CN117395381A

CN117395381A - Compression method, device and equipment for telemetry data

Info

Publication number: CN117395381A
Application number: CN202311698765.2A
Authority: CN
Inventors: 张锐; 师晨光; 徐晓帆; 朱华; 胡思奇; 马二瑞; 施琦
Original assignee: Shanghai Satellite Internet Research Institute Co ltd
Current assignee: Shanghai Satellite Internet Research Institute Co ltd
Priority date: 2023-12-12
Filing date: 2023-12-12
Publication date: 2024-01-12
Anticipated expiration: 2043-12-12
Also published as: CN117395381B

Abstract

The application discloses a method, a device and equipment for compressing telemetry data, and relates to the technical field of data processing. In the method, an image data set is constructed according to various telemetry data in the satellite on-orbit running process, corresponding main frequency sets are respectively constructed, video stream data are constructed aiming at the image data set meeting video construction conditions, and video compression coding is carried out on the video stream data; therefore, the satellite on-orbit application of the image/video compression algorithm is realized, the image/video compression algorithm is fully exerted, the signal with obvious periodicity and relativity has extremely high compression ratio, and the characteristic of signal change detail can be reserved for the signal with abundant local change detail, so that the contradiction between the periodic characteristic of telemetry data and sampling frequency selection is avoided, and the compression efficiency of the telemetry data is improved.

Description

Compression method, device and equipment for telemetry data

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a method, an apparatus, and a device for compressing telemetry data.

Background

The satellite telemetry refers to telemetry of working state parameters of each subsystem of the satellite by a data acquisition module, downloading of acquired telemetry data to a ground station through a telemetry channel, analysis processing of the received telemetry data by the ground station and feedback of useful information to a user, and important data support is provided for completing satellite environment parameters and monitoring of measurement and control tasks, monitoring of working state of each subsystem, analysis of health faults, excavation and the like.

At present, due to the enhancement of satellite communication capability and the diversification of satellite working modes, the data types of telemetry required by a ground station for judging the working state of a satellite are gradually increased, which also causes the situation that the data volume of telemetry data is increased sharply, but in view of the limited channel capacity and bandwidth of telemetry channels, the data compression technology becomes an effective way for improving the transmission efficiency of telemetry data.

In the existing telemetry data compression method used by the satellite, a downsampling mode is generally adopted to realize efficient utilization of telemetry channels (or channels), and in order to ensure that the on-orbit working state of the satellite can be completely reproduced when the ground station analyzes data, the satellite needs to download original telemetry data, so that the satellite mostly stays on a lossless compression algorithm for encoding redundancy of the telemetry data.

However, with the above-mentioned telemetry data compression method, the sampling frequency of telemetry data is low, so that it is difficult to meet the requirement of high-frequency sampling for telemetry data with no apparent periodicity or periodicity (e.g., operation mode switching or local state abnormality), and the data redundancy is still high for telemetry data with apparent periodicity or extremely periodicity (e.g., operation period or operation mode).

It can be seen that there is a need for a method for compressing telemetry data, which avoids the contradiction between the periodic characteristics of the telemetry data and sampling frequency selection, thereby improving the compression efficiency of the telemetry data.

Disclosure of Invention

The embodiment of the application provides a method, a device and equipment for compressing telemetry data, which are used for avoiding contradiction between periodic characteristics of the telemetry data and sampling frequency selection, so that the compression efficiency of the telemetry data is improved.

In a first aspect, embodiments of the present application provide a method for compressing telemetry data, the method comprising:

acquiring various telemetry data of a satellite in the in-orbit operation process, and performing frequency domain analysis on the various telemetry data to acquire a plurality of main frequency sets;

constructing an image data set for the plurality of telemetry data based on the number of dominant frequencies contained by the plurality of dominant frequency sets, respectively;

video stream data is constructed based on the plurality of image data sets, and video compression encoding is performed on the video stream data.

In a second aspect, embodiments of the present application further provide a telemetry data compression apparatus, the apparatus including:

the data acquisition module is used for acquiring various telemetry data in the satellite in-orbit operation process;

The frequency domain analysis module is used for carrying out frequency domain analysis on various telemetry data to obtain a plurality of main frequency sets;

the image construction module is used for constructing image data sets for various telemetry data respectively based on the number of main frequencies contained in the main frequency sets;

and the data coding module is used for constructing video stream data based on a plurality of image data sets and carrying out video compression coding on the video stream data.

In an alternative embodiment, when performing frequency domain analysis on a plurality of telemetry data to obtain a plurality of primary frequency sets, the frequency domain analysis module is specifically configured to:

filtering the plurality of telemetry data to obtain the plurality of telemetry data after processing;

performing frequency domain transformation on the processed various telemetry data to obtain transformed various telemetry data;

and obtaining a main frequency set corresponding to each of the plurality of telemetry data based on the converted frequency spectrum information of the plurality of telemetry data.

In an alternative embodiment, the image construction module is specifically configured to, when constructing the image data set for the plurality of telemetry data based on the number of dominant frequencies comprised by the plurality of dominant frequency sets, respectively:

if a first main frequency set with the number of main frequencies being more than 1 exists in the plurality of main frequency sets, screening N main frequencies from the first main frequency set; wherein N is an integer greater than 0;

Based on the N main frequencies, the telemetry data corresponding to the first main frequency set is divided into N+1 sub telemetry data, and based on the N+1 sub telemetry data, an image data set containing multi-frame image data is constructed.

In an alternative embodiment, the image construction module is specifically configured to, in constructing an image dataset comprising a plurality of frames of image data based on n+1 sub-telemetry data:

dividing N+1 sub-telemetry data into M data frames according to a data segmentation threshold; wherein, the data segmentation threshold characterizes: the data minimum division length of each sub telemetry data, M is an integer greater than 1;

and constructing single-frame image data based on M data frames of the N+1 sub telemetry data respectively, and constructing an image data set containing multi-frame image data based on the obtained multiple single-frame image data.

In an alternative embodiment, after dividing telemetry data corresponding to the first set of primary frequencies into n+1 sub-telemetry data based on N primary frequencies, the image construction module is further configured to:

screening first sub-telemetry data with the largest data quantity from the N+1 sub-telemetry data;

and aligning N sub-telemetry data except the first sub-telemetry data in the N+1 sub-telemetry data by taking the first sub-telemetry data as a reference.

In an alternative embodiment, in aligning N sub-telemetry data other than the first sub-telemetry data of the n+1 sub-telemetry data, the image construction module is specifically configured to:

for the first N-1 sub-telemetry data of the N sub-telemetry data, the following operations are respectively performed:

performing data supplementation on the L < th > sub-telemetry data based on the L < th > -1 < th > sub-telemetry data until the data amount of the L < th > sub-telemetry data after supplementation is the same as that of the first sub-telemetry data; wherein L is an integer greater than 0 and less than N.

In an alternative embodiment, in aligning N sub-telemetry data other than the first sub-telemetry data of the n+1 sub-telemetry data, the image construction module is further configured to:

and adopting a set data supplementing rule to supplement the data of the Nth sub-telemetry data in the N sub-telemetry data until the data quantity of the N sub-telemetry data after supplementation is the same as that of the first sub-telemetry data.

