CN110297826B - Method for dynamically analyzing satellite telemetry data based on json - Google Patents
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Abstract
The invention discloses a method for dynamically analyzing satellite telemetering data based on json, which is characterized in that a user needs to fill corresponding telemetering amount and corresponding attributes in an Excel form according to a uniform format, automatically generates a corresponding json data format file according to the Excel form, receives telemetering data on the ground, analyzes each telemetering measurement of the telemetering data according to the corresponding json data format, performs corresponding formula operation according to needs and displays the telemetering data on an interface. The invention is suitable for analyzing and displaying the telemetering data of different satellites in different formats by one ground station, is convenient for developing and modifying ground display software in the satellite development process, and solves the problem of analyzing the telemetering data of different satellites in different formats by one-station multi-satellite ground.
Description
Technical Field
The invention belongs to a satellite data dynamic analysis technology, and particularly relates to a json-based method for dynamically analyzing satellite telemetry data.
Background
The satellite telemetry data processing aims to comprehensively and effectively reflect the current running state of a tested satellite in the shortest time, visually reflect the state of the satellite after corresponding action is taken after the satellite receives an instruction through the parameters, position equipment problems and design defects in the running process of the satellite through the parameters and provide a valuable and high-reliability test conclusion.
The satellite telemetry data is determined by a satellite manufacturer, during a development test process, the satellite telemetry data is preliminarily determined, and satellite ground station control software is preliminarily determined, but some perfection modification is inevitable during a satellite development design process, so that the satellite ground control display software is also modified correspondingly, and the modification perfection process causes work to be complicated and boring. When the satellites are interconnected on the ground, the same ground station displays the telemetering information of different satellites, and if each satellite receives telemetering and is switched to corresponding different ground display software, the operation is troublesome and the display effect is poor. Therefore, in this context, it is important to design a method that is compatible with satellite telemetry data in various formats and at the same time facilitates modification of the completed terrestrial display data.
200910237626.3 discloses a telemetry data processing method of satellite telemetry data, which inputs the code, parameter type, digital quantity analysis mode or analog quantity analysis mode of telemetry parameters related to satellite test and the position of parameters in a telemetry data packet into a parameter table of a basic database; inputting codes, instruction types, binary codes of instructions and instruction danger levels of all test instructions related to satellite test into an instruction table of a basic database; test logics of execution instructions related to satellite test and test rule criteria are input into a flow chart of a basic database, and the satellite telemetering data processing method with high automation level and good data consistency is provided. But this approach is complicated to modify during testing and does not involve the reception of multiple satellite telemetry data.
Disclosure of Invention
The invention aims to provide a json-based method for dynamically analyzing satellite telemetry data, which can be suitable for ground analysis of various telemetry protocols.
The technical solution for realizing the purpose of the invention is as follows: a json-based method for dynamically resolving satellite telemetry data comprises the following steps:
step 1, establishing an Excel table of satellite telemetry information:
storing the telemetering information of the same satellite in a working table, storing the telemetering information of different satellites in different working tables in the same Excel table, wherein the telemetering information comprises telemetering data and a plurality of telemetering quantity attributes, the telemetering data of each satellite consists of a plurality of groups of different telemetering frames, each group of telemetering frames consists of a plurality of different telemetering measurements, and each telemetering quantity corresponds to the plurality of telemetering quantity attributes; the telemetering amount attributes are arranged in rows and comprise identification, data length, data type, byte offset of the telemetering amount in the satellite telemetering data, bit offset when the telemetering amount is expressed by bit variable, Chinese name, visible identification, a telemetering amount conversion function name and a reasonable range of the telemetering amount; the telemetering in each satellite is arranged in a column, and the specific telemetering is adjusted by a user;
step 2, calling an xlrd module by adopting a python language to analyze and read the Excel table, and the specific steps are as follows:
step 2-1, opening an Excel form of the telemetry information by using open _ workbook (filename) in xlrd, wherein the filename is the name of the Excel form, and calling a function sheet _ by _ name (sheet _ name) to obtain a worksheet with a specified name in the Excel, wherein the sheet _ name is the name of the specified worksheet;
step 2-2, sequentially calling row _ slice (row x) functions to return a list consisting of all cell objects in each row in a working table, forming an object of json data by one telemeasurement and the attribute thereof in the working table, and finally forming the json data format of the satellite by all telemeasurements;
step 3, calling a decodeBuf (byte [ ] buf) function to decode the telemetry frame;
when the ground station receives the telemetering data, analyzing the received telemetering data according to the json data format of the telemetering data, marking all byte lengths and starting words of the telemetering frames at the head of the json data frame, wherein the content of the json data format corresponds to the frame content of the specific telemetering frame; checking according to the frame head of the telemetry frame, if the length of the received telemetry frame data is not wrong, acquiring the attribute of each telemetry measurement of the content in the json data format, and then decoding the corresponding data of the frame content;
step 4, operating a conversion function and analyzing data;
a python function is adopted to execute in the operation conversion process of the telemetering data, and an Ironpython operation environment is called in C #, so that a dynamic pyhton script containing operation conversion formulas corresponding to all telemetering measurements defined by a user is analyzed and operated;
step 5, displaying a result;
and determining whether to display the telemetering amount information on the ground terminal according to the visible attribute of each telemetering amount, and if the visible indicates that the telemetering amount is visible, sequentially displaying the id of the telemetering amount and the converted numerical value on the terminal.
