Data processing method and system for satellite temperature telemetry

Abstract

The present invention discloses a data processing method and system for satellite temperature telemetry, wherein the data processing unit is used to obtain the working temperature of each working part of the satellite measured by thermistors, and to process the satellite temperature telemetry data, to eliminate wild values ​​and fill missing values; the present invention relates to the field of data processing technology. The data processing method and system for satellite temperature telemetry can obtain data of good quality through the data processing unit when there are many wild values ​​and missing data in the actual telemetry data, and avoid jamming caused during data transmission under the action of the transmission node unit, thereby improving the stability of the data transmission process. The prediction model unit can generate a temperature change prediction model based on the existing data, so as to facilitate the subsequent early warning analysis unit to perform analysis and early warning, and can also quickly match the corresponding fault parameters, saving more time for fault location, and making data processing more timely and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data processing method and system for satellite temperature telemetry. Background Art

[0002] At present, there are a large number of temperature sensors for satellites flying in orbit. The working temperature of various working parts of the satellite is measured by thermistors, and provided to ground monitoring or to the satellite control system for temperature control to ensure that the various working components of the satellite operate in a normal temperature environment.

[0003] In the process of existing satellite temperature telemetry data processing, the situation where there are many wild values ​​and data missing in the actual telemetry data is directly ignored. This will lead to a lack of support for some data during the data analysis process, affecting the results of the data analysis. In addition, for a large amount of data transmission, it will cause jams due to data transmission congestion, and it is impossible to generate an early warning model based on the existing data and to locate the fault in time after the abnormality. The data processing efficiency is low. Therefore, the present invention proposes a data processing method and system for satellite temperature telemetry to solve the above-mentioned problems. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a data processing method and system for satellite temperature telemetry, which solves the problems mentioned in the above background technology.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data processing system for satellite temperature telemetry, comprising:

[0008] Data processing unit: used to obtain the working temperature of each working part of the thermistor measurement satellite, process the satellite temperature telemetry data, remove wild values ​​and fill in missing values, and provide the data to ground monitoring or satellite control system. Wild values ​​are values ​​that are significantly different from other data in the data set;

[0009] Transmission node unit: used to set storage nodes in the transmission path, divide the data to be transmitted, and calculate the storage time in the storage node according to the divided data, wherein the data is divided into first-class data length, second-class data length and third-class data length;

[0010] Prediction model unit: used to construct a temperature change model for the acquired temperature data with time as the horizontal axis and temperature value as the vertical axis, and generate a temperature change prediction model according to the temperature change model, wherein the temperature change prediction model is a two-dimensional or three-dimensional model;

[0011] Early warning analysis unit: used to compare the acquired temperature data with the temperature change prediction model, analyze the amplitude of data change, determine whether it is in an abnormal temperature state, and then match the corresponding fault parameters according to the abnormal temperature state.

[0012] As an improved technical solution, the data processing unit processes the satellite temperature telemetry data, removes wild values ​​and fills missing values ​​in the following specific manner:

[0013] S1: First, the threshold and filtering methods are used to remove the data outliers, and then the adaptive sliding window interpolation method is used to fill the missing data. The judgment of the missing data values ​​requires further analysis, specifically:

[0014] S11: Set the threshold range of data missing and mark it as AX and AY, and mark the value of data missing as AZ, specifically including: AZ∈(0, AX), AZ∈(AX, AY) and AZ∈(AY, ∞), where AX, AY and AZ are all positive values;

[0015] S12: When AZ∈(0, AX), it is a small local deletion with less missing data, which does not affect the judgment of the sequence change trend. When AZ∈(AX, AY), there is a large length deletion within the period, and the missing duration is less than the satellite orbit period, which does not affect the judgment of the sequence change trend. When AZ∈(AY, ∞), there is a long-term continuous large interval deletion, the telemetry data link is abnormal, and the data quality is too poor to be considered.