In an alternative embodiment, in constructing the image data set for the plurality of telemetry data based on the number of dominant frequencies comprised by the plurality of dominant frequency sets, respectively, the image construction module is further configured to:

If there is a second main frequency set having a main frequency number of 1 among the plurality of main frequency sets, an image data set including single-frame image data is constructed based on telemetry data corresponding to the second main frequency set.

In an alternative embodiment, after constructing the image data sets for the plurality of telemetry data, respectively, the image construction module is further configured to:

based on the data characteristics of the plurality of telemetry data, carrying out correlation analysis on the plurality of telemetry data to obtain a plurality of data similarities;

and sequencing the data similarities to obtain a similarity arrangement sequence, and taking the similarity arrangement sequence as an image arrangement sequence of the plurality of image data sets.

In an alternative embodiment, the data encoding module is specifically configured to, when constructing video stream data based on a plurality of image data sets:

if a first image data set containing multi-frame image data exists in the plurality of image data sets, constructing video stream data based on the first image data set;

if there is a second image data set including single frame image data and having an image data amount smaller than the data amount threshold value among the plurality of image data sets, video stream data is constructed based on the second image data set.

In an alternative embodiment, the data encoding module is further configured to:

and if a third image data set which contains single-frame image data and has the image data amount not smaller than the data amount threshold value exists in the plurality of image data sets, performing image compression coding on the third image data set.

In a third aspect, the present application provides a data compression device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program being for:

In an alternative embodiment, the processor is specifically configured to:

In an alternative embodiment, the processor is further configured to:

In an alternative embodiment, the processor is specifically configured to:

In an alternative embodiment, the processor is further configured to:

In an alternative embodiment, the processor is specifically configured to:

from the plurality of image data sets, a first image data set containing a plurality of frames of image data exists, and video stream data is constructed based on the first image data set;

In an alternative embodiment, the processor is further configured to:

In a fourth aspect, the present application provides a data decompression apparatus comprising a memory, a processor and a computer program stored on the memory and executable by the processor, the processor when executing the computer program being configured to:

Receiving compressed data from a data compression device; wherein compressing the data comprises: video stream data after video compression encoding and/or single-frame image data after image compression encoding;

and decoding and decompressing the compressed data to obtain corresponding telemetry data.

In a fifth aspect, the present application provides a computer readable storage medium having a computer program stored therein, which when executed by a processor, performs the steps of a method of compressing telemetry data of the first aspect.

In a sixth aspect, the present application provides a computer program product which, when invoked by a computer, causes the computer to perform the steps of the telemetry data compression method of the first aspect.

The beneficial effects of the application are as follows:

in the telemetry data compression method provided by the embodiment of the application, an image data set is constructed according to various telemetry data in the satellite in-orbit operation process, corresponding main frequency sets are respectively constructed, video stream data is constructed aiming at the image data set meeting video construction conditions, and video compression encoding is carried out on the video stream data; therefore, the satellite on-orbit application of the image/video compression algorithm is realized, the image/video compression algorithm is fully exerted, the signal with obvious periodicity and relativity (namely, the telemetry data with low sampling frequency requirement) has extremely high compression ratio, and the characteristic of signal variation details can be reserved for the signal with abundant local variation details (namely, the telemetry data with high sampling frequency requirement), so that the problem that in the related art, the requirement of high-frequency sampling is difficult to meet for telemetry data with insignificant periodicity or without periodicity, and the problem that the data redundancy is still extremely high for telemetry data with significant periodicity or extremely periodic periodicity is avoided, namely, in the way, the contradiction between the periodic characteristics of telemetry data and sampling frequency selection is avoided, and the compression efficiency of telemetry data is improved.

Furthermore, other features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the description of the embodiments are briefly described below, and it is obvious that the drawings described herein are used to provide further understanding of the present application and constitute a part of the present application and are not meant to be unduly limiting. In the drawings:

fig. 1 is a schematic view of an application scenario of a satellite communication system according to an embodiment of the present application;

FIG. 2 is a schematic flow chart of an implementation of a method for compressing telemetry data according to an embodiment of the present application;

fig. 3 is a schematic flow chart of a method for obtaining a primary frequency set according to an embodiment of the present application;

FIG. 4 is a schematic diagram of data after SSA processing according to an embodiment of the present disclosure;

fig. 5 is a specific schematic diagram of a main frequency distribution according to an embodiment of the present application;

Fig. 6 is a schematic diagram of single-frame image data according to an embodiment of the present application;

fig. 7 is a schematic diagram of a specific application scenario of data alignment according to an embodiment of the present application;

fig. 8 is a schematic diagram of aligned data based on DTW data according to an embodiment of the present application;

fig. 9 is a schematic arrangement diagram of single-frame image data according to an embodiment of the present application;

FIG. 10 is a schematic diagram of logic for determining a target image data set according to an embodiment of the present disclosure;

fig. 11 is a schematic structural diagram of a hardware compression module according to an embodiment of the present application;

FIG. 12 is a flow chart of a method for compressing telemetry data according to an embodiment of the present application;

fig. 13 is a schematic diagram of a data transmission flow of measurement and control processing on a satellite according to an embodiment of the present application;

fig. 14 is a schematic flow chart of a method for ground decompression data according to an embodiment of the present application;

FIG. 15 is a schematic structural diagram of a telemetry data compression device according to an embodiment of the present application;

fig. 16 is a schematic structural diagram of a data compression device according to an embodiment of the present application.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the technical solutions of the present application, but not all embodiments. All other embodiments, which can be made by a person of ordinary skill in the art without any inventive effort, based on the embodiments described in the present application are intended to be within the scope of the technical solutions of the present application.

It should be noted that "a plurality of" is understood as "at least two" in the description of the present application. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. A is connected with B, and can be represented as follows: both cases of direct connection of A and B and connection of A and B through C. In addition, in the description of the present application, the words "first," "second," and the like are used merely for distinguishing between the descriptions and not be construed as indicating or implying a relative importance or order.

In addition, in the technical scheme, the data are collected, transmitted, used and the like, and all meet the requirements of national related laws and regulations.

Some technical terms in the embodiments of the present application are explained below to facilitate understanding by those skilled in the art.

(1) Lossy compression: the characteristic of insensitivity to certain frequency components in images or sound waves is utilized, so that certain information is allowed to be lost in the compression process; while the original data cannot be fully restored, the lost portion has reduced impact on understanding the original image, but has replaced with a much larger compression ratio.

(2) Lossless compression: the compression is performed by using the statistical redundancy of the data, so that the original data can be completely restored without causing any distortion, but the compression rate is limited by the theory of the statistical redundancy of the data, and is generally 2:1 to 5:1. such methods are widely used for compression of text data, programs and image data for special applications.

Therefore, the lossy compression is to reduce the audio sampling frequency and bit rate, and the output audio file is smaller than the original file; the lossless compression can compress the volume of the audio file to be smaller on the premise of 100% storage of all data of the original file, and the compressed audio file is restored to achieve the same size and the same code rate as the source file.

(3) A field programmable gate array (Field Programmable Gate Array, FPGA): is a further development product based on programmable devices such as programmable array logic (Programmable Array Logic, PAL), generic array logic (Generic Array Logic, GAL) and the like.

The programmable device is used as a semi-custom circuit in the field of application specific integrated circuits (Application Specific Integrated Circuit, ASIC), which not only solves the defect of custom circuits, but also overcomes the defect of limited gate circuits of the original programmable device.

