A time series analysis and calculation method for spacecraft telemetry data

Abstract

The present invention relates to a time series analysis and calculation method for spacecraft telemetry data. The method comprises: receiving the demand for data to be accessed in multiple time periods; generating a time series calculation expression according to the demand for data to be accessed in multiple time periods, and using the time series calculation expression as the input for accessing the database of telemetry data; wherein the time series calculation expression represents the demand for data to be accessed in each time period with an operation item; extracting the time series calculation expression in the database and generating a string set; wherein the string set is a set of time series operation strings; parsing and retrieving the string set item by item in the database, and generating a parameter time series set, wherein the parameter time series set is a set of corresponding parameters for each time period. The present invention solves the timeliness and convenience problems of multi-telemetry time series sequence calculation by accessing input through a time series calculation expression, so that ground operation and maintenance personnel can flexibly perform analysis and calculation of multi-telemetry time series.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of spacecraft measurement and control data processing, and in particular to a time series analysis and calculation method for spacecraft telemetry data. Background Art

[0002] The status confirmation, anomaly detection and health assessment tasks in the management process of various on-orbit spacecraft involve a large amount of telemetry data calculation. As a typical time series data, spacecraft telemetry data has massive time requirements for time series characteristic calculation in engineering applications, time scale offset calculation and other needs, which cannot be met by existing technical methods.

[0003] At present, there are many tools that can be used for time series calculation of spacecraft telemetry data. For example, the data structure (DataFrame) in the Python language Pandas module supports the filtering and screening of time series data, but these tools still have some shortcomings when directly processing spacecraft telemetry time series data. Take the difference comparison between the current telemetry sequence value and the telemetry sequence value of a period of last year, which is commonly used in spacecraft telemetry time series analysis, as an example. When multiple segments of telemetry time series data are involved, the filtering function is to filter out the required subset from the full set of data, that is, to filter out two segments of data from one year of data for calculation, resulting in a large amount of data access redundancy, which is difficult to meet the calculation time requirements. At the same time, analysts expect to obtain a time series result with the current period as the time scale and the difference between the two time periods as the value sequence, but the existing tools cannot directly perform calculations due to the inconsistent time scales of the two segments of data and the unequal length of data.

[0004] Therefore, it is necessary to improve one or more problems existing in the above-mentioned related technical solutions.

[0005] It should be noted that this section is intended to provide background or context for the technical solutions of the present invention stated in the claims. The description herein is not admitted to be prior art by inclusion in this section. Summary of the invention

[0006] The object of the present invention is to provide a time series analysis and calculation method for spacecraft telemetry data, thereby solving one or more problems caused by the limitations and defects of the relevant technology at least to a certain extent.

[0007] The present invention provides a time series analysis and calculation method for spacecraft telemetry data, comprising:

[0008] Receive data requests to be accessed in multiple time periods;

[0009] Generate a time series calculation expression according to the data requirements to be accessed in the multiple time periods, and use the time series calculation expression as an input for accessing a database of telemetry data; wherein the time series calculation expression represents the data requirements to be accessed in each time period with an operation term;

[0010] Extracting the time series calculation expression in the database and generating a character string set; wherein the character string set is a set of time series operation character strings;

[0011] The character string set is parsed and retrieved item by item in the database, and a parameter time series set is generated, wherein the parameter time series set is a set of corresponding parameters for each time period.

[0012] Optionally, the step of receiving the data requirements to be accessed in multiple time periods includes:

[0013] A general data access function is used to receive data requests to be accessed in multiple time periods.

[0014] Optionally, the step of generating a time series calculation expression according to the data requirements to be accessed in the multiple time periods, and using the time series calculation expression as an input for accessing a database of telemetry data includes:

[0015] The timing calculation expression is composed of a first operation term and four arithmetic operators, wherein the first operation term represents the data requirements to be accessed in each time period, and the first operation term is composed of a second operation term and four arithmetic operators, and the second operation term contains the telemetry parameter code in each time period.

[0016] Optionally, the step of extracting the time series calculation expression in the database and generating a character string set includes:

[0017] The time series calculation expression is parsed by a regular expression to generate the character string set.

[0018] Optionally, the step of parsing and retrieving the string set item by item in the database and generating a parameter time series set includes:

[0019] The parameter time series set is generated by parsing and retrieving the string set item by item through a regular expression; wherein each item of the parameter time series set contains the retrieved telemetry parameter and the start time and end time corresponding to the telemetry parameter.

[0020] Optionally, after the step of parsing and retrieving the string set item by item in the database and generating a parameter time series set, the step further includes:

[0021] Each item in the parameter time series set is format-converted and converted into a format of a general data access function.