[0016] As an improved technical solution, when the adaptive sliding window interpolation method is used in S1 to fill in the missing data, the adaptive sliding window interpolation method can adaptively fill in the missing data of different missing lengths and different missing positions, and the filling value conforms to the data characteristics.

[0017] As an improved technical solution, the transmission node unit divides the data to be transmitted, and the specific method of calculating the storage time in the storage node according to the divided data is:

[0018] P1: Mark the length of the data to be transmitted as Q j , and then mark the first type of data length, the second type of data length and the third type of data length as Q1, Q2 and Q3 respectively, where, Then, depending on the length of the code, Q1, Q2, and Q3 differ by one or more bits;

[0019] P2: Then mark the storage time of each node as T1, T2 and T3, and the storage time of Q1, Q2 and Q3 corresponds to it respectively, and then mark the transmission time of Q1, Q2 and Q3 as CT1, CT2 and CT3 respectively, where T1=0, T2=CT1, T3=CT2, until Q3 is completely transmitted.

[0020] As an improved technical solution, when Q1, Q2 and Q3 in P2 are transmitting, the transmission rate is calculated as follows: R = (1 / T)*, where: T is the width or repetition period of a digital pulse signal; a digital pulse is also called a code element, N is the number of valid discrete values ​​taken by a code element, and N is an integer power of 2.

[0021] As an improved technical solution, after the temperature change model is constructed in the prediction model unit, the specific method of generating the temperature change prediction model according to the temperature change model is as follows:

[0022] Set the time interval to T, then mark the two endpoints of the time interval T as DA and DB, calculate the change weight within the time interval T, and mark it as W, where, The average of the change weights is then labeled in, according to To model the temperature change.

[0023] As an improved technical solution, the specific method of determining whether the temperature is abnormal in the early warning analysis unit and then matching the corresponding fault parameters according to the abnormal temperature state is as follows:

[0024] M1: Set the temperature change threshold ST, then mark the temperature value predicted by the temperature change model as YT, and mark the actual temperature value as KT. When KT∈ST±YT, the temperature data is normal, otherwise, the temperature data is abnormal.

[0025] M2: Then divide the fault parameters of temperature anomaly and mark them as YC j , and are marked as YC1, YC2, ..., YC according to the abnormal intervals. n ,when At this time, according to YC j Find the corresponding fault parameter range and accurately determine the cause of the temperature abnormality.

[0026] As an improved technical solution, the M2 is based on YC j Find the corresponding fault parameter range and accurately determine the cause of temperature abnormality. j When searching for the corresponding fault parameters, manual analysis is required.

[0027] As an improved technical solution, the method further includes a data storage unit for storing data in the data processing unit, the transmission node unit, the prediction model unit and the early warning analysis unit.

[0028] The present invention also discloses a data processing method for satellite temperature telemetry, which specifically comprises the following steps:

[0029] Step 1: Obtain the working temperature of each working part of the satellite using thermistors, process the satellite temperature telemetry data, remove wild values ​​and fill in missing values, and provide the data to ground monitoring or satellite control system;

[0030] Step 2: Set up storage nodes in the transmission path, divide the data to be transmitted, and calculate the storage time in the storage nodes based on the divided data;

[0031] Step 3: construct a temperature change model for the acquired temperature data with time as the horizontal axis and temperature value as the vertical axis, and generate a temperature change prediction model according to the temperature change model;

[0032] Step 4: Compare the acquired temperature data with the temperature change prediction model, analyze the amplitude of the data change, determine whether it is in an abnormal temperature state, and then match the corresponding fault parameters according to the abnormal temperature state.

