CN109239750B - Method and device for monitoring track parameters and electronic terminal - Google Patents
Method and device for monitoring track parameters and electronic terminal Download PDFInfo
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- CN109239750B CN109239750B CN201811050311.3A CN201811050311A CN109239750B CN 109239750 B CN109239750 B CN 109239750B CN 201811050311 A CN201811050311 A CN 201811050311A CN 109239750 B CN109239750 B CN 109239750B
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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Abstract
The embodiment of the invention provides a method and a device for monitoring track parameters and an electronic terminal, wherein the method comprises the following steps: receiving and decoding a binary data stream of a subframe structure in a global navigation satellite system navigation message, wherein the binary data stream comprises a plurality of orbit parameters; calculating a current parameter error value of the track parameter; determining a threshold range of errors according to the error distribution model; and judging whether the error value of the current parameter is within a threshold range, if so, judging that the track parameter is normal and outputting a normal monitoring result. By the method, the precision of the orbit parameters in the navigation messages of the global navigation satellite system can be monitored in real time, and the condition of abnormal precision of the orbit parameters can be identified, so that a way for finding abnormal problems is provided, and the condition that the reason of the abnormal can not be determined is avoided.
Description
Technical Field
The invention relates to the field of monitoring and evaluation, in particular to a method and a device for monitoring track parameters and an electronic terminal.
Background
In the navigation positioning service, when the satellite signal is wrong, the user is confused and inconvenient to use, and great potential safety hazard exists. The GNSS (Global Navigation Satellite System) monitoring refers to monitoring any anomaly which may affect the performance of the Navigation positioning service in the GNSS, and has great importance and application value. At present, the monitoring of the GNSS at home and abroad puts the focus on the evaluation work of the use result, but the monitoring is not substantial, the reason can not be known when the satellite data is abnormal, and good service can not be provided for users.
Disclosure of Invention
In view of the above, an object of the embodiments of the present invention is to provide a method and an apparatus for monitoring track parameters, and an electronic terminal.
In a first aspect, an embodiment of the present invention provides a method for monitoring an orbit parameter, which is applied to a global navigation satellite system, and the method includes:
receiving and decoding a binary data stream of a subframe structure in a GNSS navigation message, wherein the binary data stream comprises a plurality of track parameters;
calculating a current parameter error value of the track parameter;
determining a threshold range of errors according to the error distribution model;
and judging whether the error value of the current parameter is within a threshold range, if so, judging that the track parameter is normal and outputting a normal monitoring result.
In a second aspect, an embodiment of the present invention provides an apparatus for monitoring a track parameter, where the apparatus includes:
the GNSS navigation device comprises a receiving module, a decoding module and a processing module, wherein the receiving module is used for receiving and decoding a binary data stream of a subframe structure in a GNSS navigation message, and the binary data stream comprises a plurality of track parameters;
the first calculation module is used for calculating a current parameter error value of the track parameter;
the second calculation module is used for determining the threshold range of the error according to the error distribution model;
the first judgment module is used for judging whether the error value of the current parameter is in a threshold range or not;
and the output module is used for outputting the monitoring result.
In a third aspect, an embodiment of the present invention provides an electronic terminal, including:
a memory;
a processor;
the memory is used for storing programs that support the processor to execute the above-mentioned methods, and the processor is configured to execute the programs stored in the memory.
Compared with the prior art, the method, the device and the electronic terminal for monitoring the orbit parameters in the embodiment of the invention receive and decode the GNSS navigation message to obtain the orbit parameters in the binary data stream, and further perform error analysis on the orbit parameters. By judging whether the current error value of the track parameter is within a threshold range, whether the precision of the obtained track parameter is normal can be judged. For example, the threshold range may be determined by an adaptive distribution model determined by long-term evolution characteristics. By the method, the precision of the track parameters in the GNSS navigation messages can be monitored in real time, the condition of track parameter precision abnormity can be identified, an approach for finding the abnormity problem is provided, and the condition that the abnormity reason cannot be determined is avoided. For example, when the accuracy is abnormal, the abnormal condition can be sent to a relevant mechanism to solve the abnormal condition, and a user who uses the receiver for positioning can be informed, so that the user can know that the satellite data is abnormal at the moment, and the satellite navigation is facilitated to provide better service for the user.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a block diagram of an electronic terminal according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for monitoring a track parameter according to an embodiment of the present invention.
