraft according to an embodiment of the invention;
FIG. 3 is a hardware architecture diagram of an electronic device according to an embodiment of the present invention;
fig. 4 is a structural diagram of a modeling apparatus for a health evolution model of a spacecraft according to an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
As described above, in the prior art, the fault evolution rule is generally estimated only by using the change rule of a few parameters in the telemetry data of the spacecraft, so that the internal relation between the multisource high-dimensional data cannot be mined, and the dynamic analysis under the conditions of long time sequence and variable working conditions is difficult to realize, so that the evolution model is inaccurate and the fault prediction has a larger error. In view of the above, the invention provides a modeling method for a spacecraft health evolution model, which is used for synthesizing multi-source high-dimensional data, mining the inherent change rule of the data and establishing a more accurate evolution model.
Specific implementations of the above concepts are described below.
Referring to fig. 1, an embodiment of the present invention provides a modeling method for a spacecraft health evolution model, including:
step 100, acquiring telemetry data of spacecrafts at different times, and preprocessing; the spacecraft telemetry data comprise data of a plurality of working parameters of the spacecraft;
in step 100, the plurality of working parameters of the spacecraft may include overall working parameters of the spacecraft, such as current, voltage, and ambient temperature, and may also include working parameters of the core component, such as engine speed, where data of the working parameters may change with time, and different values may be corresponding to different times;
102, performing feature extraction based on the preprocessed spacecraft telemetry data to obtain feature quantities corresponding to all working parameters;
in step 102, feature extraction is performed on the data of each working parameter, and each working parameter can correspond to various feature quantities; the data of the working parameters corresponds to time, and after the characteristic extraction, the data of the characteristic quantity corresponds to time;
104, based on all the feature quantities, carrying out fusion processing to obtain one-dimensional features corresponding to time;
in step 104, all the feature quantities corresponding to the same time are fused to obtain one-dimensional features corresponding to different times;
step 106, performing curve fitting on the obtained one-dimensional features according to the time sequence to obtain a fitted curve, and taking the fitting value of the fitted curve as a health factor corresponding to time;
step 106, fitting according to a plurality of one-dimensional features corresponding to different time to obtain corresponding health factors;
step 108, screening all the characteristic quantities based on the obtained correlation degree between the health factors and the characteristic quantities, and selecting the characteristic quantities with high correlation degree with the health factors;
in the step 108, based on the correlation degree, all the feature quantities are screened, the feature quantity which is more closely related to the health factor is selected, the feature quantity which is less related to the health factor is screened out, the feature which is more related to the health factor can be determined, and the calculated quantity is reduced;
step 110, constructing a fuzzy neural network, and training the constructed fuzzy neural network by taking the selected characteristic quantity as input and the corresponding health factor as output to obtain a spacecraft health evolution model;
in this step 110, the plurality of inputs and outputs corresponding to different times are used as training samples to train the constructed fuzzy neural network, so that the constructed fuzzy neural network learns the mapping relationship between the different time feature quantities and the health factors.
According to the embodiment of the invention, a plurality of working parameters of the spacecraft are obtained for feature extraction and data fusion, the multi-source high-dimensional data are synthesized to determine the health factors for reflecting the state of the spacecraft, various feature quantities closely related to the health factors are determined based on the correlation degree, and the mapping relation between the feature quantities and the health factors is established based on the fuzzy neural network, so that the health evolution model of the spacecraft is obtained. According to the method, the characteristic quantity which is closely related to the health factor can be screened out, the characteristic quantity corresponding to the characteristic quantity which is closely related to the health factor is obtained after preprocessing, characteristic extraction and characteristic screening are carried out on the basis of newly acquired remote measurement data of the spacecraft, the corresponding health factor can be obtained after the characteristic quantity is input into the spacecraft health evolution model, the health state of the spacecraft can be evaluated through the health factor, and the analysis of the spacecraft health evolution process is facilitated by establishing the relationship between the health factor and the time sequence.
The manner in which the individual steps shown in fig. 1 are performed is described below.