Compared with the prior art, the invention has the remarkable advantages that:
(1) when the ground display terminal is designed, a user only needs to design according to the existing requirements, and the subsequent modification only needs to add and modify the corresponding telemetering amount and the attribute thereof in the Excel form, so that a large amount of design modification time is saved, and a large amount of repeated work is avoided.
(2) When the telemetering data of one station and a plurality of satellites is received, the ground terminal can automatically generate a corresponding telemetering data interface according to the telemetering data improved by a satellite design party, and the corresponding interface does not need to be switched and called every time the satellite passes the border, so that the design of the ground terminal of one station and a plurality of satellites is simplified.
(3) In the process of processing the telemetering data, a processing function corresponding to the telemetering amount is constructed and adjusted in a callback function mode, so that a user can freely define an analysis operation mode for modifying telemetering, and the user can conveniently convert the received telemetering data.
(4) The ground software judges, processes and analyzes the telemetering data according to the frame head of the telemetering frame, and the theoretical numerical value of the telemetering amount has corresponding upper and lower limits, so that the accuracy of telemetering data analysis is guaranteed.
Drawings
FIG. 1 is a diagram of the json data format of telemetry data of the present invention.
Fig. 2 is a diagram of the json data format of a telemetry frame of the present invention.
FIG. 3 is a diagram of the specific composition of the star frame of the present invention.
Fig. 4 is a data composition diagram of frame contents according to the present invention.
Fig. 5 is a diagram illustrating the property of the telemetry measurement according to the present invention.
FIG. 6 is a flow chart of a method for dynamically resolving satellite telemetry data based on json in accordance with the present invention.
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures.
With reference to fig. 6, the json-based method for dynamically analyzing satellite telemetry data according to the present invention can be adapted to ground analysis of various telemetry protocols, and includes the following steps:
step 1, establishing an Excel table of satellite telemetry information:
the remote measuring information of the same satellite is stored in one working table, the remote measuring information of different satellites is stored in different working tables in the same Excel table, the remote measuring information comprises remote measuring data and a plurality of remote measuring quantity attributes, the remote measuring data of each satellite is composed of a plurality of groups of different remote measuring frames, each group of remote measuring frames is composed of a plurality of different remote measuring quantities, and each remote measuring quantity corresponds to the remote measuring quantity attributes. The telemetering amount attributes are arranged in rows and comprise identification, data length, data type, byte offset of the telemetering amount in the satellite telemetering data, bit offset when the telemetering amount is expressed by bit variable, Chinese name, visual identification, data conversion function name and reasonable range of the telemetering amount; the telemetry measurements in each satellite are arranged in columns and are used to represent attitude and position information, health status, and operating status of various components of the satellite, such as user adjustable information about the satellite's voltage, current, temperature, switching status, orbit, etc.
Step 2, calling an xlrd module by adopting a python language to analyze and read the Excel table, and the specific steps are as follows:
step 2-1, opening an Excel table by using open _ workbook (filename) in xlrd, wherein the filename is the name of the Excel table, and calling a function sheet _ by _ name to acquire a worksheet with a specified name in the Excel, wherein the sheet _ name is the name of the specified worksheet.