Fig. 3 is a detailed flowchart of determining an error distribution model according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of an error distribution curve according to an embodiment of the present invention.
Fig. 5 is a functional block diagram of an apparatus for monitoring a track parameter according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
GNSS (Global Navigation Satellite System) refers to all Satellite Navigation systems in general, including Global, regional, and enhanced, such as the united states GPS (Global Positioning System), russian Glonass (Glonass Satellite Navigation System), European Galileo (Galileo Satellite Navigation System), chinese beidou Satellite Navigation System, and related enhanced systems, such as the united states WAAS (Wide Area Augmentation System), European EGNOS (European Geostationary Navigation Overlay Service), and japanese MSAS (Multi-Functional Satellite Augmentation System), and the like, and also covers other Satellite Navigation systems to be built and later. The international GNSS system is a complex combined system with multiple systems, multiple planes and multiple modes. GNSS systems encompass multiple navigation satellites.
The GNSS navigation messages are messages broadcast by the navigation satellites to users for describing the operating state parameters of the navigation satellites, and include time parameters and orbit parameters. Time information can be obtained using the time parameters, and position coordinates and velocity of the receiver user can be calculated using the orbit parameters.
Fig. 1 is a block diagram of an electronic terminal 100 according to an embodiment of the present invention. The electronic terminal 100 includes a device 110 for monitoring track parameters, a memory 120, a storage controller 130, a processor 140, an antenna 150, a display unit 160, and the like. The memory 120, the memory controller 130, the processor 140, the antenna 150, and the display unit 160 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The device 110 for monitoring track parameters comprises at least one software functional module which can be stored in the memory 120 in the form of software or firmware (firmware) or is fixed in an Operating System (OS) of the electronic terminal 100. The processor 140 is configured to execute an executable module stored in the memory 120, such as a software functional module or a computer program included in the apparatus for monitoring track parameters 110.
The Memory 120 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 120 is used for storing a program, and the processor 140 executes the program after receiving an execution instruction. Access to memory 120 by processor 140, and possibly other components, may be under the control of memory controller 130. The method executed by the electronic terminal 100 according to the process definition disclosed in any embodiment of the present invention can be applied to the processor 140, or implemented by the processor 140.
The processor 140 may be an integrated circuit chip having signal processing capabilities. The Processor 140 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor 140 may be any conventional processor or the like.
The antenna 150 is used for receiving signals of navigation satellites.
The display unit 160 is used to display the image data for user reference. In this embodiment, the display unit 160 may be a liquid crystal display or a touch display. In the case of a touch display, the display can be a capacitive touch screen or a resistive touch screen, which supports single-point and multi-point touch operations. Supporting single-point and multi-point touch operations means that the touch display can sense touch operations from one or more locations on the touch display at the same time, and the sensed touch operations are sent to the processor 140 for calculation and processing.
It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic terminal 100. For example, the electronic terminal 100 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1. In the embodiment of the present invention, the electronic terminal 100 may be a device capable of connecting to a network and having an operation processing capability, such as a server, a personal computer, or a mobile device. In some embodiments, the terminal device may also not be limited to a physical device, and may also be a virtual machine, a virtual server, or the like, for example. In this embodiment, the electronic terminal 100 may be configured to receive a radio signal broadcast by a navigation satellite, and perform calculation and analysis according to the received signal to obtain a monitoring result. Further, the electronic terminal 100 may also be in communication connection with an external device to send the monitoring result to the external device.