Optionally, for step 100, the preprocessing includes:
and carrying out noise reduction and outlier rejection processing on the acquired telemetry data of the spacecraft.
Noise reduction and outlier rejection processing are beneficial to improving the reliability of telemetry data, and are more beneficial to mining deep rules of data change and spacecraft health state evolution.
In some optional embodiments, the acquired telemetry data of the spacecraft may be subjected to outlier rejection processing, and may be subjected to outlier rejection processing by using a 3σ criterion, specifically:
1) For the ith operating parameter x i (i≤I)，x i For vector, including multiple data at different time, the length of sliding window is set to be m, for dividing time period, the starting point of data window is s, the data in the sliding window can be expressed asThe average of all data in the sliding window is calculated as follows:
wherein I is the number of items of all working parameters,for working parameter x in sliding window i Average value of (2);
2) The standard deviation of all data in the sliding window is calculated, namely the following formula:
in the method, in the process of the invention,for working parameter x in sliding window i Standard deviation of (2);
3) If the following formula is satisfied:
then it is determined thatIs separated fromGroup points, and rejecting the data; otherwise determine->Is a normal value; where j represents the jth data point in the window;
4) For data in next sliding windowRepeating the steps 1) -3) until all the data are processed.
It can be understood that the above data refers to different time data corresponding to any one working parameter, and the outlier rejection processing is performed on other working parameters in the same manner.
The data of the spacecraft can fluctuate in the process of collection and transmission, and abnormal outliers contained in the data can be removed by the embodiment, so that a data rule can be better discovered.
Optionally, for step 102, performing feature extraction based on the preprocessed spacecraft telemetry data, including:
and respectively calculating the time domain characteristics or the frequency domain characteristics of each working parameter in a time-sharing manner based on the preprocessed spacecraft telemetry data and a preset sliding window.
The time domain features or the frequency domain features of the working parameters generally have physical significance, and the mapping relation between the performance degradation and the health evolution is easy to establish.
In some optional embodiments, step 102 calculates the time domain feature of each operating parameter separately in time periods based on the preprocessed telemetry data of the spacecraft and a preset sliding window, including calculating one or more of the following time domain features of each operating parameter separately in time periods: maximum, minimum, peak-to-peak, mean, average amplitude, square root amplitude, variance, standard deviation, root mean square, kurtosis, skewness, waveform factor, peak factor, pulse factor, margin factor, kurtosis factor, and clearance factor. For specific calculation methods, reference may be made to the prior art, for example, MATLAB software, which will not be further described herein.
Optionally, step 104 further includes:
performing dimension reduction processing on all characteristic quantities of each working parameter to obtain one-dimensional fusion characteristics corresponding to each working parameter and time;
and performing dimension reduction treatment on the one-dimensional fusion characteristics of all the working parameters to obtain one-dimensional characteristics corresponding to time.
In the above embodiment, all the features of the working parameters under the same time are subjected to dimension reduction fusion, then all the working parameters are subjected to dimension reduction fusion, and then a fitting curve is obtained through fitting, so that the health factor with better monotonicity and trend is obtained.
In some embodiments, the dimension reduction process may use principal component analysis (or principal component analysis), that is, the principal component analysis is first used to reduce the dimension of all feature quantities of each working parameter to obtain one-dimensional fusion features of each working parameter, and then the principal component analysis is used to reduce the dimension of one-dimensional fusion features of all working parameters to obtain one-dimensional features corresponding to time.
The embodiment can extract the data rule which can most reflect the healthy evolution process in the high-dimensional data by twice utilizing the data dimension reduction capability fusion data of principal component analysis.
Of course, other methods, such as singular value decomposition, may be used for the dimension reduction process, and specific limitations and details are not provided herein.
Optionally, in step 106, performing curve fitting on the obtained one-dimensional feature according to time sequence to obtain a fitted curve, including:
based on the one-dimensional characteristics, carrying out standardization processing on the obtained one-dimensional characteristics according to the time sequence change trend to obtain one-dimensional standard characteristics; the change trend comprises an ascending trend and a descending trend;
and performing curve fitting on the one-dimensional standard characteristic by adopting a least square method to obtain a fitted curve.