And 2-2, sequentially calling a row _ slice (row x) function to return a list consisting of all cell objects in each row in a working table, wherein one telemeasurement and the attribute thereof in the working table form an object of json data. Eventually all telemetry measurements form the json data format for the satellite as shown in fig. 1.
The json data format is illustrated below:
fig. 1 shows a json data format of telemetry data, the json data format is an array, elements of the array are objects, and each object corresponds to a telemetry frame, such as a housekeeping frame, an attitude control frame and a subsystem frame. Fig. 2 shows a json data format diagram.
Each telemetry frame object is composed of a set of "name/value" pairs, fig. 3 is a specific data composition of a star frame, which is illustrated by way of example as a star frame: the first "name/value" pair of the star frame indicates the type of the telemetry frame, the second "name/value" pair describes the byte length of the telemetry frame, the third "name/value" indicates the start word of the frame, the fourth "name/value" indicates the actual byte offset of the telemetry frame in the telemetry data, and the four "name/value" pairs are used in combination mainly for extracting and verifying the corresponding telemetry frame after receiving the telemetry data by the surface pair. The using method comprises the following steps: after the ground station receives the data, reading the telemetry data with the frame byte length number (the data length of each telemetry frame is fixed) at the specified offset position of the telemetry data according to the frame offset, judging whether the length of the received telemetry frame and the start word correspond, if not, judging that the receiving is wrong, otherwise, normally receiving and interpreting the telemetry frame as the telemetry frame specified by the frame type.
Next, the "name/value" pair is the specific telemetry frame content, and referring to fig. 4, the frame content is named content, its corresponding value is an array, the array elements are data objects, each data object represents a telemetry measure, for example, a satellite frame, the first telemetry measure is a software version number, the second telemetry measure is a satellite code number, and the third telemetry measure is a signal source … …, thus forming a set of telemetry frames.
For each telemetry measurement, each object will be described from the following points of view, "id", "leng", "type", "index", "bit-index", "chip", "visual", "coeff", "range", respectively; the value corresponding to id is an English identification of the telemetering amount, leng is the number of bytes occupied by the telemetering amount, type refers to the data type of the telemetering amount, index refers to the offset of the telemetering amount in the whole telemetering frame, Chinese refers to the name of Chinese of the telemetering amount, visible refers to whether the telemetering amount is set to be visible in a display terminal, coeff specifies whether the telemetering amount needs formula conversion so as to be a function name needing to be executed, range specifies the upper and lower data limits of the telemetering amount theory and is used for initially judging data on the ground to avoid analyzing abnormal data, bit-index refers to the bit offset of the variable and is effective only when the telemetering amount is a bit variable.
Step 3, calling a decodeBuf (byte [ ] buf) function to decode the telemetry frame;
when the ground station receives the telemetering data, the received telemetering data is analyzed according to the json data format of the telemetering data (the json data format is the json data format generated in the step 2), and all basic parameters such as the byte length of a telemetering frame and the starting word are marked in the head of the json data frame. The content of the json data format corresponds to the frame content of a particular telemetry frame (see fig. 5). And checking according to the frame head of the telemetry frame, if the length of the received telemetry frame data is not wrong, acquiring the attribute of each telemetry measurement of the content in the json data format, and then decoding the corresponding data of the frame content. The method comprises the following specific steps:
and 3-1, performing byte conversion and character string conversion on the telemetry quantity of the telemetry frame according to the attribute of each object in the content in the json data format. Content in the json data format appoints specific attributes of each telemetry measurement, a getValue (byte [ ] buf, string type, byte index, byte bit _ index, byte length, string coeff, string vRange) function is called, and each parameter in the function is the attribute of each telemetry measurement (defined in step 2, and the specific meaning refers to the json data format description in step 2). buf is the data start address of the telemetry frame to be resolved.