Please refer to fig. 2, which is a flowchart illustrating a method for monitoring track parameters applied to the electronic terminal 100 shown in fig. 1 according to an embodiment of the present invention. In this embodiment, the method may be applied to a global navigation satellite system. The specific process shown in fig. 2 will be described in detail below.
Step S210: receiving and decoding a binary data stream of a subframe structure in a GNSS navigation message, wherein the binary data stream comprises a plurality of orbit parameters.
Step S220: calculating a current parameter error value for the orbit parameter.
Step S230: and determining the threshold range of the error according to the error distribution model.
Step S240: and judging whether the error value of the current parameter is within a threshold range, if so, judging that the track parameter is normal and outputting a normal monitoring result.
In this embodiment, the electronic terminal 100 may receive a radio signal sent by a navigation satellite, and further perform monitoring according to the received signal, particularly calculate an orbit parameter sent by the navigation satellite and monitor the accuracy of the orbit parameter. When the accuracy is identified to be abnormal, the abnormal situation can be sent to a related mechanism to solve the abnormal situation, and a user using a receiver to position can be informed, so that the user can know that the satellite data is abnormal at the moment, the satellite navigation can better serve the user, a way for finding the abnormal problem is provided, and the situation that the abnormal reason cannot be determined is avoided.
Specifically, the orbit parameters can be obtained by receiving the GNSS navigation messages in real time and decoding the binary data stream of the subframe structure in the navigation messages. For example, a receiver capable of receiving GNSS navigation message data streams may be used to acquire data in real time, and then the data acquired by the receiver may be decoded by using the prior art, where the decoding process needs to be performed according to a coding protocol or a coding rule matched with the receiver. The binary data stream of the subframe structure is a data sequence represented by 0 and 1 transmitted by a GNSS satellite in a frame unit.
The orbit parameters comprise parameters such as an orbit inclination angle, an eccentricity ratio and a rising intersection right ascension, and the instantaneous position coordinates of the satellite can be calculated through the parameters such as the orbit inclination angle, the eccentricity ratio and the rising intersection right ascension in the orbit parameters, so that the coordinates of a receiver user can be solved. Due to various reasons such as uncertainty of signal transmission itself and transmission hysteresis, it is necessary to further analyze the decoded binary data stream. For example, it may be determined whether the binary code of each track parameter is abnormal, and if so, an abnormal result may be output, otherwise, each track parameter may be further analyzed. For example, errors or accuracy of the orbit parameters may be monitored. And then aiming at the basic characteristics of the track parameter errors, obtaining a threshold range of the errors by the distribution model determined based on the long-term evolution characteristics of the errors, if the error value of the current track parameter falls in the threshold range, considering that the track parameter is normal, and outputting a monitoring result, otherwise, considering that the track parameter is abnormal, and outputting an abnormal result.
By the method, the precision of the orbit parameters in the GNSS navigation message can be monitored in real time, and abnormal results can be output in time when the numerical values of the orbit parameters are abnormal, so that satellite navigation can provide better service for users.
In this embodiment, after step S210, the method further includes step S211 to step S212.
Step S211: and judging whether the binary coding of the track parameters is abnormal or not.
Step S212: if yes, outputting a first abnormal result.
The first exception result includes an exception type and an exception binary data stream. Whether the encoding is abnormal or the numerical precision is abnormal when the track parameters are abnormal can be distinguished by judging whether the binary encoding of the track parameters is abnormal or not.
Wherein, the step S211 comprises steps S2111-S2114.
Step S2111: and acquiring an interface control file of the GNSS platform, wherein the interface control file specifies the encoding rule of the orbit parameters.
Step S2112: and obtaining the data bit and the coding bit information of the track parameter according to the decoded binary data stream.
Step S2113: and judging whether the data bit and the coding bit information meet the coding rule.
Step S2114: if so, judging that the binary coding of the track parameters is normal, otherwise, judging that the binary coding of the track parameters is abnormal.