In the embodiment, the one-dimensional standard features are subjected to standardized processing and curve fitting by adopting a least square method, so that the health factors with good monotonicity and trending property are obtained.
Specifically, for the acquired one-dimensional feature F * Performing standardization processing between (0, 1) to obtain one-dimensional standard featureWherein:
if one-dimensional feature F * If the trend is upward, then:
if one-dimensional feature F * For the downward trend, then:
in the method, in the process of the invention,is F * Maximum value of>Is F * Minimum value of->Is one-dimensional standard feature->I epsilon (1, 2, …, p), p being the length of each feature in the feature extraction process;
in some embodiments, the curve fitting the one-dimensional standard feature by using the least square method may specifically include:
the one-dimensional standard features are fitted in a segmented manner by adopting the following formula:
wherein a, b and c are fitting parameters, t i On-track runtime for the ith parameter in one-dimensional standard featureLong;
assuming that the one-dimensional standard characteristic is fitted in s segments, a of the j (j.ltoreq.s) th segment j Determined by the following formula:
wherein a is j-1 、b j-1 、c j-1 Fitting parameters, t, of the previous section of curve respectively stage_j The corresponding start time of the j-th section;
fitting parameters b for each segment j 、c j Is calculated by the following formula:
wherein n is j The number of data contained in the j-th segment for the one-dimensional standard feature,the value of the one-dimensional standard feature at the stage_j+i-1 time is shown.
Optionally, step 108, screening all feature quantities based on the correlation degree between the obtained health factor and each feature quantity, including:
based on the obtained health factor and all the characteristic quantities, calculating the health factor and the pearson correlation coefficient of each characteristic quantity respectively;
and screening all the characteristic quantities based on a preset correlation coefficient threshold and the pearson correlation coefficient corresponding to each characteristic quantity, and selecting the characteristic quantity with the pearson correlation coefficient larger than the correlation coefficient threshold.
In the above embodiment, the pearson correlation coefficient between the health factor and any feature is calculated to determine the degree of correlation between the health factor and the feature, and the specific calculation process may refer to the prior art, which is not described herein. The correlation coefficient threshold value can be set according to actual needs, and preferably, the number of the finally selected feature quantities with high correlation degree is not less than 6.
Optionally, as shown in fig. 2, the fuzzy neural network constructed in step 110 includes four layers, where the first layer is an input layer, and the number of hidden layers depends on the sum of the numbers of input variables; the second layer is a radial basis function layer, the main function of the second layer is to blur input variables, and the number of hidden layers is mostly designed based on manual experience; the third layer is a regular layer, the main function of the third layer is defuzzification, and the number of hidden layers is the same as that of radial basis function layers; the fourth layer is the output layer whose main function is to calculate the health factor by means of a linear summation.
The spacecraft health evolution model can be specifically described as:
HI(t)＝ω(t)v T (t)
wherein HI (t) is a health factor of t time and is an output result of the evolution model, and ω (t) = [ ω ] 1 (t),ω 2 (t),…,ω l (t),…,ω 10 (t)]Is a weight vector between the rule layer and the output layer, v (t) = [ v ] 1 (t),v 2 (t),…,ω l (t),…,ω 10 (t)]Is the output of the third layer of rules at time t,
wherein v is l (t) is t time rule layer first neuron output, θ j (t) is the jth neuron output of the t-time second radial basis function layer,
wherein x= [ x ] 1 ,x 2 ,…,x n ]For the input of the first layer, n represents the number of neurons of the input layer, c j (t)＝[c 1j (t),c 2j (t),…,c nj (t)]Is the center, sigma, of the jth neuron of the t-time radial basis function layer j (t)＝[σ 1j (t),σ 2j (t),…,σ nj (t)] T Is the width of the jth neuron of the t-time radial basis function layer.