[0022] Optionally, after the step of converting the format of each item in the parameter time series set into the format of a universal data access function, the step further includes:

[0023] The retrieved telemetry parameters are substituted into the time series calculation expression in the format of the general data access function for calculation.

[0024] Optionally, the step of substituting the retrieved telemetry parameters in the format of the universal data access function into the time series calculation expression for calculation includes:

[0025] Substitute the retrieved telemetry parameter into the second operand in the format of the general data access function.

[0026] Optionally, the step of substituting the retrieved telemetry parameters in the format of the universal data access function into the time series calculation expression for calculation includes:

[0027] After substituting the running result at each moment into the first operation item, the length of the remaining items of the first operation item is adjusted based on the sequence length of the first item of the first operation item.

[0028] The technical solution provided by the present invention may include the following beneficial effects:

[0029] In the present invention, access input is performed through time series calculation expressions, which solves the timeliness and convenience problems of multi-telemetry time series calculations, allowing ground operation and maintenance personnel to flexibly perform analysis and calculation of multi-telemetry time series to meet the work requirements of on-orbit spacecraft status confirmation, anomaly detection and health assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present invention, and together with the specification are used to explain the principles of the present invention. Obviously, the accompanying drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0031] Figure 1 A schematic diagram showing a flow chart of a method for time series analysis and calculation of spacecraft telemetry data in an exemplary embodiment of the present invention;

[0032] Figure 2 A schematic diagram showing the judgment logic of a method for time series analysis and calculation of spacecraft telemetry data in an exemplary embodiment of the present invention;

[0033] Figure 3 An example diagram of time series analysis calculation of spacecraft telemetry data in an exemplary embodiment of the present invention is shown. DETAILED DESCRIPTION

[0034] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present invention will be more comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0035] In addition, the accompanying drawings are only schematic illustrations of embodiments of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and their repeated descriptions will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0036] The present invention provides a time series analysis and calculation method for spacecraft telemetry data, referring to Figure 1 As shown in , the following steps are included:

[0037] Step S100: receiving data requirements to be accessed in multiple time periods.

[0038] Step S200: Generate a time series calculation expression according to the data requirements to be accessed in multiple time periods, and use the time series calculation expression as an input for accessing a database of telemetry data.

[0039] Step S300: extracting the time series calculation expression from the database and generating a character string set.

[0040] Step S400: parsing and retrieving the string set item by item in the database, and generating a parameter time series set.

[0041] The time series calculation expression uses operation items to represent the demand for data to be accessed in each time period, the string set is a set of time series operation strings, and the parameter time series set is a set of corresponding parameters for each time period.

[0042] It should be understood that, by analyzing the shortcomings of existing spacecraft telemetry time series data calculation tools and combining the actual engineering needs of on-orbit satellite management, the present invention proposes a time series analysis and calculation method for spacecraft telemetry data. The method mainly includes three parts: time series calculation parameter setting and calculation expression input; time series expression regular parsing and access function construction; single time series data access and multi-time series data alignment calculation.

[0043] It is also necessary to understand that the timing calculation parameter setting and calculation expression input include: setting the general data access function, setting the spacecraft name and start and end time parameters, and inputting the timing calculation expression.

[0044] It is also necessary to understand that the regular parsing of the time series expression and the construction of the access function include: regular parsing of all time series items, parsing the time series time item by item, and constructing a data access function expression.

[0045] It is also necessary to understand that the single-time series data access and multi-time series data alignment calculation includes: single-time series data access function execution and multi-time series data time scale alignment calculation.

[0046] By adopting the time series analysis and calculation method of spacecraft telemetry data mentioned above and accessing and inputting through time series calculation expressions, the timeliness and convenience issues of multi-telemetry time series calculation are solved, so that ground operation and maintenance personnel can flexibly perform analysis and calculation of multi-telemetry time series to meet the work requirements of on-orbit spacecraft status confirmation, anomaly detection and health assessment.

[0047] Next, we will refer to Figure 1 to Figure 2 Each step of the time series analysis and calculation method of the spacecraft telemetry data in this example embodiment is described in more detail.

[0048] In some embodiments, step S100 includes:

[0049] A general data access function is used to receive data requests to be accessed in multiple time periods.

[0050] It is important to understand that reference Figure 2 As shown in Figure 2 In 1.1, if the expression of telemetry time series calculation directly uses the access form of 'TM[startTime:endTime]' (TM is the telemetry parameter code, startTime is the start time of the time series data, and endTime is the end time of the time series data), it is necessary to prepare the data in the time period in advance. When multiple time periods are involved, data can only be prepared according to the maximum time range, which will cause redundant access to a large amount of data. The purpose of delayed parsing is achieved by setting the general data access function GetParaData, and the time series data of the required time period is only extracted when the calculation is actually performed. The GetParaData function requires parameters (mission, paraCode, startTime, endTime), where mission is the spacecraft code and paraCode is the telemetry parameter code. Figure 2In 1.2, set the global spacecraft name parameter missionC, the start time parameter startTimeC, and the end time parameter endTimeC for the expression calculation. The global parameters are used when the telemetry parameters in the timing calculation expression do not specifically specify the spacecraft name or the start time and end time.