The GNSS platform may be a platform of any one of the above satellite navigation systems, for example, a GPS platform, or a platform of a beidou satellite navigation system. Each satellite navigation system may provide an Interface Control file (ICD), which is a file used for constraining, standardizing, and defining a signal Interface between a navigation satellite and a receiving terminal, where an encoding rule of an orbit parameter is specified in the Interface Control file, and a requirement is made on a data bit and an encoding bit of the orbit parameter.
Specifically, all bytes in a binary data stream of a subframe structure are read in a circulating manner, a synchronous head of two adjacent bytes is searched first, then the next two bytes are read to judge the type of a coding rule or a coding protocol, then the number of bytes occupied by information content at a certain moment and a stored binary coding value are read, whether the data bit of each parameter in the subframe structure is consistent with the requirement of the coding rule or not is compared, if the data bit, the coding bit information and the requirement of the coding rule are consistent, the coding rule is judged to be met, and if not, the coding rule is judged not to be met.
The data bit and the coding bit information of the track parameter can be obtained by decoding the received binary data stream, and the data bit and the coding bit information are compared with the coding rule in the interface control file, so that whether the track parameter obtained by decoding meets the coding rule or not can be judged, and the condition that the coding rule indicates that the binary coding of the track parameter is normal; otherwise, the binary coding of the track parameter is abnormal.
In this embodiment, step S220 includes steps S221 to S223.
Step S221: and calculating the current spatial position of the single star according to the decoded orbit parameters.
Step S222: and acquiring a theoretical spatial position through the track precision data.
Step S223: and obtaining a current parameter error value of the track parameter according to the current space position and the theoretical space position.
In one embodiment, the current spatial position (X) of the satellite A can be calculated by the parameters of the orbit inclination, eccentricity, ascension of the ascending point, and the like in the orbit parameters1,Y1,Z1) And comparing the current spatial position (X)1,Y1,Z1) As a measured value; the orbit precision data can be directly acquired through the existing real-time release center of the orbit precision product, wherein the orbit precision data comprises the theoretical space position (X) of the satellite A2,Y2,Z2). The theoretical spatial position of the satellite is provided by using computing mechanisms of some real-time products, most of which are authenticated, and the issued data has high reliability. Such as the IGS agency in the united states, the iGMAS agency in china, etc., can provide precise products, precise data in real time. The precision product and precision data have a theoretical spatial position of a satellite, and the precision product and precision data have high precision, so that the theoretical spatial position (X) is2,Y2,Z2) May be considered theoretical true or reference values. It should be noted that in other embodiments, the track precision data may be provided by a third party platform or organization.
By comparing the measured values (X)1,Y1,Z1) And theoretical truth (X)2,Y2,Z2) The difference is calculated to obtain the current parameter error value (Δ X, Δ Y, Δ Z) of the corresponding orbit parameter in the navigation message. Comparing the current parameter error value with the threshold range of the error obtained by statistics, whether the precision of the track parameter is abnormal can be further known.
In this embodiment, the step S230 includes steps S231 to S232.
Step S231: a distribution function is determined based on the error distribution model.
Step S232: and determining the threshold range of the error according to the distribution function and the preset fault-tolerant probability.
The error distribution model is determined according to the long-term evolution characteristics of the error values of the orbit parameters, and the error distribution model is determined by historical orbit parameter data in the actual evolution process. Specifically, an error distribution curve can be drawn according to a large number of error parameter value samples, and an error distribution model is determined through error distribution curve fitting. The error distribution model may be subject to normal distribution, T distribution, and other distributions. After the error distribution model is obtained, the distribution function and parameters in the distribution function can be determined according to the distribution type to be followed. For example, if the error distribution model obeys normal distribution, the distribution function may be determined to be a normal distribution function, and further, the standard deviation σ and the mean μ of the important parameters of the function may be determined.
And obtaining the threshold range of the error according to the preset fault tolerance probability and each parameter of the distribution function. In one example, the given fault tolerance probability may be 0.98, or may be 0.95, or may be 0.9.