Step 3-2, the getValue function firstly determines whether the telemetry measurement to be analyzed is a byte, uint16, uint32 or bit data type according to the incoming type parameter, then obtains specific data at a corresponding offset position in buf according to the index attribute corresponding to the telemetry quantity, and performs corresponding operation according to the corresponding data type, if the telemetry measurement is a byte, uint16 or uint32 variable, then obtains data with a corresponding length at a corresponding position in buf telemetry frames according to the index offset, and if the telemetry quantity is a bit type variable, then obtains the telemetry quantity value with a corresponding bit length at a corresponding position according to the bit _ index value. If the coeff attribute of the telemetering measurement is not null, it indicates that the telemetering measurement needs to be subjected to corresponding numerical value conversion, and the telemetering measurement of a corresponding type corresponds to a corresponding operation function, which is specifically described as follows: when the type of the telemetering amount data is Int16, the operation function is computeRealValue _ Int16(Int16 para, string coeff), wherein para is the telemetering amount value needing to be converted, and coeff is the operation function name corresponding to the telemetering amount;
step 4, operating a conversion function and analyzing data;
since python has rich mathematical computation libraries, a python function is adopted to perform the operation conversion process of the telemetry data, and the dynamic pyhton script is analyzed and operated by calling the Ironpython operating environment in C #. In NET frames, the compiler and execution engine of the IronPython are included, so an engine instance can be created from the C # code and then the specified script executed. The script already contains all operation conversion formulas required by all telemetering quantities defined by a user, and all analysis formulas in the method are defined in an ipyrun. The calling method of C # is through GetVariable, the definition of the function is as GetVariable < T > (string name), wherein the string name is the operation conversion function corresponding to the telemetering amount, and the executed operation conversion result is used as the final display result.
Step 5, displaying a result;
and determining whether to display the telemetering information on the ground terminal according to the visible attribute of each telemetering measurement, and if the visible indicates that the telemetering measurement is visible, sequentially displaying the id of the telemetering measurement and the converted numerical value on the terminal.
Claims (5)
1. A json-based method for dynamically analyzing satellite telemetry data is characterized by comprising the following steps:
step 1, establishing an Excel table of satellite telemetry information:
storing the telemetering information of the same satellite in a working table, storing the telemetering information of different satellites in different working tables in the same Excel table, wherein the telemetering information comprises telemetering data and a plurality of telemetering quantity attributes, the telemetering data of each satellite consists of a plurality of groups of different telemetering frames, each group of telemetering frames consists of a plurality of different telemetering measurements, and each telemetering quantity corresponds to the plurality of telemetering quantity attributes; the telemetering amount attributes are arranged in rows and comprise identification, data length, data type, byte offset of the telemetering amount in the satellite telemetering data, bit offset when the telemetering amount is expressed by bit variable, Chinese name, visible identification, a telemetering amount conversion function name and a reasonable range of the telemetering amount; the telemetering in each satellite is arranged in a column, and the specific telemetering is adjusted by a user;
step 2, calling an xlrd module by adopting a python language to analyze and read the Excel table, and the specific steps are as follows:
step 2-1, opening an Excel form of the telemetry information by using open _ workbook (filename) in xlrd, wherein the filename is the name of the Excel form, and calling a function sheet _ by _ name (sheet _ name) to obtain a worksheet with a specified name in the Excel, wherein the sheet _ name is the name of the specified worksheet;
step 2-2, sequentially calling row _ slice (row x) functions to return a list consisting of all cell objects in each row in a working table, forming an object of json data by one telemeasurement and the attribute thereof in the working table, and finally forming the json data format of the satellite by all telemeasurements;
in step 2-2, the json data format is illustrated as follows:
the json data format is an array, the elements of the array are objects, and each object corresponds to a telemetry frame; each telemetry frame object consists of a set of "name/value" pairs: the first "name/value" pair describes the type of telemetry frame, the second "name/value" pair describes the byte length of the telemetry frame, the third "name/value" represents the start word of the frame, and the fourth "name/value" represents the actual byte offset of the telemetry frame in the telemetry data;
after receiving data, the ground station starts to read the telemetry data of the frame byte length number at the specified offset position of the telemetry data according to the frame offset, then judges whether the received telemetry frame length and the start word correspond, if not, judges that the receiving is wrong, otherwise, normally receives the telemetry frame and interprets the telemetry frame as the telemetry frame specified by the frame type;