(4) System On Chip (SOC) for short: which may also be referred to as a system-on-chip or chip-on-chip, is a product that is an integrated circuit with dedicated targets that contains the entire system and has embedded software. It is also a technique to achieve the whole process from determining the system functions, to software/hardware partitioning, and to complete the design.

(5) Empirical mode decomposition (Empirical Mode Decomposition, EMD): the method can also be called empirical mode analysis, is a novel self-adaptive signal time-frequency processing method, and is particularly suitable for the analysis processing of nonlinear non-stationary signals.

(6) Variational modal decomposition (Variational Mode Decomposition, VMD): the method is characterized in that in the process of obtaining the decomposition components, the frequency center and the bandwidth of each component are determined by iteratively searching the optimal solution of the variation model, so that the frequency domain subdivision of the signal and the effective separation of each component can be adaptively realized.

(7) Principal component analysis (Principal Component Analysis, PCA): also called principal component analysis, aims to convert multiple indices into a few comprehensive indices by using the idea of dimension reduction, often used to reduce the dimension of a dataset, while maintaining the features of the dataset that contribute most to the difference. This is done by retaining the lower order principal components and ignoring the higher order principal components. Such low order components tend to preserve the most important aspects of the data. However, this is not necessarily the case, depending on the particular application.

(8) Singular spectrum analysis (Singular Spectrum Analysis, SSA): the method is a method for researching nonlinear time series data, a track matrix is constructed according to an observed time series, then the track matrix is decomposed and reconstructed, and signals (such as long-term trend signals, periodic signals, noise signals and the like) representing different components of an original time series are extracted, so that the structure of the time series is analyzed and can be further predicted.

(9) Dynamic time warping (Dynamic Time Warping, DTW): according to the principle of distance nearest, constructing the corresponding relation of two sequence elements with different lengths, and evaluating the similarity of the two sequences. When constructing the correspondence of two sequence elements, it is necessary to extend or compress the sequence.

(10) DTW centroid averaging algorithm (DTW Barycenter Averaging, DBA): the time sequence averaging algorithm based on the DTW can be used for calculating the average form of a plurality of time sequences, so that the average result is more accurate. The basic idea is as follows: and performing DTW alignment on each time sequence, and then performing average calculation on the aligned sequences. In order to avoid the problem of 'average result deviation' in the process of average calculation, the DTWBA algorithm introduces a concept of 'barycentric point', namely, when the average calculation is carried out, the point of each sequence is mapped onto the average sequence to obtain the average sequence containing all sequence characteristics, and then the DTW alignment is carried out on the average sequence until the average sequence converges.

Further, based on the above nouns and related term explanations, the following briefly describes the design concept of the embodiments of the present application:

the satellite telemetry is to collect/telemeter working state parameters of each subsystem of the satellite by a data collection module, download the collected telemetry data through a telemetry channel, and feed back useful information after analysis and processing of the telemetry data by a ground station to a user, thereby providing important data support for completing satellite environment parameters and measurement and control task monitoring, each subsystem working state monitoring, health fault analysis and mining.

As the number of satellite loading single machines is increasingly complex and the working modes are various, the satellite telemetry parameters required by the ground (station) for accurately judging the working state of the satellite are also more and more, and the telemetry data total amount is increased. But the channel capacity and bandwidth for telemetry data transmission are limited, and data compression techniques become an essential way to improve transmission efficiency.

The high-efficiency data compression method can effectively reduce the energy consumption of the data transmission equipment, and can download telemetry data with higher sampling rate and larger data volume on the premise of original capacity and bandwidth, and meanwhile, the data compression is more than 3 times, so that the signal-to-noise ratio of a telemetry channel can be effectively improved by more than 3 dB.

Moreover, because the compression algorithm is limited by the computing capacity of the satellite-borne equipment and is not widely applied on the track due to the consideration of technical maturity, the remote measurement processing generally adopts a downsampling mode to realize the efficient utilization of a remote measurement channel.

In addition, since telemetry data needs to be downloaded to ensure that the on-orbit working state of a satellite is completely reproduced when the data is analyzed on the ground, related strategy research on telemetry data compression is mostly stopped on lossless compression algorithms based on encoding for telemetry data redundancy, and exemplary common algorithms include: huffman coding, arithmetic coding, run-length coding, LZ series coding, and the like.

Further, the telemetry data of the satellite (namely satellite telemetry signals) represents important information such as the working state, health state and the like of each single machine and system during the in-orbit operation of the satellite; the satellite has strong periodicity in running period, working mode and the like, and when the satellite works normally, the autocorrelation and the cross correlation of a large amount of telemetry are obvious, a large amount of data is 'useless information', the data redundancy is high, and a telemetry channel is occupied; the demand for high-frequency sampling of telemetry signals is particularly strong in ground analysis when the satellite has the phenomena of working mode switching, local state abnormality and the like.

It can be seen that this brings a significant pair of contradictions to sampling rate selection of on-board telemetry signals (i.e. telemetry data), namely that it is difficult to meet the requirement for high frequency sampling for telemetry data that is not obvious or periodic (e.g. operating mode switching or local state anomalies), and that the data redundancy can still be high for telemetry data that is obvious or very periodic (e.g. operating cycle or operating mode).

In view of this, in the embodiment of the present application, since the image/video compression algorithm mature on the ground can just effectively solve the contradiction, the compression ratio for the signals with obvious periodicity and correlation is extremely high, and the signal variation details can be kept for the signals with abundant local variation details, that is, the compression ratio is relatively low, the method for compressing telemetry data is provided, which specifically includes: acquiring various telemetry data of a satellite in an in-orbit running process; each telemetry data is satellite data obtained from one telemetry dimension according to a set sampling frequency for the telemetry of a corresponding satellite; then, carrying out frequency domain analysis on various telemetry data to obtain a plurality of main frequency sets; wherein each primary frequency characterizes: in the corresponding sampling period, the frequency of satellite remote measurement is acquired; further, constructing image data sets for the plurality of telemetry data, respectively, based on the number of primary frequencies contained in the plurality of primary frequency sets; finally, constructing video stream data based on a plurality of image data sets, and performing video compression coding on the video stream data; in this way, the contradiction between the periodic characteristics of the telemetry data and the sampling frequency selection is avoided, and the compression efficiency of the telemetry data is improved.

Referring to fig. 1, an application scenario schematic diagram of a satellite communication system provided in an embodiment of the present application includes: a satellite 101 and ground stations (102 a,102 b), and information interaction between the satellite 101 and the ground stations (102 a,102 b) may be via a wireless communication network.

Illustratively, the satellite 101 may access the network for communication with ground stations (102 a,102 b) via cellular mobile communication technology, such as, for example, fifth generation mobile communication (5th Generation Mobile Networks,5G) technology.

The number of communication devices involved in the application scenario is not limited, for example, a plurality of ground stations may be used, or there may be no ground station, or other network/communication devices may be included, as shown in fig. 1, and only satellite 101 and ground stations (102 a,102 b) are described as an example; further, the satellite 101 has a data compression device 103 disposed thereon, and the ground stations (102 a,102 b) have data decompression devices, namely, a data decompression device 104a and a data decompression device 104b disposed thereon.