The essence of the precision monitoring described in this embodiment is that, under the condition of determination of an error distribution model, abnormal conditions are monitored in real time under a certain fault-tolerant probability. Therefore, the envelope boundary other than the fault tolerance probability is mainly concerned, and if the fault tolerance probability is P, the envelope boundary is (1-P), that is, the range in which the precision monitoring is required.
As shown in fig. 3, before step S230, the method further includes: an error distribution model is determined. Wherein the step of determining the error distribution model comprises steps S310 to S340.
Step S310: importing historical track parameter data, wherein the historical track parameter data comprise all track parameters in a preset time period from the current moment to the previous moment.
Step S320: and sampling the historical track parameter data according to a preset time period to obtain a historical parameter error value.
Step S330: and obtaining an error distribution curve according to the historical parameter error value.
Step S340: and performing hypothesis test on the historical parameter error values, and determining an error distribution model to which the historical parameter error values obey according to the error distribution curve fitting.
The preset time period may be one year, two years, three years or five years. The size of the preset time period can be arbitrarily selected by those skilled in the art according to actual needs to obtain the track parameter data in different time periods.
In one embodiment, first orbit data is imported, the first orbit data includes all orbit parameters in a first time period, a first parameter error value corresponding to the first orbit data is obtained according to a preset time period, and a frequency distribution histogram and an error distribution curve are obtained according to the first parameter error value; hypothesis testing is performed on the first parameter error values to determine a first distribution model to which the first parameter error values are subject. Importing second track parameter data in a preset time period from the current moment; acquiring a second parameter error value corresponding to the second track parameter data according to a preset time period; substituting the second parameter error value into the first distribution model to determine a final error distribution model. The reliability of the error distribution model can be improved by importing historical orbit parameter data in different time periods for multiple times.
For example, the data of the historical orbit parameters in the last 5 years can be imported twice, so as to obtain a first distribution model, and the general shape of the error distribution model can be obtained through the first distribution model; and importing historical orbit parameter data in the last 2 years to obtain a second distribution model, and observing whether the second distribution model and the first distribution model meet the same distribution characteristics, if so, calculating a threshold range for the second distribution model, and calculating the threshold range for the first distribution model. The second distribution model is selected, so that the data size is sufficient, the sampling precision can be met, and the error distribution model meeting the requirement can be obtained. The calculation is less intensive than selecting the first distribution model.
A preliminary error distribution model can be provided through the first distribution model, on the basis of the first distribution model, a more accurate error distribution model can be determined through the second distribution model, and high reliability is achieved.
In another embodiment, the historical orbit parameter data may be imported only once to directly obtain the error distribution model. For example, historical orbit parameter data over two or five years may be imported directly to generate an error distribution model. This has the advantage that the process steps can be simplified.
After a large amount of historical orbit parameter data are imported, a large amount of historical orbit parameter data are sampled. The sampling period may be a preset time period, for example, 0.1 second, 1 second, 30 seconds, 60 seconds, or two minutes. The real-time statistics of the parameter error value can be realized by sampling according to the preset time period, and a person skilled in the art can randomly select the size of the preset time period according to actual needs so as to sample according to the preset time period and realize the real-time statistics.
In one example, the track parameter data within 5 years from the current time is sampled at intervals of 30 seconds to obtain parameter error values, and all the parameter error values within 5 years can be used as the first sample. The method for obtaining the parameter error value may be calculated by referring to the method related to the current parameter error value. According to the first sample, a frequency distribution histogram and an error distribution curve of parameter error values of a single star can be drawn.
The error distribution model which meets the parameter error value within 5 years can be fitted and determined through the error distribution curve, and meanwhile, hypothesis test verification is carried out on the parameter error value within 5 years, so that the accuracy of the error distribution model can be improved.
And sampling the track parameter data in the previous two years from the current time according to the time interval of 30 seconds to obtain parameter error values, wherein all the parameter error values in the 2 years can be used as second samples. And obtaining a frequency distribution histogram and an error distribution curve of the parameter error value of the single star according to the second sample to obtain an error distribution model at the current moment, wherein the error distribution model at the current moment is similar to the error distribution model obtained through the first sample.