[0051] In some embodiments, step S200 includes:

[0052] The timing calculation expression is composed of a first operation term and four arithmetic operators, wherein the first operation term represents the data requirements to be accessed in each time period, and the first operation term is composed of a second operation term and four arithmetic operators, and the second operation term contains the telemetry parameter code in each time period.

[0053] It is important to understand that reference Figure 2 As shown in Figure 2 In 1.3, enter the time series calculation expression string expTimeStr. The time series calculation expression consists of operands and operators. The operands can have n items and the format is TM i [startTime i :endTime i ], where 0≤i≤n, i is the number of the operation item, and n is a natural number. The operators are +, -, *, and / . When performing arithmetic operations on multiple time series, the values ​​of each series participate in the operation, but the time scale does not participate in the operation. The time scale of the first operation item is used as the time scale of the operation result.

[0054] In some embodiments, step S300 includes:

[0055] The time series calculation expression is parsed through a regular expression to generate a string set.

[0056] It is important to understand that reference Figure 2 As shown in Figure 2 In 2.1, the time series calculation expression contains n time series that need to be calculated. Use the regular expression "\w*\[[^:]*:[^\]]*\]" to convert all the time series expressions expTimeStr into TM i [startTime i :endTime i ] are extracted to form the parameter timing string set setParaStr={TMStr1,…,TMStr n}, where TMStr i =“TM i [startTime i :endTime i ]”.

[0057] In some embodiments, step S400 includes:

[0058] The string set is parsed and retrieved item by item through regular expressions to generate a parameter time series set; wherein each item of the parameter time series set contains the retrieved telemetry parameter and the start time and end time corresponding to the telemetry parameter.

[0059] It is important to understand that reference Figure 2 As shown in Figure 2 In 2.2, each item TMStr in the parameter timing string set setParaStr i Further use the regular expression "(\w*)\[([^:]*):([^\]]*)\]" to parse the parameter TM i 、Start time startTime i and end time endTime i Three operation items. After all the time series string items are parsed, a parameter time series set setParaSeris={TM1[startTime1:endTime1],…,TM n [startTime n :endTime n ]}.

[0060] In some embodiments, after step S400, the method further includes:

[0061] Step S500: convert the format of each item in the parameter time series set into the format of the general data access function.

[0062] It is important to understand that reference Figure 2 As shown in Figure 2 In 2.3, each TM in the parameter time series set setParaSeris i [startTime i :endTime i ]Converted to the format of the general access function expItemStr i = "GetParaData(missionC,TM i ,startTime i ,endTime i )”, where missionC is the global spacecraft name parameter.

[0063] In some embodiments, after step S500, the method further includes:

[0064] Step S600: Substitute the retrieved telemetry parameters into the time series calculation expression in the format of the general data access function for calculation.

[0065] It is important to understand that reference Figure 2 As shown in Figure 2 In 2.3, the string TMStr in the input timing calculation expression string expTimeStr is i Replaced with string expItemStr i , and obtain a new time series calculation expression string expTimeStrR.

[0066] In some embodiments, step S600 includes:

[0067] Substitute the retrieved telemetry parameters into the second operand in the format of the general data access function.

[0068] It is important to understand that reference Figure 2 As shown in Figure 2 In 3.1, the data access function of each data item in the expression expTimeStrR is calculated to obtain the time series data item dataSerisItem i , only the data in the time period in which the data item participates in the calculation is extracted. There are n operation items in total, so n time series data items are obtained, that is, 0≤i≤n.

[0069] In some embodiments, step S600 includes:

[0070] After substituting the running result at each moment into the first operation item, the length of the remaining items of the first operation item is adjusted based on the sequence length of the first item of the first operation item.

[0071] It is important to understand that reference Figure 2 As shown in Figure 2 In 3.2, the obtained n time series data items are calculated according to the four arithmetic operators in the expression expTimeStrR, where the time stamp of the first data item (i.e., the first item) dataSerisItem1 appearing in expTimeStrR is used as the time stamp of the calculation result expResult, and the value sequence of each data item is the value sequence of the result expResult of the four arithmetic operations. When multiple sequences are of different lengths, the length of the value sequence of the first data item (i.e., the first item) dataSerisItem1 in the expression is used as the basis, and those longer than the sequence are automatically truncated, and those shorter than the sequence are filled with the previous value.