The data compression device 103 deployed on the satellite 101 is configured to acquire multiple telemetry data in an in-orbit operation process of the satellite 101, perform frequency domain analysis on the multiple telemetry data, obtain a plurality of main frequency sets corresponding to the multiple telemetry data, that is, obtain a plurality of main frequency sets, respectively construct an image data set for the multiple telemetry data based on a main frequency number contained in the plurality of main frequency sets (each), if a target image data set meeting a video construction condition exists in the plurality of image data sets, construct video stream data based on the target image data set, perform video compression encoding on the video stream data, and if an image data amount of an image data set containing single frame image data is not less than a data amount threshold, perform image compression encoding on the single frame image data, and send the video stream data after video compression encoding and/or the single frame image data after image compression encoding to the data decompression device; wherein each telemetry data is satellite data obtained from one telemetry dimension according to a set sampling frequency for a respective satellite telemetry, each primary frequency being characterized by: in the corresponding sampling period, the frequency of satellite remote measurement is acquired, and the video construction condition is characterized: the respective image data set comprises a plurality of frames of image data, or the respective image data set comprises a single frame of image data, and the amount of image data is less than the data amount threshold.

Note that, the single-frame image data, that is, the information included in the single-frame image, so in the embodiment of the present application, the single-frame image data and the single-frame image refer to the same thing, and no distinction is required.

Data decompression devices (104 a,104 b) disposed on the ground stations (102 a,102 b) for receiving the compressed data from the data compression devices 103 and decoding and decompressing the compressed data to obtain corresponding telemetry data; wherein compressing the data comprises: video stream data after video compression encoding and/or single frame image data after image compression encoding.

For example, assuming that the compressed data is video stream data after video compression encoding, video decoding and decompression can be performed on the video stream data after video compression encoding to obtain corresponding telemetry data; similarly, assuming that the compressed data is single-frame image data after image compression and encoding, image decoding and decompression can be performed on the single-frame image data after image compression and encoding to obtain corresponding telemetry data.

It should be noted that, in the above application scenario, the satellite 101 may not be provided, that is, the data compression device 103 may be widely applied to various spacecrafts needing to perform data compression, such as communication, navigation, remote sensing satellites, constellations, and the like.

The method for compressing telemetry data provided in exemplary embodiments of the present application will be described below in conjunction with the above application scenario, and with reference to the accompanying drawings, it being noted that the above application scenario is merely illustrated for ease of understanding the spirit and principles of the present application, and embodiments of the present application are not limited in any way in this respect.

Referring to fig. 2, which is a schematic implementation flow chart of a telemetry data compression method provided in an embodiment of the present application, an execution body uses a data compression device as an example, and a specific implementation flow chart of the method is as follows:

s201: various telemetry data of the satellite in-orbit operation is acquired.

Wherein the plurality of telemetry data characterizes: important information such as the working state, health state and the like of each single machine and the system during the satellite in-orbit operation, and each telemetry data is obtained from one telemetry dimension according to a set sampling frequency aiming at the corresponding satellite telemetry.

And, the telemetry dimension may be: any one of three directions (i.e., X, Y and Z directions) in a standard coordinate system or a world coordinate system; the satellite telemetry may be: temperature, steering, etc. are parameters related to the satellite operating conditions.

For example, taking actual telemetry data of a certain in-orbit satellite as an example, satellite data in three directions X, Y and Z are respectively acquired, and basic parameters of telemetry signals are shown in the following table 1:

TABLE 1

It should be noted that, the data compression device is periodic to acquire telemetry data of the satellite, and discrete data acquisition is performed in each sampling period (for example, an orbit period), that is, acquisition is performed for a plurality of telemetry measurements of the satellite according to a set sampling frequency.

By way of example, assuming a sampling period of 10 seconds of telemetry data acquisition every 1 minute and a sampling frequency of 30 frames/second, a data compression device may acquire 300 frames of telemetry data for one telemetry during one sampling period.

S202: and carrying out frequency domain analysis on the plurality of telemetry data to obtain a plurality of main frequency sets.

Wherein each primary frequency characterizes: and in the corresponding sampling period, the frequency of satellite remote measurement is acquired.

In an alternative embodiment, when step S202 is performed, the data compression device may perform frequency domain analysis on the multiple telemetry data after obtaining the multiple telemetry data, that is, convert the signal from the time domain to the frequency domain, so as to obtain, according to the spectrum information, a main frequency set corresponding to each of the multiple telemetry data, and referring to fig. 3, a specific implementation procedure is as follows:

S301: and filtering the plurality of telemetry data to obtain the plurality of telemetry data after processing.

For example, in the step S301, since the telemetry data generally includes various frequency components, external interference, system noise, sampling quantization error, etc., when determining the main repetition period (i.e., the determination of the main frequency) of the telemetry data, the main trend and frequency of the telemetry data need to be analyzed after eliminating the influence of the interference, noise, etc.; it should be noted that, for convenience of description and understanding, the definition of telemetry data is that the result of the corresponding operation on telemetry data is characterized.

Therefore, the data compression device can adopt algorithms such as EMD, VMD, PCA, SSA and the like to realize the filtering treatment (namely preprocessing such as 'wild picking', 'filtering') of the telemetry data, obtain main trend and frequency items of the telemetry data, reconstruct the telemetry data and lay a foundation for subsequent main frequency analysis.

For example, the data compression device filters telemetry data based on SSA algorithm to obtain principal components characterizing data characteristics (of the telemetry data), as shown in fig. 4, where solid bars represent portions of real telemetry data and dashed bars represent principal component information of telemetry data obtained after filtering portions of telemetry data.

S302: and performing frequency domain transformation on the processed various telemetry data to obtain transformed various telemetry data.

Illustratively, in executing step S302, after obtaining the processed telemetry data, the data compression device may perform a time-frequency characteristic analysis on the processed telemetry data by using a set time-frequency transformation algorithm, so as to obtain transformed telemetry data.

The set time-frequency transformation algorithm includes, but is not limited to: wavelet, short-time fourier transform, fast fourier transform (Fast Fourier Transform, FFT), etc.

S303: and obtaining a main frequency set corresponding to each of the plurality of telemetry data based on the converted frequency spectrum information of the plurality of telemetry data.

Illustratively, when executing step S303, the data compression device may obtain, after obtaining the transformed multiple telemetry data, a main frequency set corresponding to each of the multiple telemetry data according to spectrum information (i.e. an immediate frequency transformation result) of the transformed multiple telemetry data and an algorithm such as a combination cluster; wherein each set of dominant frequencies comprises: a primary frequency or multiple primary frequencies.

It should be noted that a main frequency (corresponding frequency information) may also be understood as a frequency characteristic; in addition, the data compression device can also sort the main frequencies (i.e. frequency characteristics) in the main frequency set according to the amplitude, and reasonably select a certain number of main frequencies (such as one main frequency or two main frequencies) to be used as the basis for the subsequent two-dimensional image construction.

For example, based on the method steps described in steps S302 to S303, referring to fig. 5, the data compression device performs frequency domain analysis on the principal component signal (i.e. the processed multiple telemetry data) by using FFT, so as to obtain the spectrum information (i.e. the frequency conversion result/video distribution characteristic) of the converted multiple telemetry data, and may select a suitable frequency (i.e. the principal frequency) as a division point in combination with requirements of the final imaging resolution, the video frame rate, and the like, so as to perform data division on the telemetry data having at least two principal frequencies.