The output of the fourth layer output layer obtained by the weighting factor method is described as follows:
to achieve the model parameters ω (t), c (t) (c (t) = [ c) 1 (t),c 2 (t),…,c 10 (t)]) Sum σ (t) (σ (t) = [ σ) 1 (t),σ 2 (t),…,σ 10 (t)]) The invention designs a second-order L-M-based parameter updating algorithm, which can realize simultaneous adjustment of model parameters, accelerate calculation speed, ensure calculation accuracy of a constructed model, and the updating formula of the model parameters can be expressed as:
Ξ(t+1)＝Ξ(t)+(H(t)+κ(t)I) -1 ·G(t)
wherein: xi (t) is a parameter vector of t time, xi (t) = [ ω (t), c) 1 (t),c 2 (t),…,c 10 (t),σ 1 (t),σ 2 (t),…,σ 10 (t)]H (t) is a Heterophasic pseudolarix matrix, which is calculated by:
H(t)＝J T (t)J(t)
j (t) is the jacobian vector at time t, expressed as:
e (t) is the error of time HI (t), expressed as:
e(t)＝HI′(t)-HI(t)
HI' (t) is the actual output of the model at time t. I is an identity matrix for avoiding a pathological condition in matrix inversion, G (t) is a gradient vector, and the expression is:
G(t)＝J T (t)e(t)
kappa (t) is the self-adaptive learning rate of t time, and is used for improving the convergence rate of the model, and the calculation mode is as follows:
κ(t)＝ξ(t)κ(t-1)
ξ(t)＝(ε min (t)+κ(t-1))(ε max (t)+1)
wherein, xi (t) is the adjustment coefficient of t time learning rate, epsilon min (t) is the minimum eigenvalue of the Heteropany matrix H (t), ε max (t) is the maximum eigenvalue of the Heteropan matrix H (t), 0<ε min (t)<ε max (t)，0<κ(t)<1。
Based on the spacecraft health evolution model and the parameter adjustment strategy based on the self-adaptive second-order L-M algorithm, real-time calculation of health factors can be realized.
As shown in fig. 3 and 4, the embodiment of the invention provides a modeling device for a spacecraft health evolution model. The apparatus embodiments may be implemented by software, or may be implemented by hardware or a combination of hardware and software. In terms of hardware, as shown in fig. 3, a hardware architecture diagram of an electronic device where a modeling apparatus for a health evolution model of a spacecraft provided in an embodiment of the present invention is shown, where the electronic device where the embodiment is located may generally include other hardware, such as a forwarding chip responsible for processing a message, in addition to the processor, the memory, the network interface, and the nonvolatile memory shown in fig. 3. For example, as shown in fig. 4, the device in a logic sense is formed by reading a corresponding computer program in a nonvolatile memory into a memory by a CPU of an electronic device where the device is located. The modeling device for the spacecraft health evolution model provided by the embodiment comprises:
the acquisition module 401 is used for acquiring the telemetry data of the spacecraft at different times and preprocessing the telemetry data; the spacecraft telemetry data comprise data of a plurality of working parameters of the spacecraft;
the feature extraction module 402 is configured to perform feature extraction based on the preprocessed telemetry data of the spacecraft, so as to obtain feature quantities corresponding to all working parameters;
the feature fusion module 403 is configured to perform fusion processing based on all feature amounts, so as to obtain a one-dimensional feature corresponding to time;
the fitting module 404 is configured to perform curve fitting on the obtained one-dimensional feature according to time sequence to obtain a fitted curve, and take a fitting value of the fitted curve as a health factor corresponding to time;
the feature screening module 405 is configured to screen all feature amounts based on the obtained correlation degree between the health factor and each feature amount, and select feature amounts with high correlation degree;
the model training module 406 is configured to construct a fuzzy neural network, and train the constructed fuzzy neural network with the selected feature quantity as input and the corresponding health factor as output to obtain a spacecraft health evolution model.
In an embodiment of the present invention, the obtaining module 401 may be used to perform the step 100 in the above method embodiment, the feature extracting module 402 may be used to perform the step 102 in the above method embodiment, the feature fusion module 403 may be used to perform the step 104 in the above method embodiment, the fitting module 404 may be used to perform the step 106 in the above method embodiment, the feature filtering module 405 may be used to perform the step 108 in the above method embodiment, and the model training module 406 may be used to perform the step 110 in the above method embodiment.