[0033] (III) Beneficial effects

[0034] The present invention provides a data processing method and system for satellite temperature telemetry. Compared with the prior art, it has the following beneficial effects:

[0035] The data processing method and system for satellite temperature telemetry can obtain data of good quality through the data processing unit when there are many wild values ​​and data missing in the actual telemetry data, and avoid the jamming caused by the data transmission process under the action of the transmission node unit, thereby improving the stability of the data transmission process. The prediction model unit can generate a temperature change prediction model based on the existing data to facilitate the subsequent early warning analysis unit to perform analysis and early warning, and can also quickly match the corresponding fault parameters, saving more fault location time, and making data processing more timely and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of the data processing method of satellite temperature telemetry of the present invention;

[0037] Figure 2 This is a block diagram of the data processing system for satellite temperature telemetry of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] See also Figure 1-Figure 2 , the embodiment of the present invention provides six technical solutions:

[0040] Embodiment 1: A data processing system for satellite temperature telemetry, comprising:

[0041] Data processing unit: used to obtain the working temperature of each working part of the thermistor measurement satellite, and process the satellite temperature telemetry data, remove data wild values ​​and fill missing values, and provide the data to ground monitoring or satellite control system, where the data wild value is a value that is significantly different from other data in the data set; the specific method of processing the satellite temperature telemetry data in the data processing unit to remove data wild values ​​and fill missing values ​​is as follows: S1: First, the threshold and filtering method is used to remove the data wild values, and then the adaptive sliding window interpolation method is used to fill the data missing values. The judgment of the data missing values ​​requires further analysis, specifically: S11: Set The threshold range of data missing is determined and marked as AX and AY, and the value of data missing is marked as AZ, including: AZ∈(0, AX), AZ∈(AX, AY) and AZ∈(AY, ∞), where AX, AY and AZ are all positive values; S12: When AZ∈(0, AX), it is a local small missing, with less missing data, which does not affect the judgment of the sequence change trend. When AZ∈(AX, AY), there is a large length missing within the period, and the missing duration is less than the satellite orbit period, which does not affect the judgment of the sequence change trend. When AZ∈(AY, ∞), there is a long-term continuous large interval missing, the telemetry data link is abnormal, and the data quality is too poor to be considered. When the adaptive sliding window interpolation method is used in S1 to fill the data missing, the adaptive sliding window interpolation method can adaptively fill the data missing of different missing lengths and different missing positions, and the filling value conforms to the data characteristics.

[0042] Transmission node unit: used to set up storage nodes in the transmission path, divide the data to be transmitted, and calculate the storage time in the storage node according to the divided data, wherein the data is divided into the first type of data length, the second type of data length and the third type of data length; the transmission node unit divides the data to be transmitted, and calculates the storage time in the storage node according to the divided data. The specific method is as follows: P1: mark the length of the data to be transmitted as Q j, and then mark the first type of data length, the second type of data length and the third type of data length as Q1, Q2 and Q3 respectively, where, Then, according to the length of the code, Q1, Q2 and Q3 differ by one or several bits; P2: Then, the storage time of each node is marked as T1, T2 and T3, and the storage time of Q1, Q2 and Q3 corresponds to it respectively, and then the transmission time of Q1, Q2 and Q3 is marked as CT1, CT2 and CT3 respectively, where T1 = 0, T2 = CT1, T3 = CT2, until Q3 is completely transmitted. When Q1, Q2 and Q3 are transmitting in P2, the calculation formula of the transmission rate is: R = (1 / T)*, where: T is the width or repetition period of a digital pulse signal; a digital pulse is also called a code element, N is the number of valid discrete values ​​taken by a code element, and N takes an integer power of 2.

[0043] Early warning analysis unit: used to compare the acquired temperature data with the temperature change prediction model, analyze the amplitude of data change, determine whether it is in a state of temperature abnormality, and then match the corresponding fault parameters according to the state of temperature abnormality. The specific method of determining whether it is in a state of temperature abnormality and then matching the corresponding fault parameters according to the state of temperature abnormality in the early warning analysis unit is as follows: M1: Set the temperature change threshold ST, and then mark the temperature value predicted by the temperature change model as YT, and mark the actual temperature value obtained as KT, where when KT∈ST±YT, the temperature data is normal, otherwise, the temperature data is abnormal; M2: Then divide the fault parameters of temperature abnormality and mark them as YC j , and are marked as YC1, YC2, ..., YC according to the abnormal intervals. n ,when At this time, according to YC j Find the corresponding fault parameter range and accurately determine the cause of temperature abnormality. j Find the corresponding fault parameter range and accurately determine the cause of temperature abnormality. j When searching for the corresponding fault parameters, manual analysis is required.