S203: image data sets are constructed for the plurality of telemetry data based on the number of primary frequencies contained in the plurality of primary frequency sets, respectively.

In an alternative embodiment, after obtaining the plurality of main frequency sets, the data compression device may construct the image data set of the telemetry data according to the image data constructing modes respectively set for the number of main frequencies being 1 or greater than 1, specifically as follows:

case 1: if a first main frequency set with the number of main frequencies being greater than 1 exists in the plurality of main frequency sets, the data compression equipment screens out N main frequencies from the first main frequency set, so that telemetry data corresponding to the first main frequency set are divided into N+1 sub-telemetry data based on the N main frequencies, and an image data set containing multi-frame image data is constructed based on the N+1 sub-telemetry data.

Wherein N is an integer greater than 0, and the N main frequencies may be: a plurality of main frequencies with maximum amplitudes corresponding to the main frequencies; for example, the two main frequencies with the largest amplitude (or the first two main frequencies with the amplitude ranging from large to small) corresponding to the main frequencies may be used as the N main frequencies to be screened.

In an alternative embodiment, when constructing an image data set including multi-frame image data based on n+1 sub-telemetry data, the data compression device may divide the n+1 sub-telemetry data into M data frames according to a data division threshold, thereby constructing single-frame image data based on M data frames of the n+1 sub-telemetry data, respectively, and construct an image data set including multi-frame image data based on the obtained plurality of single-frame image data, wherein M is an integer greater than 1; optionally, the data segmentation threshold characterizes: a data minimum partition length for each sub-telemetry data; by adopting the mode, the sub-telemetry data is divided into a plurality of data frames through the data segmentation threshold value, and a foundation is laid for the generation of two-dimensional single-frame image data.

Illustratively, assume that the sampling frequency of the source signal (i.e., telemetry data) for a certain telemetry parameter (i.e., satellite telemetry) within a sampling period (e.g., orbital period) is f(Hz) with a sampling period ofO _p (s) the telemetry parameter has a data size ofO _p* fAnd, if the minimum signal division length (i.e. the data division threshold) isnThe data in one sampling period is divided intoO _p* f/nGroups of frames (i.e., data frames), each group of data (i.e., sub-telemetry data) is rearranged in a horizontal arrangement as shown in FIG. 6n，O _p* f/n]And the array of the size can construct single-frame image data.

Obviously, a single-channel telemetry signal (namely telemetry data) is divided according to the method, and a series of data frames with the same length can be obtained, so that the arrangement of the single-channel telemetry signal is realized aiming at the time-frequency characteristic and the autocorrelation of satellite telemetry data; in addition, since the single-frame image data is constructed based on M data frames of n+1 sub-telemetry data, respectively, and the single-frame image data is constructed for one sampling period, the time span is short, and thus, it can be regarded as a single-frame image (data) constructed in a fast-varying period.

Further, the data compression apparatus stores the telemetry signal (i.e., sub-telemetry data) of each frequency period (i.e., sampling period) as one frame of video according to the frequency characteristic (i.e., main frequency), and it is required to keep the size of the image data formed for each sampling period uniform, but in actual engineering, the signal (sub-telemetry data) of the entire sampling period cannot be accurately acquired, and in order to keep the size of the image data corresponding to each sampling period uniform, it is required to perform data alignment for the sub-telemetry data.

In an alternative embodiment, the data compression device may further screen out the first sub-telemetry data with the largest data amount from the n+1 sub-telemetry data after dividing the telemetry data corresponding to the first main frequency set into the n+1 sub-telemetry data, so that the data alignment is performed on N sub-telemetry data except for the first sub-telemetry data in the N sub-telemetry data based on the first sub-telemetry data.

Specifically, for the first N-1 sub-telemetry data of the N sub-telemetry data, the following operations are performed, respectively: performing data supplementation on the L < th > sub-telemetry data based on the L < th > -1 < th > sub-telemetry data until the data amount of the L < th > sub-telemetry data after supplementation is the same as that of the first sub-telemetry data; wherein L is an integer greater than 0 and less than N; for an nth sub-telemetry data of the N sub-telemetry data, performing the following operations: adopting a set data supplementing rule to supplement data of the N sub-telemetry data until the data quantity of the N sub-telemetry data after supplementing is the same as that of the first sub-telemetry data; in this way, the first sub-telemetry data with the largest data volume is used as a template, and the alignment of other sub-telemetry data and the template can be realized, so that the consistency of the sizes of the subsequent single-frame image data is ensured.

For example, the preset data supplementing rule may be: performing data complement '0' operation on the Nth sub-telemetry data (namely the sub-telemetry data acquired in the last sampling period) in the N sub-telemetry data until the data quantity of the N sub-telemetry data after complement is the same as that of the first sub-telemetry data; in the embodiment of the present application, the preset data supplementing rule is not specifically limited, so long as the data information of the nth (i.e., last) sub-telemetry data itself is not affected.

Referring to fig. 7, the sampling period is an orbit period, and the rule of data alignment is that sub-telemetry data with the maximum data amount in the sampling period is used as a template, and other sub-telemetry data are aligned with the template; as shown in fig. 7, the other sub-telemetry data shortage part may be deficient according to the sub-telemetry data corresponding to the next sampling period adjacent thereto; it should be noted that, when the data alignment rule is used to perform data alignment, information will have a certain redundancy.

Specifically, the data compression device can adopt modes of DTW, DBA or autocorrelation analysis and the like to realize data alignment and arrangement of each sub telemetry data; illustratively, referring to fig. 8, the sub-telemetry signals are aligned using the DTW method due to a certain offset between the different signal frames that make up the video frame.

Case 2: if a second main frequency set with the main frequency number of 1 exists in the plurality of main frequency sets, the data compression device constructs an image data set containing single-frame image data based on telemetry data corresponding to the second main frequency set.

It should be noted that, in the process of constructing an image data set including single-frame image data based on telemetry data corresponding to the second main frequency set by the data compression device, the process of constructing an image data set including multi-frame image data is substantially the same as that of constructing a plurality of sub-telemetry data corresponding to the first main frequency set, that is, the data compression device may divide telemetry data corresponding to the second main frequency set into a plurality of data frames according to the above data division threshold, so that an image data set including single-frame image data is constructed based on the obtained plurality of data frames, and data alignment is also required in the process, so that an exemplary detailed description is omitted herein.

By adopting the mode, the content contained in each single-frame image data is telemetry data in one sampling period, so that the consistency of the size of each single-frame image data is ensured to a certain extent, the problem that the single-frame image data is easy to be abnormal due to larger data quantity when compression coding is carried out due to the fact that only one single-frame image data is generated due to the fact that the telemetry data with a plurality of main frequencies is avoided.

In an alternative embodiment, after constructing the image data sets for the plurality of telemetry data respectively, the data compression device may further perform correlation analysis on the plurality of telemetry data based on data features of the plurality of telemetry data to obtain a plurality of data similarities, thereby sorting the plurality of data similarities to obtain a similarity arrangement sequence, and using the similarity arrangement sequence as an image arrangement sequence of the plurality of image data sets; in this way, when the images are arranged, the images are arranged according to the similarity strength sequence (namely the similarity arrangement sequence) of each channel (namely various telemetry data), so that the local similarity of the reconstructed 'images' is ensured, and a sequencing scheme is provided for a subsequent compression algorithm.