The above manner may be understood as dynamically determining an evolving error distribution model in a time window sliding manner, where the window length in the above example is two years. The window length may be 1 year, 2 years, or 5 years. The window length can be set arbitrarily by those skilled in the art according to the actual needs.
As shown in fig. 4, the histogram of the frequency distribution and the graph of the error distribution are plotted when a single satellite runs in a certain direction, which are obtained by taking 2016 and 2017 historical orbit parameter data and 60-second sampling intervals as an example. "C05" in fig. 4 indicates the symbol of the star, and "X" indicates the pointing direction of the star in the X-axis direction under a coordinate system, which may be the WGS84 coordinate system. By carrying out statistical analysis on the graph, a corresponding error distribution model and a distribution function can be obtained, and a threshold range of errors can be further obtained by combining preset fault-tolerant probability.
The dynamic error distribution model determined by the long-term evolution characteristics can be obtained through the method, the obtained error distribution model has high reliability and accuracy, the distribution function of the error distribution model can be determined based on the error distribution model at the current moment after hypothesis verification, and various parameters in the distribution function, such as the standard deviation sigma and the mean value mu, are further calculated. The threshold range of the error can be determined by the individual parameter values of the distribution function and the given fault tolerance probability. By comparing the error value of the current parameter with the threshold range of the calculated error, it can be determined whether the numerical precision of the current orbit parameter is abnormal, i.e. whether the precision of the current orbit parameter is abnormal.
Because the theoretical distribution model obeyed by the parameter error value is determined based on the long-term evolution characteristic of the parameter error value, the error value is more accurately judged by the method for solving the threshold value through the self-adaptive sliding window. The method not only considers the characteristics of the track parameters, but also reduces the possibility of misjudgment or misjudgment of abnormal conditions by combining hypothesis testing in probability theory according to a mathematical distribution model determined by a large number of samples.
In this embodiment, the method further includes:
when it is determined that the current parameter error value is on a threshold range boundary or outside a threshold range, a second abnormal result is output. If the error value of the current parameter is within the threshold value range, judging that the current track parameter is normal, and outputting a monitoring result; otherwise, judging that the current track parameters are abnormal, and outputting a monitoring result. The second exception result includes an exception type and exception data.
By the method, the monitoring result can be output under the condition that the numerical precision of the current track parameter is abnormal, so that the abnormal type and the abnormal data of the track parameter of related units, organizations and enterprises can be informed.
Fig. 5 is a schematic functional block diagram of the apparatus 110 for monitoring track parameters shown in fig. 2 according to an embodiment of the present invention. The device 110 for monitoring the track parameter includes a receiving module 111, a first calculating module 112, a second calculating module 113, a first judging module 114, and an output module 115.
The receiving module 111 is configured to receive and decode a binary data stream of a subframe structure in a GNSS navigation message of a global navigation satellite system, where the binary data stream includes a plurality of orbit parameters.
A first calculation module 112 is configured to calculate a current parameter error value of the orbit parameter.
And a second calculating module 113, configured to determine a threshold range of the error according to the error distribution model.
The first determining module 114 is configured to determine whether the current parameter error value is within a threshold range.
And an output module 115, configured to output a monitoring result.
In this embodiment, the apparatus 110 for monitoring a track parameter further includes a second determining module, where the second determining module is configured to determine whether a binary code of the track parameter is abnormal, and the output module 115 is further configured to output a first abnormal result.
The second judging module comprises an obtaining module and a sub-judging module. The acquisition module is used for acquiring an interface control file of the GNSS platform, wherein the interface control file specifies an encoding rule of the track parameter, and is also used for acquiring data bit and encoding bit information of the track parameter according to the decoded binary data stream. The sub-judgment module is used for judging whether the data bit and the coding bit information meet the coding rule, if so, judging that the binary coding of the track parameter is normal, and otherwise, judging that the binary coding of the track parameter is abnormal.