[0044] Embodiment 2: This embodiment is based on Embodiment 1, and the difference from Embodiment 1 is that the prediction model unit is used to construct a temperature change model for the acquired temperature data with time as the horizontal axis and temperature value as the vertical axis, and generate a temperature change prediction model according to the temperature change model, wherein the temperature change prediction model is a two-dimensional or three-dimensional model; after the temperature change model is constructed in the prediction model unit, the specific method of generating the temperature change prediction model according to the temperature change model is: setting the time interval to T, and then marking the two endpoint values ​​of the time interval T as DA and DB, calculating the change weight within the time interval T, and marking it as W, wherein, The average of the change weights is then labeled in, according to To model the temperature change.

[0045] Embodiment 3: It also includes a data storage unit for storing data in the data processing unit, the transmission node unit, the prediction model unit and the early warning analysis unit.

[0046] Embodiment 4: Compared with Embodiment 1, Embodiment 2 and Embodiment 3, the technical solution of this embodiment is to combine the solutions of Embodiment 1, Embodiment 2 and Embodiment 3 for implementation.

[0047] Embodiment 5: A data processing method for satellite temperature telemetry, specifically comprising the following steps:

[0048] Step 1: Obtain the working temperature of each working part of the satellite using thermistors, process the satellite temperature telemetry data, remove wild values ​​and fill in missing values, and provide the data to ground monitoring or satellite control system;

[0049] Step 2: Set up storage nodes in the transmission path, divide the data to be transmitted, and calculate the storage time in the storage nodes based on the divided data;

[0050] Step 3: construct a temperature change model for the acquired temperature data with time as the horizontal axis and temperature value as the vertical axis, and generate a temperature change prediction model according to the temperature change model;

[0051] Step 4: Compare the acquired temperature data with the temperature change prediction model, analyze the amplitude of the data change, determine whether it is in an abnormal temperature state, and then match the corresponding fault parameters according to the abnormal temperature state.

[0052] Embodiment 6: A data processing system for satellite temperature telemetry, comprising:

[0053] Data processing unit: used to obtain the working temperature of each working part of the thermistor measurement satellite, process the satellite temperature telemetry data, remove wild values ​​and fill in missing values, and provide the data to ground monitoring or satellite control system. Wild values ​​are values ​​that are significantly different from other data in the data set;

[0054] Transmission node unit: used to set storage nodes in the transmission path, divide the data to be transmitted, and calculate the storage time in the storage node according to the divided data, wherein the data is divided into first-class data length, second-class data length and third-class data length;

[0055] Prediction model unit: used to construct a temperature change model for the acquired temperature data with time as the horizontal axis and temperature value as the vertical axis, and generate a temperature change prediction model according to the temperature change model, wherein the temperature change prediction model is a two-dimensional or three-dimensional model;

[0056] Early warning analysis unit: used to compare the acquired temperature data with the temperature change prediction model, analyze the amplitude of data change, determine whether it is in an abnormal temperature state, and then match the corresponding fault parameters according to the abnormal temperature state.