In one embodiment of the present specification, the performing the pretreatment includes:
and carrying out noise reduction and outlier rejection processing on the acquired telemetry data of the spacecraft.
In one embodiment of the present specification, the performing feature extraction based on the preprocessed spacecraft telemetry data includes:
and respectively calculating the time domain characteristics or the frequency domain characteristics of each working parameter in a time-sharing manner based on the preprocessed spacecraft telemetry data and a preset sliding window.
In one embodiment of the present disclosure, the fusing processing is performed based on all feature amounts to obtain a one-dimensional feature corresponding to time, including:
for time, performing dimension reduction processing on all feature quantities of each working parameter to obtain one-dimensional fusion features of each working parameter;
and performing dimension reduction processing on the one-dimensional fusion characteristics of all the working parameters to obtain one-dimensional characteristics corresponding to the time.
In one embodiment of the present description, the dimension reduction process employs principal component analysis.
In one embodiment of the present disclosure, the performing curve fitting on the obtained one-dimensional feature according to time sequence to obtain a fitted curve includes:
based on the one-dimensional characteristics, carrying out standardization processing on the obtained one-dimensional characteristics according to the time sequence change trend to obtain one-dimensional standard characteristics; the change trend comprises an ascending trend and a descending trend;
and performing curve fitting on the one-dimensional standard characteristic by adopting a least square method to obtain a fitted curve.
In one embodiment of the present disclosure, the screening all feature quantities based on the degree of correlation between the obtained health factor and each feature quantity includes:
based on the obtained health factor and all the characteristic quantities, calculating the health factor and the pearson correlation coefficient of each characteristic quantity respectively;
and screening all the characteristic quantities based on a preset correlation coefficient threshold and the pearson correlation coefficient corresponding to each characteristic quantity, and selecting the characteristic quantity with the pearson correlation coefficient larger than the correlation coefficient threshold.
It can be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on a modeling apparatus for a health evolution model of a spacecraft. In other embodiments of the invention, a spacecraft health evolution model modeling apparatus may include more or fewer components than shown, or certain components may be combined, or certain components may be split, or different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
The content of information interaction and execution process between the modules in the device is based on the same conception as the embodiment of the method of the present invention, and specific content can be referred to the description in the embodiment of the method of the present invention, which is not repeated here.
The embodiment of the invention also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the modeling method of the spacecraft health evolution model in any embodiment of the invention when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium is stored with a computer program, when the computer program is executed by a processor, the processor is caused to execute the modeling method for the spacecraft health evolution model in any embodiment of the invention.
Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium may realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present invention.
Examples of the storage medium for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer by a communication network.
Further, it should be apparent that the functions of any of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform part or all of the actual operations based on the instructions of the program code.
Further, it is understood that the program code read out by the storage medium is written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion module connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion module is caused to perform part and all of actual operations based on instructions of the program code, thereby realizing the functions of any of the above embodiments.
It is noted that relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: various media in which program code may be stored, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. The modeling method for the spacecraft health evolution model is characterized by comprising the following steps of:
acquiring telemetry data of spacecrafts at different times, and preprocessing; the spacecraft telemetry data comprise data of a plurality of working parameters of the spacecraft;
performing feature extraction based on the preprocessed spacecraft telemetry data to obtain feature quantities corresponding to all working parameters;
based on all the characteristic quantities, carrying out fusion processing to obtain one-dimensional characteristics corresponding to time;
performing curve fitting on the obtained one-dimensional characteristics according to the time sequence to obtain a fitted curve, and taking the fitting value of the fitted curve as a health factor corresponding to time;
screening all the characteristic quantities based on the obtained correlation degree between the health factors and the characteristic quantities, and selecting the characteristic quantities with high correlation degree;
constructing a fuzzy neural network, taking the selected characteristic quantity as input and the corresponding health factor as output, and training the constructed fuzzy neural network to obtain a spacecraft health evolution model;
the fusion processing is performed based on all the feature quantities to obtain one-dimensional features corresponding to time, and the method comprises the following steps:
performing dimension reduction processing on all characteristic quantities of each working parameter to obtain one-dimensional fusion characteristics corresponding to each working parameter and time;
and performing dimension reduction treatment on the one-dimensional fusion characteristics of all the working parameters to obtain one-dimensional characteristics corresponding to time.