The first calculation module 112 is further configured to determine a distribution function based on the error distribution model, and determine a threshold range of the error according to the distribution function and a preset fault tolerance probability.
In this embodiment, the apparatus 110 for monitoring track parameters further includes an importing module, a sampling module, a generating module, and a verifying module.
The importing module is used for importing historical track parameter data, and the historical track parameter data comprise all track parameters in a preset time period from the current moment to the previous moment.
The sampling module is used for sampling the historical track parameter data according to a preset time period so as to obtain a historical parameter error value.
And the generating module is used for obtaining an error distribution curve and a frequency distribution histogram according to the historical parameter error value.
And the verification module is used for carrying out hypothesis test on the historical parameter error value and determining an error distribution model obeyed by the historical parameter error value according to the error distribution curve fitting.
Wherein the output module 115 is further configured to output a second abnormal result when the current parameter error value is determined to be on a threshold range boundary or outside a threshold range.
For further details of the apparatus 110 for monitoring track parameters, reference may be made to the foregoing description related to the method for monitoring track parameters, which is not repeated herein.
In summary, according to the method, the apparatus, and the electronic terminal for monitoring the orbit parameter provided by the embodiments of the present invention, the threshold range of the error can be obtained based on the error distribution model dynamically determined by the long-term evolution characteristics according to the basic characteristics of the orbit parameter error, and whether the numerical precision of the orbit parameter is abnormal can be determined by comparing the current parameter error value of the orbit parameter at the current moment with the threshold range, so as to implement real-time monitoring of the orbit parameter. Wherein the information of the orbit parameter can be obtained by receiving and decoding a binary data stream in the GNSS navigation message. If the encoding mode of the obtained track parameters does not meet the encoding rule in the interface control file, the current track parameter encoding abnormity can be judged and the monitoring result is output. And the numerical precision of the orbit parameters can be continuously monitored in real time under the condition that the encoding of the orbit parameters is normal. By the method, on one hand, the precision of the orbit parameters in the GNSS navigation message can be monitored in real time, and satellite navigation positioning is facilitated to provide better service for users. On the other hand, since the adaptive sliding window is used in the monitoring process to calculate the threshold range, the accurate and reasonable threshold range is calculated through a theoretical error distribution model to which the parameter error value obeys. The theoretical distribution model to which the parameter error value obeys is determined based on the long-term evolution characteristic of the parameter error value, and the error value is more accurately judged by the method for solving the threshold value through the self-adaptive sliding window. The method not only considers the characteristics of the orbit parameters, but also reduces the possibility of abnormal misjudgment or misjudgment by combining hypothesis testing in probability theory according to a mathematical distribution model determined by a large number of samples. On the other hand, whether the track parameter abnormity is coding abnormity or numerical precision abnormity can be judged in a distinguishing way through the method, and the two abnormal conditions can be automatically monitored respectively.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A method for monitoring orbital parameters, the method being applied to a global navigation satellite system, the method comprising:
receiving and decoding a binary data stream of a subframe structure in a global navigation satellite system navigation message, wherein the binary data stream comprises a plurality of orbit parameters;
calculating a current parameter error value of the track parameter;
determining an error distribution model comprising: importing historical orbit parameter data, wherein the historical orbit parameter data comprise all orbit parameters in a preset time period from the current moment to the previous moment; sampling the historical orbit parameter data according to a preset time period to obtain a historical parameter error value; obtaining an error distribution curve according to the historical parameter error value; performing hypothesis test on the historical parameter error value, and fitting and determining an error distribution model obeyed by the historical parameter error value according to the error distribution curve;
the performing hypothesis testing on the historical parameter error values and determining an error distribution model to which the historical parameter error values comply according to the error distribution curve fitting includes:
importing first orbit data, acquiring a first parameter error value corresponding to the first orbit data according to a preset time period, and obtaining a frequency distribution histogram and an error distribution curve according to the first parameter error value; performing a hypothesis test on the first parameter error value to determine a first distribution model to which the first parameter error value is subject, the first orbit data comprising all orbit parameters over a first time period;
importing second track parameter data in a preset time period from the current moment; acquiring a second parameter error value corresponding to the second track parameter data according to a preset time period; substituting the second parameter error value into the first distribution model to determine the error distribution model;
determining a distribution function based on the error distribution model;
determining the threshold range of the error according to the distribution function and the preset fault-tolerant probability;
and judging whether the error value of the current parameter is within a threshold range, if so, judging that the track parameter is normal and outputting a normal monitoring result.