It should be noted that, for telemetry signals (i.e., multiple telemetry data) of different channels, the data compression device may obtain the similarity arrangement sequence by adopting a multi-channel signal (multiple telemetry data) correlation analysis based on extreme gradient lifting (eXtreme Gradient Boosting, XGBoost) or cross-correlation analysis; therefore, aiming at the cross correlation of different telemetry signals, multichannel telemetry signals are arranged, the compression coding characteristic of an image/video compression algorithm is played to the maximum extent, and the purpose of efficiently compressing the telemetry signals is achieved.

By way of example, assuming that telemetry data a (a 11, a16, a21, a26, a31, a36, a41, and) and telemetry data B (B11, B13, B21, B23, B31, B33, and) are two types of telemetry data adjacent in similarity arrangement, the telemetry data a and the telemetry data B are a corresponding single-frame image data arrangement schematic diagram, referring to fig. 9.

S204: video stream data is constructed based on the plurality of image data sets, and video compression encoding is performed on the video stream data.

In an alternative embodiment, in performing step S204, if there is a first image data set including a plurality of frames of image data in the plurality of image data sets, constructing video stream data based on the first image data set; if there is a second image data set including single-frame image data and having an image data amount smaller than the data amount threshold value, the video stream data is constructed based on the second image data set, that is, the video stream data is constructed for both of the plurality of image data sets, and then video compression encoding is performed.

Thus, referring to fig. 10, assuming that the target image data set is a first image data set including multi-frame image data, video stream data may be constructed according to a plurality of single-frame image data (i.e., multi-frame image data) included in the target image data set; thus, since the multi-frame image data is constructed for a plurality of sampling periods, this time span is long for one video stream data, and thus it can be regarded as a slow-change period constructed video (data stream); further, assuming that the target image data set is a second image data set including single-frame image data and having an image data amount smaller than the data amount threshold, video stream data may be constructed based on the plurality of target image data sets, so that a video (data stream) construction of a multi-telemetry channel is realized, that is, a plurality of single-frame image data having a smaller image data amount are constructed as video stream data.

It should be noted that, the above image data set includes multiple frames of image data, and the image data set includes a single frame of image data, and the amount of image data is smaller than the threshold value of the amount of data, which may also be referred to as a video construction condition, that is, when it is determined that the image data set satisfies the video construction condition, the construction of video stream data can be performed for the image video data.

Optionally, if a third image data set containing single frame image data and having an image data amount not smaller than the data amount threshold exists in the plurality of image data sets, performing image compression encoding on the third image data set; in this way, the single-frame image data with larger image data volume can be directly subjected to image compression coding, and the single-frame image data after the image compression coding is directly sent to a ground station or data decompression equipment so as to restore telemetry data of satellites.

Based on the method for compressing telemetry data recorded in steps S201 to S204, the embodiment of the present application may be suitable for compression transmission scenarios of various satellite telemetry signals running on orbit, and the specific design of the data compression device may be application layer software based on an operating system or a hardware compression module implemented by adopting an FPGA/SOC, where, referring to fig. 11, it is a schematic structural diagram of a hardware compression module provided in the embodiment of the present application; also, referring to fig. 12, the hardware compression module may implement the following telemetry data compression method flow:

s1201: starting.

S1202: raw data filtering, principal component analysis, and reconstruction of the signal.

The original data are various telemetry data acquired in the satellite in-orbit operation process, and the reconstructed signals are various telemetry data after frequency domain transformation.

S1203: the dominant frequency of the signal is calculated.

S1204: judging the number of the main frequencies, if the number is larger than 1, turning to step S1205; if 1, the process proceeds to step S1210.

S1205: a plurality of dominant frequencies.

S1206: at least two dominant frequencies are selected.

Illustratively, in performing step S1206, the hardware compression module may select 2 dominant frequencies.

S1207: the data is aligned.

S1208: the fast-varying period constructs a single frame image.

For example, in executing step S1208, it is assumed that each telemetry parameter is divided according to 200 data intervals, rearranged according to columns, and then three different telemetry parameters are rearranged according to rows, so as to obtain a single frame image of 431×600 (png format), where the number of bits of the corresponding original data of each image is 431×600= 258.6kB, the number of bits of the data after image compression is 60.03kB, and the compression rate is 23.21%.

S1209: the slow-varying period constructs the video.

Illustratively, when step S1209 is performed, the video stream is written in the order of storing the images, so as to form a video file, and the lower graph intercepts the video part screenshot to represent the constructed video file. If 11 single-frame images are formed in total after the compression in the previous step S1208, the total bit number of the images is 660.22kB, the total bit number of the compressed video file is 559kB, and the compression rate is 84.66%.

Obviously, based on the two-step compression method described in the steps S1208-S1209, the total compression rate of the telemetry data is 19.65%, and the telemetry data is efficiently compressed; after step S1209, the hardware compression module proceeds to step S1204a to perform video compression encoding.

S1210:1 dominant frequency.

S1211: the data is aligned.

S1212: a single frame image is constructed.

After executing step S1212, the hardware compression module may perform the method steps of steps S1213 to S1214a, or may perform the method step of step S1214 b; specifically, the determination may be made according to the image data amount and the data amount threshold of the single frame image, or according to the actual requirement, and either of the two methods may be adopted.

S1213: video is constructed from multiple telemetry channels.

S1214a: video compression encoding.

S1214b: and (5) image compression coding.

S1215: and (5) ending.

It should be noted that, in the process of constructing a single frame image, a suitable image format may be selected from the "two-dimensional" signal according to the task requirement, and the image may be encoded into one image.

Illustratively, if a complete signal is to be obtained, lossless compression methods of the image need to be used, including but not limited to: image compression techniques such as WebPNG images, PNG images, JPEG2000 images, and the like; if the accuracy requirements for the telemetry signal are not high, a lossy compression method may be used to further boost the compression ratio, including but not limited to: image compression techniques such as JPEG images and BMP images.

In addition, for the signal (or the telemetry channel with strong multipath correlation) with at least two main frequencies, the video compression technology can be adopted for compression, namely, the method is adopted for realizing two-dimensional image generation for the relatively high-frequency signal (or different telemetry channels), then a video stream is formed according to the low-frequency characteristic, and the compression is further realized according to the low-frequency correlation; wherein, the high frequency signal characterizes: the corresponding telemetry data corresponds to a plurality of primary frequencies.

In an alternative embodiment, the data compression device may implement downloading of the on-board compressed data (i.e. video stream data after video compression encoding and/or single frame image data after image compression encoding) through a telemetry channel, so that the ground (station) or an on-ground data decompression device receives signals and decompresses, and referring to fig. 13, the data transmission flow of the on-board measurement and control processing may be: original data, data compression equipment, source packet, star framing, calculation of frame cyclic redundancy check (Cyclic Redundancy Check, CRC) plus sync word (telemetry/encryption, encryption or telemetry/encryption, encryption), scrambling, channel coding.

In summary, in the method for compressing telemetry data provided in the embodiment of the present application, an image data set is configured according to multiple telemetry data in the satellite in-orbit operation process, and video stream data is configured for the image data set meeting the video configuration condition, and video compression encoding is performed on the video stream data; therefore, the satellite on-orbit application of the image/video compression algorithm is realized, the image/video compression algorithm is fully exerted, the signal with obvious periodicity and relativity (namely, the telemetry data with low sampling frequency requirement) has extremely high compression ratio, and the characteristic of signal change detail can be reserved for the signal with abundant local change detail (namely, the telemetry data with high sampling frequency requirement).