2. The modeling method of a health evolution model of a spacecraft of claim 1, wherein,
the pretreatment comprises the following steps:
and carrying out noise reduction and outlier rejection processing on the acquired telemetry data of the spacecraft.
3. The modeling method of a health evolution model of a spacecraft of claim 1, wherein,
the feature extraction based on the preprocessed spacecraft telemetry data comprises the following steps:
and respectively calculating the time domain characteristics or the frequency domain characteristics of each working parameter in a time-sharing manner based on the preprocessed spacecraft telemetry data and a preset sliding window.
4. The modeling method of a health evolution model of a spacecraft of claim 1, wherein,
and the dimension reduction treatment adopts a principal component analysis method.
5. The modeling method of a health evolution model of a spacecraft of claim 1, wherein,
performing curve fitting on the obtained one-dimensional characteristics according to the time sequence to obtain a fitted curve, wherein the curve fitting comprises the following steps:
based on the one-dimensional characteristics, carrying out standardization processing on the obtained one-dimensional characteristics according to the time sequence change trend to obtain one-dimensional standard characteristics; the change trend comprises an ascending trend and a descending trend;
and performing curve fitting on the one-dimensional standard characteristic by adopting a least square method to obtain a fitted curve.
6. The modeling method of a health evolution model of a spacecraft of claim 1, wherein,
and screening all the characteristic quantities based on the obtained correlation degree between the health factors and the characteristic quantities, wherein the screening comprises the following steps:
based on the obtained health factor and all the characteristic quantities, calculating the health factor and the pearson correlation coefficient of each characteristic quantity respectively;
and screening all the characteristic quantities based on a preset correlation coefficient threshold and the pearson correlation coefficient corresponding to each characteristic quantity, and selecting the characteristic quantity with the pearson correlation coefficient larger than the correlation coefficient threshold.
7. A modeling apparatus for a health evolution model of a spacecraft, comprising:
the acquisition module is used for acquiring the remote measurement data of the spacecraft at different times and preprocessing the remote measurement data; the spacecraft telemetry data comprise data of a plurality of working parameters of the spacecraft;
the feature extraction module is used for carrying out feature extraction based on the preprocessed spacecraft telemetry data to obtain feature quantities corresponding to all working parameters;
the feature fusion module is used for carrying out fusion processing based on all the feature quantities to obtain one-dimensional features corresponding to time;
the fitting module is used for performing curve fitting on the obtained one-dimensional characteristics according to the time sequence to obtain a fitting curve, and taking the fitting value of the fitting curve as a health factor corresponding to time;
the feature screening module is used for screening all the feature quantities based on the obtained correlation degree between the health factors and the feature quantities, and selecting the feature quantities with high correlation degree;
the model training module is used for constructing a fuzzy neural network, taking the selected characteristic quantity as input and the corresponding health factor as output, and training the constructed fuzzy neural network to obtain a spacecraft health evolution model;
the fusion processing is performed based on all the feature quantities to obtain one-dimensional features corresponding to time, and the method comprises the following steps:
performing dimension reduction processing on all characteristic quantities of each working parameter to obtain one-dimensional fusion characteristics corresponding to each working parameter and time;
and performing dimension reduction treatment on the one-dimensional fusion characteristics of all the working parameters to obtain one-dimensional characteristics corresponding to time.
8. An electronic device comprising a memory and a processor, the memory having stored therein a computer program, characterized in that the processor, when executing the computer program, implements the modeling method of the model of the health evolution of a spacecraft as claimed in any one of claims 1-6.
9. A storage medium having stored thereon a computer program, characterized in that the computer program, when executed in a computer, causes the computer to perform the modeling method of the model of the health evolution of a spacecraft according to any one of claims 1-6.
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