2. The method of monitoring orbital parameters of claim 1, wherein after the step of receiving and decoding a binary data stream of a sub-frame structure in a global navigation satellite system navigation message, the method further comprises:
judging whether the binary coding of the track parameter is abnormal or not;
if yes, outputting a first abnormal result.
3. The method of monitoring an orbital parameter of claim 2 wherein said step of determining whether said binary encoding of said orbital parameter is abnormal comprises:
acquiring an interface control file of a global navigation satellite system platform, wherein the interface control file specifies an encoding rule of an orbit parameter;
obtaining data bits and coding bit information of the track parameters according to the decoded binary data stream;
judging whether the data bit and the coding bit information meet the coding rule;
if so, judging that the binary coding of the track parameters is normal, otherwise, judging that the binary coding of the track parameters is abnormal.
4. The method of monitoring an orbit parameter of claim 1, wherein the step of calculating a current parameter error value for the orbit parameter comprises:
calculating the current spatial position of a single star according to the track parameters obtained after decoding;
acquiring a theoretical spatial position through track precision data;
and obtaining a current parameter error value of the track parameter according to the current space position and the theoretical space position.
5. The method of monitoring orbital parameters of claim 1, further comprising:
when it is determined that the current parameter error value is on a threshold range boundary or outside a threshold range, a second abnormal result is output.
6. The method for monitoring orbital parameters of claim 1, wherein the binary data stream of the sub-frame structure is a data sequence denoted by 0, 1 transmitted in frame units by satellites in a global navigation satellite system.
7. An apparatus for monitoring a rail parameter, the apparatus comprising:
the receiving module is used for receiving and decoding a binary data stream of a subframe structure in a global navigation satellite system navigation message, wherein the binary data stream comprises a plurality of orbit parameters;
the first calculation module is used for calculating a current parameter error value of the track parameter;
the system comprises an importing module, a processing module and a processing module, wherein the importing module is used for importing historical track parameter data, and the historical track parameter data comprise all track parameters in a preset time period from the current moment;
the sampling module is used for sampling the historical track parameter data according to a preset time period to obtain a historical parameter error value;
the generating module is used for obtaining an error distribution curve according to the historical parameter error value;
the verification module is used for carrying out hypothesis test on the historical parameter error value and determining an error distribution model obeyed by the historical parameter error value according to the error distribution curve fitting;
the verification module is further configured to import first orbit data, obtain a first parameter error value corresponding to the first orbit data according to a preset time period, and obtain a frequency distribution histogram and an error distribution curve according to the first parameter error value; performing a hypothesis test on the first parameter error value to determine a first distribution model to which the first parameter error value is subject, the first orbit data comprising all orbit parameters over a first time period; importing second track parameter data in a preset time period from the current moment; acquiring a second parameter error value corresponding to the second track parameter data according to a preset time period; substituting the second parameter error value into the first distribution model to determine the error distribution model;
a second calculation module for determining a distribution function based on the error distribution model; determining the threshold range of the error according to the distribution function and the preset fault-tolerant probability;
the first judgment module is used for judging whether the error value of the current parameter is in a threshold range or not;
and the output module is used for outputting the monitoring result.
8. An electronic terminal, comprising:
a memory;
a processor;
the memory is for storing a program that enables a processor configured to execute the program stored in the memory to perform the method of any one of claims 1 to 6.
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