the next 'name/value' pair is the specific telemetry frame content, the name of the frame content is content, the corresponding value is an array, the array elements are data objects, and each data object represents a telemetry measure;
for each telemetry measurement, each object will be described from the following points of view, "id", "leng", "type", "index", "bit-index", "chip", "visual", "coeff", "range", respectively; the value corresponding to id is an English identification of the telemetering amount, leng is the number of bytes occupied by the telemetering amount, type refers to the data type of the telemetering amount, index refers to the offset of the telemetering amount in the whole telemetering frame, Chinese refers to the name of Chinese of the telemetering amount, visible refers to whether the telemetering amount is set to be visible in a display terminal, coeff specifies whether the telemetering amount needs formula conversion so as to be a function name needing to be executed, range specifies the upper and lower data limits of the telemetering amount theory and is used for preliminarily judging data on the ground, abnormal data are avoided being analyzed, bit-index refers to the bit offset of the variable and is effective only when the telemetering amount is a bit variable;
step 3, calling a decodeBuf (byte [ ] buf) function to decode the telemetry frame;
when the ground station receives the telemetering data, analyzing the received telemetering data according to the json data format of the telemetering data, marking all byte lengths and starting words of the telemetering frames at the head of the json data frame, wherein the content of the json data format corresponds to the frame content of the specific telemetering frame; checking according to the frame head of the telemetry frame, if the length of the received telemetry frame data is not wrong, acquiring the attribute of each telemetry measurement of the content in the json data format, and then decoding the corresponding data of the frame content;
step 4, operating a conversion function and analyzing data;
a python function is adopted to execute in the operation conversion process of the telemetering data, and an Ironpython operation environment is called in C #, so that a dynamic pyhton script containing operation conversion formulas corresponding to all telemetering measurements defined by a user is analyzed and operated;
step 5, displaying a result;
and determining whether to display the telemetering amount information on the ground terminal according to the visible attribute of each telemetering amount, and if the visible indicates that the telemetering amount is visible, sequentially displaying the id of the telemetering amount and the converted numerical value on the terminal.
2. The json-based method for dynamically resolving satellite telemetry data as recited in claim 1, wherein: the remote measurement is used to represent attitude and position information of the satellite, health conditions, and operating states of each component.
3. The json-based method for dynamically resolving satellite telemetry data as recited in claim 1, wherein: each telemetry frame comprises a satellite affair frame, an attitude control frame and a subsystem frame.
4. The json-based method for dynamically resolving satellite telemetry data as recited in claim 1, wherein: in step 3, decoding the corresponding data of the frame content, specifically comprising the following steps:
step 3-1, performing byte conversion and character string conversion on the telemetering amount of the telemetering frame according to the attribute of each object in the content in the json data format, wherein the content in the json data format appoints the specific attribute of each telemetering measurement, and calling a getValue (byte [ ] buf, string type, byte index, byte bit _ index, byte length h, string coeff, string vRange) function, wherein each parameter in the function is the attribute of each telemetering measurement, and buf is the data starting address of the telemetering frame to be analyzed;
step 3-2, determining whether the telemetry measurement to be analyzed is a byte, uint16, uint32 or bit data type according to the transmitted type parameter by the getValue function, then acquiring specific data at a corresponding offset position in buf according to an index attribute corresponding to the telemetry measurement, and performing corresponding operation according to the corresponding data type; if the telemetering measurement is a byte, uint16 or uint32 variable, acquiring data with corresponding length at a corresponding position in the buf telemetering frame according to the index offset, and if the telemetering measurement is a bit type variable, acquiring a telemetering measurement value with corresponding bit length at a corresponding position according to the value of the bit _ index; if the coeff attribute of the telemetering measurement is not null, the telemetering measurement is indicated to need to be subjected to corresponding numerical value conversion, and the telemetering measurement of the corresponding type corresponds to a corresponding operation function.
5. The json-based method for dynamically resolving satellite telemetry data as recited in claim 1, wherein: step 4, in the NET frame, a compiler and an execution engine of IronPython are included, an engine instance is created through C # codes, then a specified script is executed, the script already includes operation conversion formulas corresponding to all telemeasurement defined by a user, and all analysis formulas are defined in an ipyrun. C # is realized by calling a GetVariable function, wherein the GetVariable function is defined as GetVariable < T > (string name), and the string name is an operation conversion function corresponding to the telemetering amount which needs to be subjected to data processing.
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