[0057] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0058] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0059] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data processing system for satellite temperature telemetry, characterized in that: include: Data processing unit: used to obtain the working temperature of each working part of the thermistor measurement satellite, process the satellite temperature telemetry data, remove wild values ​​and fill in missing values, and provide the data to ground monitoring or satellite control system. Wild values ​​are values ​​that are significantly different from other data in the data set; Transmission node unit: used to set storage nodes in the transmission path, divide the data to be transmitted, and calculate the storage time in the storage node according to the divided data, wherein the data is divided into first-class data length, second-class data length and third-class data length; The transmission node unit divides the data to be transmitted, and the specific method of calculating the storage time in the storage node according to the divided data is as follows: P1: Mark the length of the data to be transmitted as Q, and then mark the length of the first type of data, the second type of data, and the third type of data as Q1, Q2, and Q3, respectively. , then according to the length of the code, Q1, Q2 and Q3 differ by one or more bits; P2: Then mark the storage time of each node as T1, T2 and T3, and the storage time of Q1, Q2 and Q3 corresponds to it respectively, and then mark the transmission time of Q1, Q2 and Q3 as CT1, CT2 and CT3 respectively, where T1=0, T2=CT1, T3=CT2, until Q3 is completely transmitted; When Q1, Q2 and Q3 in P2 are transmitting, the calculation formula of the transmission rate is: R=(1 / T)*log2N, where: T is the width or repetition period of a digital pulse signal; a digital pulse is also called a code element, N is the number of valid discrete values ​​taken by a code element, and N takes an integer power of 2; Prediction model unit: used to construct a temperature change model for the acquired temperature data with time as the horizontal axis and temperature value as the vertical axis, and generate a temperature change prediction model according to the temperature change model, wherein the temperature change prediction model is a two-dimensional or three-dimensional model; Early warning analysis unit: used to compare the acquired temperature data with the temperature change prediction model, analyze the amplitude of data change, determine whether it is in a state of abnormal temperature, and then match the corresponding fault parameters according to the state of abnormal temperature; The specific method of determining whether the temperature is abnormal in the early warning analysis unit and then matching the corresponding fault parameters according to the abnormal temperature state is as follows: M1: Set the temperature change threshold ST, then mark the temperature value predicted by the temperature change model as YT, and mark the actual temperature value as KT. When KT∈ST±YT, the temperature data is normal, otherwise, the temperature data is abnormal. M2: Then divide the fault parameters of temperature anomaly and mark them as YC j , and are marked as YC1, YC2, ..., YC according to the abnormal intervals. n ,when At this time, according to KT in YC j Find the corresponding fault parameter range in the instrument and accurately determine the cause of the temperature abnormality.

2. The satellite temperature telemetry data processing system according to claim 1, characterized in that: The specific method for processing the satellite temperature telemetry data in the data processing unit to remove wild values ​​and fill missing values ​​is as follows: S1: First, the threshold and filtering methods are used to remove the data outliers, and then the adaptive sliding window interpolation method is used to fill the missing data. The judgment of the missing data values ​​requires further analysis, specifically: S11: Set the threshold range of missing data and mark it as AX and AY, and mark the value of missing data as AZ, specifically including: AZ∈(0,AX), AZ∈(AX,AY) and AZ∈(AY,∞), where AX, AY and AZ are all positive values; S12: When AZ∈(0,AX), it is a small local deletion with less missing data, which does not affect the judgment of the sequence change trend. When AZ∈(AX,AY), there is a large length deletion within the period, and the missing duration is less than the satellite orbit period, which does not affect the judgment of the sequence change trend. When AZ∈(AY,∞), there is a long-term continuous large interval deletion, the telemetry data link is abnormal, and the data quality is too poor to be considered.

3. The satellite temperature telemetry data processing system according to claim 2, characterized in that: When the adaptive sliding window interpolation method is used in S1 to fill in missing data, the adaptive sliding window interpolation method can adaptively fill in missing data of different missing lengths and different missing positions, and the filling value conforms to the data characteristics.

4. The satellite temperature telemetry data processing system according to claim 1, characterized in that: The M2 is based on KT in YC j When searching for the corresponding fault parameter interval in the YC j When the corresponding fault parameter range cannot be found within the range, manual analysis is required.

5. The satellite temperature telemetry data processing system according to claim 1, characterized in that: It also includes a data storage unit for storing data in the data processing unit, the transmission node unit, the prediction model unit and the early warning analysis unit.

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