Therefore, the problem that in the related art, for telemetry data with unobvious periodicity or no periodicity, the requirement of high-frequency sampling is difficult to meet, and for telemetry data with obvious periodicity or extremely periodicity, the data redundancy is still high is avoided, namely, in this way, the contradiction between the periodic characteristics of the telemetry data and sampling frequency selection is avoided, and the compression efficiency of the telemetry data is improved.

In addition, the method for compressing the telemetry data firstly utilizes a principal component analysis algorithm to acquire main change characteristics of telemetry signals; next, the period information of the signal is acquired by using a wavelet algorithm, an FFT algorithm, or the like; further, the autocorrelation of each signal is utilized to reconstruct a single-channel signal from a one-dimensional signal into a two-dimensional 'image' signal; furthermore, sorting the multi-channel signals by utilizing the cross correlation of the multi-signals, and constructing an original two-dimensional image, or constructing signals of multiple channels/different track periods into multi-frame original video signals; finally, data compression and downloading are achieved by means of appropriate image/video compression. Therefore, aiming at the time-frequency characteristic, the autocorrelation characteristic and the cross-correlation characteristic of the satellite telemetry data, multichannel telemetry signals are reasonably arranged, the compression coding characteristic of an image/video compression algorithm is furthest exerted, and the purpose of efficiently compressing the telemetry signals is achieved.

Further, after receiving the signal (i.e. video stream data after video compression encoding and/or single frame image data after image compression encoding), the data decompression device can obtain telemetry data through decoding and decompression, wherein the method comprises the steps of decompressing and decoding the video stream or the image; for example, referring to fig. 14, the data decompression apparatus may implement the following ground decompression data:

s1401: starting.

For single-frame image data after image compression encoding, the following steps are executed:

s1402a: and (5) decoding the image.

S1403: the single frame image is inversely constructed as a signal.

S1404: restoring the data.

S1405: and (5) ending.

For video compression encoded video stream data, and if the video stream data is constructed from multiple telemetry channels, performing the steps of:

s1402b: and (5) video decoding.

S1406a: the video frames are decoded into multiple telemetry images.

S1403: the single frame image is inversely constructed as a signal.

S1404: restoring the data.

S1405: and (5) ending.

Furthermore, if the video stream data is constructed from a slow-change period, the following steps are performed:

s1406b: the video frames are decoded into slowly varying periodic images.

S1407: the single frame image is inversely constructed as a multi-dimensional signal.

S1404: restoring the data.

S1405: and (5) ending.

It should be noted that the signals reversely configured in the step S1403 may be multidimensional signals (for example, X, Y and Z dimensions), and as shown in fig. 14, only an exemplary description is given: the right image represents a single telemetry signal of different dimensions and the left image represents a plurality of telemetry signals.

In addition, based on the above mode, the restored data/restored signal is basically consistent with the original data/original signal in global and has high local similarity, so that the compression method of the telemetry data according to the embodiment of the application improves the data compression efficiency and ensures the fidelity of data transmission.

Further, based on the same technical concept, the embodiment of the present application provides a telemetry data compression device, which is used to implement the above-mentioned method flow of the embodiment of the present application. Referring to fig. 15, the telemetry data compression apparatus includes: a data acquisition module 1501, a frequency domain analysis module 1502, an image construction module 1503, and a data encoding module 1504, wherein:

a data acquisition module 1501 for acquiring various telemetry data of the satellite in-orbit operation;

The frequency domain analysis module 1502 is configured to perform frequency domain analysis on multiple telemetry data to obtain multiple main frequency sets;

an image construction module 1503 for constructing image data sets for the plurality of telemetry data based on the number of dominant frequencies contained in the plurality of dominant frequency sets, respectively;

the data encoding module 1504 is configured to construct video stream data based on a plurality of image data sets, and perform video compression encoding on the video stream data.

In an alternative embodiment, when performing frequency domain analysis on multiple telemetry data to obtain multiple primary frequency sets, the frequency domain analysis module 1502 is specifically configured to:

In an alternative embodiment, the image construction module 1503 is specifically configured to, when constructing an image dataset for a plurality of telemetry data based on the number of dominant frequencies comprised by the plurality of dominant frequency sets, respectively:

In an alternative embodiment, the image construction module 1503 is specifically configured to, when constructing an image dataset comprising a plurality of frames of image data based on n+1 sub-telemetry data:

In an alternative embodiment, after dividing the telemetry data corresponding to the first set of primary frequencies into n+1 sub-telemetry data based on N primary frequencies, the image construction module 1503 is further configured to:

In an alternative embodiment, in aligning N sub-telemetry data other than the first sub-telemetry data, the image construction module 1503 is specifically configured to:

In an alternative embodiment, in aligning N sub-telemetry data other than the first sub-telemetry data, the image construction module 1503 is further configured to:

In an alternative embodiment, the image construction module 1503 is further configured to, in constructing the image data set for each of the plurality of telemetry data based on the number of dominant frequencies comprised by the plurality of dominant frequency sets:

In an alternative embodiment, the image construction module 1503 is further configured to, after constructing the image data sets for the plurality of telemetry data, respectively:

In an alternative embodiment, in constructing video stream data based on a plurality of image data sets, the data encoding module 1504 is specifically configured to:

In an alternative embodiment, the data encoding module 1504 is further configured to:

Based on the same technical concept, the embodiment of the application also provides data compression equipment, which can realize the compression method flow of the telemetry data provided by the embodiment of the application. In one embodiment, the data compression device may be a server, a terminal device, or other electronic device. Referring to fig. 16, the data compression apparatus may include:

the embodiment of the present application does not limit the specific connection medium between the processor 1601 and the memory 1602, and the connection between the processor 1601 and the memory 1602 through the bus 1600 is exemplified in fig. 16. Bus 1600 is shown in bold lines in fig. 16, and the manner in which other components are connected is illustrated schematically and not by way of limitation. Bus 1600 may be divided into address bus, data bus, control bus, etc., and is represented by only one thick line in fig. 16 for ease of illustration, but does not represent only one bus or one type of bus. Alternatively, the processor 1601 may also be referred to as a controller, and the names are not limited.

In this embodiment, the memory 1602 stores instructions executable by the at least one processor 1601, and the at least one processor 1601 may perform a telemetry data compression method as discussed above by executing the instructions stored by the memory 1602. The processor 1601 may implement the functions of the various modules in the apparatus shown in fig. 15.

The processor 1601 is a control center of the apparatus, and may connect various parts of the entire control device using various interfaces and lines, and by executing or executing instructions stored in the memory 1602 and invoking data stored in the memory 1602, various functions of the apparatus and processing data, thereby performing overall monitoring of the apparatus.

In an alternative design, processor 1601 may include one or more processing units, and processor 1601 may integrate an application processor primarily handling operating systems, user interfaces, application programs, etc., with a modem processor primarily handling wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 1601. In some embodiments, the processor 1601 and the memory 1602 may be implemented on the same chip, and in some embodiments they may be implemented separately on separate chips.