[0072] Combination Figure 3 The above embodiments will be described below by means of specific examples.

[0073] 1. Setting of timing calculation parameters and input of calculation expressions. The user sets the global spacecraft code missionC = "Planet M", the start time parameter startTimeC = "2000-01-01", the end time parameter endTimeC = "2000-01-02", and inputs the timing calculation expression expTimeStr = "TM1['2000-01-01':'2000-01-02'] - TM1['1999-01-01':'1999-01-02']" that needs to be calculated.

[0074] 2. Regular parsing of timing expressions and construction of access functions. Parse two operation items, TM1['2000-01-01':'2000-01-02'] and TM1['1999-01-01':'1999-01-02'], from the expression expTimeStr, then construct data access functions and replace the original expression to obtain a new timing calculation expression string expTimeStrR = "GetParaData(M Planet, TM1, '2000-01-01', '2000-01-02') - GetParaData(M Planet, TM1, '1999-01-01', '1999-01-02')".

[0075] 3. Single-timing data access and multi-timing data alignment calculation. Execute the data extraction functions in the expression to obtain two data items dataSerisItem1 and dataSerisItem2. dataSerisItem1 actually extracts only one day's worth of timing data, and dataSerisItem2 also extracts only one day's worth of timing data. Then, the two data items are executed according to the four arithmetic operations in the expression. Using the time stamps within the interval from '2000-01-01' to '2000-01-02' as the time stamps of the result, and the sequence difference between the two data items as the value sequence of the result, as shown in Figure 3 subfigure (b) below. For comparison, in subfigure (a), another sequence expression "TM1['2000-01-01':'2000-01-02']" is executed. By comparing the two subfigures, it can be seen that the timing data of the TM1 parameter has no trend change from 2000-01-01 to 2000-01-02, but the comparison difference with the data in the interval from 1999-01-01 to 1999-01-02 shows an obvious upward trend.

[0076] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0077] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the appended claims.

Claims

1. A time series analysis and calculation method for spacecraft telemetry data, characterized in that: include: Receive data requests to be accessed in multiple time periods; Generate a time series calculation expression according to the data requirements to be accessed in the multiple time periods, and use the time series calculation expression as an input for accessing a database of telemetry data; wherein the time series calculation expression uses an operation term to represent the data requirements to be accessed in each time period; the time series calculation expression is composed of a first operation term and four arithmetic operators, wherein the first operation term represents the data requirements to be accessed in each time period, and the first operation term is composed of a second operation term and four arithmetic operators, and the second operation term contains a telemetry parameter code in each time period; Extracting the time series calculation expression in the database and generating a character string set; wherein the character string set is a set of time series operation character strings; The string set is parsed and retrieved item by item in the database, and a parameter time series set is generated; wherein the parameter time series set is a set of corresponding parameters for each time period; the string set is parsed and retrieved item by item through a regular expression to generate the parameter time series set; wherein each item in the parameter time series set contains the retrieved telemetry parameter and the start time and end time corresponding to the telemetry parameter.

2. The time series analysis and calculation method of spacecraft telemetry data according to claim 1 is characterized in that: The step of receiving the data requirements to be accessed in multiple time periods comprises: A general data access function is used to receive data requests to be accessed in multiple time periods.

3. The time series analysis and calculation method of spacecraft telemetry data according to claim 1, characterized in that: The step of extracting the time series calculation expression in the database and generating a character string set comprises: The time series calculation expression is parsed by a regular expression to generate the character string set.

4. The time series analysis and calculation method of spacecraft telemetry data according to claim 3 is characterized in that: After the step of parsing and retrieving the string set item by item in the database and generating a parameter time series set, the following step is further included: Each item in the parameter time series set is format-converted and converted into a format of a general data access function.

5. The time series analysis and calculation method of spacecraft telemetry data according to claim 4 is characterized in that: After the step of converting the format of each item in the parameter time series set into the format of the universal data access function, the method further includes: The retrieved telemetry parameters are substituted into the time series calculation expression in the format of the general data access function for calculation.

6. The time series analysis and calculation method of spacecraft telemetry data according to claim 5, characterized in that: The step of substituting the retrieved telemetry parameters in the format of the universal data access function into the time series calculation expression for calculation comprises: Substitute the retrieved telemetry parameter into the second operand in the format of the general data access function.

7. The time series analysis and calculation method of spacecraft telemetry data according to claim 6 is characterized in that: The step of substituting the retrieved telemetry parameters in the format of the universal data access function into the time series calculation expression for calculation comprises: After substituting the running result at each moment into the first operation item, the length of the remaining items of the first operation item is adjusted based on the sequence length of the first item of the first operation item.

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