The processor 1601 may be a general purpose processor such as a CPU, digital signal processor, application specific integrated circuit, field programmable gate array or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, and may implement or perform the methods, steps and logic blocks disclosed in the embodiments of the present application. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of a telemetry data compression method disclosed in connection with the embodiments of the present application may be embodied directly in hardware processor execution or in a combination of hardware and software modules in a processor.

Memory 1602 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 1602 may include at least one type of storage medium, which may include, for example, flash Memory, hard disk, multimedia card, card Memory, random access Memory (Random Access Memory, RAM), static random access Memory (Static Random Access Memory, SRAM), programmable Read-Only Memory (Programmable Read Only Memory, PROM), read-Only Memory (ROM), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory), magnetic Memory, magnetic disk, optical disk, and the like. Memory 1602 is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory 1602 in the present embodiment may also be a circuit or any other device capable of implementing a memory function for storing program instructions and/or data.

By programming the processor 1601, code corresponding to one of the telemetry data compression methods described in the previous embodiments may be solidified into a chip, thereby enabling the chip to perform the steps of one of the telemetry data compression methods of the embodiment shown in fig. 2 at run-time. How to design and program the processor 1601 is a well-known technology for those skilled in the art, and will not be described in detail herein.

Specifically, the processor 1601 when executing the computer program is configured to:

In an alternative embodiment, the processor 1601 is specifically configured to:

In an alternative embodiment, the processor 1601 is further configured to:

In an alternative embodiment, the processor 1601 is specifically configured to:

In an alternative embodiment, the processor 1601 is further configured to:

In an alternative embodiment, the processor 1601 is specifically configured to:

In an alternative embodiment, the processor 1601 is further configured to:

Based on the same inventive concept, the embodiment of the present application further provides a data decompression device, including a memory, a processor, and a computer program stored on the memory and executable by the processor, wherein the processor is configured to:

Based on the same inventive concept, embodiments of the present application also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform a method of compression of telemetry data as previously discussed.

In some alternative embodiments, aspects of a method of telemetry data compression may also be implemented in the form of a program product comprising program code for causing a control apparatus to carry out the steps of a method of telemetry data compression according to various exemplary embodiments of the present application as described herein when the program product is run on a device.

It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the elements described above may be embodied in one element in accordance with embodiments of the present application. Conversely, the features and functions of one unit described above may be further divided into a plurality of units to be embodied.

Furthermore, although the operations of the methods of the present application are depicted in the drawings in a particular order, this is not required to or suggested that these operations must be performed in this particular order or that all of the illustrated operations must be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's equipment, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.

In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected over the Internet using an Internet service provider).

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, provided that such modifications and variations of the present application fall within the scope of the claims and their equivalents.

Claims

1. A method of compressing telemetry data, comprising:

acquiring various telemetry data of a satellite in an in-orbit operation process, and performing frequency domain analysis on the various telemetry data to acquire a plurality of main frequency sets;

constructing an image data set for the plurality of telemetry data, respectively, based on a number of dominant frequencies contained by the plurality of dominant frequency sets;

video stream data is constructed based on a plurality of image data sets, and video compression encoding is performed on the video stream data.

2. The method of claim 1, wherein the performing frequency domain analysis on the plurality of telemetry data to obtain a plurality of primary frequency sets comprises:

and obtaining a main frequency set corresponding to each of the plurality of telemetry data based on the spectrum information of the plurality of telemetry data after transformation.

3. The method of claim 1, wherein the constructing image data sets for the plurality of telemetry data based on the number of primary frequencies contained by the plurality of primary frequency sets, respectively, comprises:

dividing telemetry data corresponding to the first main frequency set into N+1 sub telemetry data based on the N main frequencies, and constructing an image data set containing multi-frame image data based on the N+1 sub telemetry data.

4. The method of claim 3, wherein constructing an image data set comprising multi-frame image data based on the n+1 sub-telemetry data comprises:

dividing the N+1 sub-telemetry data into M data frames according to a data segmentation threshold; wherein the data segmentation threshold characterizes: a data minimum division length of each sub-telemetry data, the M being an integer greater than 1;

and constructing single-frame image data based on M data frames of the N+1 sub telemetry data respectively, and constructing the image data set containing multi-frame image data based on the obtained multiple single-frame image data.

5. The method of claim 3, wherein after dividing telemetry data corresponding to the first set of primary frequencies into n+1 sub-telemetry data based on the N primary frequencies, further comprising:

screening out first sub-telemetry data with the largest data quantity from the N+1 sub-telemetry data;

and aligning N pieces of sub-telemetry data except the first sub-telemetry data in the N+1 pieces of sub-telemetry data by taking the first sub-telemetry data as a reference.

6. The method of claim 5, wherein aligning N sub-telemetry data, other than the first sub-telemetry data, of the n+1 sub-telemetry data, with respect to the first sub-telemetry data, comprises:

For the first N-1 sub-telemetry data in the N sub-telemetry data, respectively performing the following operations:

performing data supplementation on the L < th > sub-telemetry data based on the L < th > +1 < th > sub-telemetry data until the L < th > sub-telemetry data after supplementation is the same as the data amount of the first sub-telemetry data; wherein L is an integer greater than 0 and less than N.

7. The method of claim 5, wherein the aligning N sub-telemetry data, other than the first sub-telemetry data, of the n+1 sub-telemetry data, with respect to the first sub-telemetry data, further comprises:

8. The method of claim 1, wherein the constructing image data sets for the plurality of telemetry data, respectively, based on the number of dominant frequencies contained by the plurality of dominant frequency sets, further comprises:

if a second main frequency set with the main frequency number of 1 exists in the plurality of main frequency sets, an image data set containing single-frame image data is constructed based on telemetry data corresponding to the second main frequency set.

9. The method of claim 1, wherein after constructing the image data sets for the plurality of telemetry data, respectively, further comprises:

10. The method of any of claims 1-9, wherein the constructing video stream data based on the plurality of sets of image data comprises:

if a second image data set which contains single-frame image data and has an image data amount smaller than a data amount threshold exists in the plurality of image data sets, video stream data is constructed based on the second image data set.

11. The method of claim 10, wherein the method further comprises:

And if a third image data set which contains single-frame image data and has the image data amount not smaller than the data amount threshold exists in the plurality of image data sets, performing image compression coding on the third image data set.

12. A telemetry data compression apparatus, comprising:

the frequency domain analysis module is used for carrying out frequency domain analysis on the plurality of telemetry data to obtain a plurality of main frequency sets;

an image construction module for constructing image data sets for the plurality of telemetry data, respectively, based on the number of dominant frequencies contained by the plurality of dominant frequency sets;

13. A data compression device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor is configured to, when executing the computer program:

14. The data compression device of claim 13, wherein the processor is specifically configured to:

15. The data compression device of claim 13, wherein the processor is specifically configured to:

16. The data compression device of claim 15, wherein the processor is specifically configured to:

17. The data compression device of claim 15, wherein the processor is further configured to:

18. The data compression device of claim 17, wherein the processor is specifically configured to:

19. The data compression device of claim 17, wherein the processor is further configured to:

20. The data compression device of claim 13, wherein the processor is further configured to:

21. The data compression device of claim 13, wherein the processor is further configured to:

22. The data compression device of any one of claims 13-21, wherein the processor is specifically configured to:

23. The data compression device of claim 22, wherein the processor is further configured to: