CN113240212A - Data processing method, electronic device and medium for generating flight trajectory - Google Patents
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Abstract
The invention relates to a data processing method, electronic equipment and a medium for generating flight trajectories, which comprises the steps of S1, obtaining ADS-B original data corresponding to a target flight id at the ith moment, and generating an ith flight data candidate set; step S2, generating B based on ith flight data candidate setiAnd storing the data into a data set to be processed; step S3 based on Bi‑1And BiGenerating an ith state vector candidate distribution, and executing the step S4 when i is smaller than S, and S is smaller than N, otherwise, executing the step S5; step S4, generating the ith track point C based on the ith state vector candidate distributioni(ii) a Step S5 based on the Ci‑S、Ci‑S+1、…Ci‑1And generating an ith state vector prediction distribution based on the current prediction adjustment parameter, the ith state vector prediction distribution and the ith state vectorGenerating an ith track point by candidate distribution, and updating a prediction adjustment parameter; and step S6, generating the flight path of the target flight. The invention improves the accuracy and the smoothness of flight paths.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data processing method, an electronic device, and a medium for generating a flight trajectory of a flight.
Background
The ADS-B (Automatic Dependent Surveillance-Broadcast) is an aircraft operation monitoring technology based on a GPS (global positioning system) and air-ground and air-air communication data chain, four-dimensional position information (longitude, latitude, altitude and time) and additional information (information such as collision warning information and airline inflection points) of an aircraft and identification numbers of the aircraft can be obtained through avionic equipment such as a Global Navigation Satellite System (GNSS) and the like, and the information is broadcasted to a ground station and an air monitoring terminal in a real-time state so as to realize effective air traffic control in advance. In the prior art, flight trajectories are generally generated directly based on ADS-B data.
However, in some cases, GPS positioning is inaccurate, or GPS generates signal deviation due to satellite clock errors, influence of ionosphere and troposphere on GPS signal propagation, multipath effect, receiver thermal noise, and the like, and meanwhile, in an actual transmission process, the ADS-B signal is interfered by an external signal, which causes fluctuation or distortion of the ADS-B signal, and the ADS-B signal after being interfered by noise has a great influence on receiver decoding, so that information loss such as aircraft position and the like or errors finally cause data abnormality. In addition, most ADS-B receiving equipment does not have an anti-interference function, and in some plateau areas and other areas affected by terrain, the receiver is difficult to receive ADS-B signals basically, so that the ADS-B is absent, interrupted and unstable. In addition, there is a case where ADS-B is missing or interrupted in an area where no acceptable station is located, such as when a flight flies to a remote area, due to the unevenness of the receiving station. And because ADS-B data is fused by a plurality of sources, time confusion is often brought, and due to the existence of abnormal points, a drawn flight track has shapes of sawteeth, burrs and the like. In summary, the flight trajectory generated directly based on the ADS-B data in the prior art is poor in accuracy and not smooth enough.
Disclosure of Invention
The invention aims to provide a data processing method, electronic equipment and medium for generating a flight trajectory, and the accuracy and the smoothness of the flight trajectory are improved.
According to a first aspect of the present invention, there is provided a data processing method for generating flight trajectory of flight, including:
s1, obtaining ADS-B original data corresponding to the target flight id at the ith moment from M preset data sources, and performing filtering processing to generate an ith flight data candidate set, wherein the value of i is 1 to N, the M data sources comprise a first data source to an Mth data source, and the priority from the first data source to the Mth data source is sequentially reduced;
step S2, selecting and completing data based on the ith flight data candidate set to generate the data B to be processed corresponding to the target flight id at the ith momentiAnd storing the data to be processed into a data set to be processed corresponding to the target flight id;
step S3 based on Bi-1And BiGenerating an i-th state vector candidate distribution, wherein B0For the preset initial data to be processed of the target flight, when i is smaller than the preset predicted number S, executing the step S4, and S is smaller than N, otherwise, executing the step S5;
step S4 based onGenerating ith track point C by i-state vector candidate distributioni；
Step S5 based on the Ci-S、Ci-S+1、…Ci-1Generating ith state vector prediction distribution based on the ith state vector prediction distribution and ith state vector candidate distribution, and updating the prediction adjustment parameters based on the ith state vector prediction distribution, the ith trace point and Q trace points adjacent to the ith trace point;
and step S6, generating the flight path of the target flight based on the ith track point.
Compared with the prior art, the invention has obvious advantages and beneficial effects. By means of the technical scheme, the data processing method, the electronic equipment and the medium for generating the flight trajectory can achieve considerable technical progress and practicability, have wide industrial utilization value and at least have the following advantages:
according to the method, the candidate data of the flight with high accuracy are obtained, the data to be processed is determined based on the candidate data, the accuracy of the data to be processed is improved, each state vector candidate distribution is generated based on the data to be processed, the state vector prediction distribution is generated based on the continuous determined track points, each track point is determined by combining each state vector candidate distribution and the state vector prediction distribution, the smoothness of the flight trajectory generated is improved, in addition, the prediction adjustment parameters are updated in real time, the accuracy of the state vector prediction distribution is continuously improved, and the accuracy of the flight trajectory generated is further improved.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
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Fig. 1 is a flowchart of a data processing method for generating a flight trajectory according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description will be given with reference to the accompanying drawings and preferred embodiments of a data processing method, an electronic device and a medium for generating a flight trajectory according to the present invention.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
The embodiment of the invention provides a data processing method for generating flight trajectories, which comprises the following steps as shown in fig. 1:
s1, obtaining ADS-B original data corresponding to the target flight id at the ith moment from M preset data sources, and performing filtering processing to generate an ith flight data candidate set, wherein the value of i is 1 to N, the M data sources comprise a first data source to an Mth data source, and the priority from the first data source to the Mth data source is sequentially reduced;
it should be noted that the time interval between two adjacent moments is specifically determined according to the time interval generated by the ADS-B and the actual application requirement, wherein the time interval between the ADS-B data generated by the domestic receiving station is about 4 to 7 seconds domestically, and the ADS-B data generated by the international receiving station is about 30 seconds domestically.
Step S2, selecting and completing data based on the ith flight data candidate set to generate the data B to be processed corresponding to the target flight id at the ith momentiAnd storing the data to be processed into a data set to be processed corresponding to the target flight id;
step S3 based on Bi-1And BiGenerating an i-th state vector candidate distribution, wherein B0For the preset initial data to be processed of the target flight, when i is smaller than the preset predicted number S, executing the step S4, and S is smaller than N, otherwise, executing the step S5;
step S4, generating the ith track point C based on the ith state vector candidate distributioni；
Step S5 based on the Ci-S、Ci-S+1、…Ci-1Generating ith state vector prediction distribution based on the ith state vector prediction distribution and ith state vector candidate distribution, and updating the prediction adjustment parameters based on the ith state vector prediction distribution, the ith trace point and Q trace points adjacent to the ith trace point;
and step S6, generating the flight path of the target flight based on the ith track point.
The invention can generate the track point corresponding to each moment in real time based on steps S1-S6, thereby generating the flight track of the target flight in real time.
According to the embodiment of the invention, the candidate data of the flight with high precision is obtained, the data to be processed is determined based on the candidate data, the accuracy of the data to be processed is improved, each state vector candidate distribution is generated based on the data to be processed, the state vector prediction distribution is generated based on the continuous determined track points, each track point is determined by combining each state vector candidate distribution and the state vector prediction distribution, the smoothness of the flight trajectory generated is improved, in addition, the prediction adjustment parameters are updated in real time, the accuracy of the state vector prediction distribution is continuously improved, and the accuracy of the flight trajectory generated is further improved.
As an example, the step S1 includes:
step S11, obtaining ADS-B original data list { A) corresponding to the target flight id at the ith time from M preset data sourcesi1，Ai2，…AiMIn which AimRepresenting ADS-B original data acquired from an mth data source at the ith moment, wherein the value of M is 1 to M;
it should be noted that, the ADS-B data corresponding to the flight id obtained from the multiple data sources can ensure that the ADS-B data at each time can be obtained as comprehensively as possible, and the ADS-B data with high data quality can be selected from the multiple ADS-B data, so as to improve the accuracy of generating the flight track. It can be understood that the selection of the M data sources and the specific value of M are set comprehensively according to a number of stations for acquiring the ADS-B data, a quality requirement for the ADS-B data, and other factors arranged in a ground area corresponding to an actual flight.
Step S12, analysis AimObtaining the flight data from the data, generating corresponding flight data according to a preset flight data structure, judging whether the flight data is in a preset first threshold interval, if so, executing the step S30, otherwise, executing the step AimFiltering;
it can be understood that the ADS-B format is different due to different time algorithms, different data levels, different amount of contained ADS-B data, and the like, and thus step S12 analyzes aimThe required preset flight data is obtained, and the flight data is generated according to the unified flight data structure, so that the corresponding data structure can be set according to specific application requirements, the corresponding flight data is obtained, the unified data structure is convenient for subsequent processing, and the data processing efficiency is improved. In addition, the corresponding first threshold interval may be set based on each type of flight data, and as long as one type of flight data is not within the corresponding first threshold interval, the corresponding a is setimAnd filtering is performed, so that the accuracy of obtaining ADS-B candidate data is improved.
Step S13, based on A in the mth data sourceimThe preset flight data corresponding to the adjacent first n moments obtain AimThe fluctuation degree of flight data of the scheduled flight is preset and compared with a preset fluctuation degree threshold value, if the fluctuation degree is larger than the preset fluctuation degree threshold value, the A is compared with the AimFiltering, otherwise, adding AimAnd adding the data into the ADS-B data candidate set.
Further combining the proximity data to determine a, via step S13imWhether the data are abnormal data or not is judged, the abnormal data are filtered, and the accuracy of acquiring the flight data candidate set at each moment is further improved.
After the flight data candidate set at each moment is obtained based on the steps S11-S13, the number of the flight data candidate sets at each moment may be 0 or 0, and therefore, the flight data candidate set at each moment needs to be filtered and supplemented, unique flight data with high accuracy is determined for each moment as to-be-processed data, and the to-be-processed data set is constructed to provide accurate and reliable data for generating a flight trajectory. As an example, the step S2 includes:
step S21, traversing the ith flight data candidate set, if the ith flight data candidate set is empty, executing step S22, otherwise executing step S23;
step S21, retrieving a preset historical database based on the target flight id and the ith moment, and acquiring preset flight data corresponding to the target flight id at the corresponding historical moment as BiThe flight history data is stored in a to-be-processed data set corresponding to the target flight id, wherein the history database is used for storing a flight history track data record, and the flight history track data record comprises a data pair field consisting of the flight id, the time and preset flight data;
it can be understood that the historical track data records stored in the historical database have certain accuracy and reliability, so that the integrity of the data can be ensured based on the flight historical track data when partial time point data is missing.
Step S23, determining the candidate data corresponding to the data source with the highest priority in the ith flight data candidate set as BiAnd storing the data into the data set to be processed.
As an embodiment, the preset flight data structure includes a longitude data segment, a latitude data segment, an altitude data segment, and a speed data segment, and correspondingly, the flight data packet includes longitude, latitude, altitude, and speed, it is understood that different flight data combinations may be selected to obtain the flight trajectory according to actual calculation requirements. As an example, the step S3 includes:
step S31, Bi-1And BiCorresponding longitude, latitude, altitude and speed basePerforming decomposition processing on a preset six-dimensional state space to generate an ith state vector candidate distribution, wherein the six-dimensional state space comprises a first quadrant, a second quadrant and a third quadrant, the acceleration and the angular velocity of the first quadrant are both equal to 0, the acceleration and the angular velocity of the second quadrant are not equal to 0 and the angular velocity is not equal to 0, the acceleration and the angular velocity of the third quadrant are not equal to 0, the ith state vector is (x, y, v, a, theta and omega), x represents longitude, y represents latitude, v represents velocity, a represents acceleration, theta represents rotation angle, and omega represents angular velocity.
It should be noted that most of the existing flight trajectories are generated directly based on single-dimensional data of position information, and the flight trajectory generation method converts data into multi-dimensional data based on longitude, latitude, altitude and speed and generates flight trajectories based on the multi-dimensional data, so that the accuracy of generating flight trajectories is improved.
It can be understood that, since each dimension data is dynamically changed during the operation of the aircraft, the state vector at the next time generally conforms to a certain distribution rule, as an embodiment, the i-th state vector candidate distribution and the i-th state vector prediction distribution are both normal distributions, and the step S4 includes:
step S41, determining the ith state vector with the highest distribution probability of the ith state vector candidate as the ith target vector, and generating the ith trace point based on the state parameter corresponding to the ith target vector.
In step S5, generating an ith trace point based on the ith state vector prediction distribution and the ith state vector candidate distribution includes:
step S51, generating an ith intersection distribution based on the ith state vector prediction distribution and the ith state vector candidate distribution;
step S52, determining the ith state vector with the highest probability in the ith intersection distribution as the ith target vector, and generating the ith trace point based on the state parameter corresponding to the ith target vector.
Note that, the initial S track points are determined based directly on the ith state vector candidate distribution through step S41. Through the steps S51-S42, the state vector prediction distribution of the point and the vector candidate distribution of the point are generated from the S +1 th track point based on the S track points before the point, so that the track points are generated, the accuracy of the flight path is improved, and the smoothness of the flight path can be ensured.
As an embodiment, in step S5, updating the prediction adjustment parameter based on the ith state vector prediction distribution, the ith trace point, and Q trace points adjacent to the ith trace point includes:
step S53, determining the state vector with the highest prediction distribution probability of the ith state vector as the ith prediction state vector, and analyzing and acquiring the prediction state value corresponding to each vector dimension;
s54, acquiring a target state value corresponding to each vector dimension based on the ith track point;
step S55, determining a first variation parameter of each dimension based on the predicted state value corresponding to each vector dimension and the target state value corresponding to each vector dimension;
step S56, obtaining a target state value of each vector dimension corresponding to each adjacent point of Q track points adjacent to the ith track point, and determining a second change parameter of each vector dimension based on the target state values of each vector dimension corresponding to the ith track point and the Q track points adjacent to the ith track point;
and step S57, updating the prediction adjustment parameter of each vector dimension based on the first variation parameter and the second variation parameter.
It should be noted that, through steps S53 to S57, the prediction adjustment parameters are dynamically adjusted, and the accuracy of predicting the state vector corresponding to the next trajectory point in the subsequent calculation process is improved, so that the accuracy of generating the flight trajectory is further improved.
The flight trajectory generated by the method can be visually displayed, specifically, the flight trajectory can be displayed on a preset region based on precision and dimensionality, corresponding curve change curves are synchronously generated for the flight height and the flight speed respectively to be displayed, the flight data of each dimensionality corresponding to the current moment is displayed in real time, and user experience is improved.
An embodiment of the present invention further provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions configured to perform a method according to an embodiment of the invention.
The embodiment of the invention also provides a computer-readable storage medium, and the computer instructions are used for executing the method of the embodiment of the invention.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A data processing method for generating flight trajectories of flights is characterized by comprising the following steps:
s1, obtaining ADS-B original data corresponding to the target flight id at the ith moment from M preset data sources, and performing filtering processing to generate an ith flight data candidate set, wherein the value of i is 1 to N, the M data sources comprise a first data source to an Mth data source, and the priority from the first data source to the Mth data source is sequentially reduced;
step S2, selecting and completing data based on the ith flight data candidate set to generate the data B to be processed corresponding to the target flight id at the ith momentiAnd storing the data to be processed into a data set to be processed corresponding to the target flight id;
step S3 based on Bi-1And BiGenerating an i-th state vector candidate distribution, wherein B0For targeted flightsPresetting initial data to be processed, and executing step S4 when i is less than a preset prediction number S, wherein S is less than N, otherwise, executing step S5;
step S4, generating the ith track point C based on the ith state vector candidate distributioni；
Step S5 based on the Ci-S、Ci-S+1、…Ci-1Generating ith state vector prediction distribution based on the ith state vector prediction distribution and ith state vector candidate distribution, and updating the prediction adjustment parameters based on the ith state vector prediction distribution, the ith trace point and Q trace points adjacent to the ith trace point;
and step S6, generating the flight path of the target flight based on the ith track point.
2. The method of claim 1,
the step S1 includes:
step S11, obtaining ADS-B original data list { A) corresponding to the target flight id at the ith time from M preset data sourcesi1，Ai2，…AiMIn which AimRepresenting ADS-B original data acquired from an mth data source at the ith moment, wherein the value of M is 1 to M;
step S12, analysis AimObtaining the flight data from the data, generating corresponding flight data according to a preset flight data structure, judging whether the flight data is in a preset first threshold interval, if so, executing the step S30, otherwise, executing the step AimFiltering;
step S13, based on A in the mth data sourceimThe preset flight data corresponding to the adjacent first n moments obtain AimThe fluctuation degree of flight data of the scheduled flight is preset and compared with a preset fluctuation degree threshold value, if the fluctuation degree is larger than the preset fluctuation degree threshold value, the A is compared with the AimFiltering, otherwise, adding AimAnd adding the data into the ADS-B data candidate set.
3. The method of claim 1,
the step S2 includes:
step S21, traversing the ith flight data candidate set, if the ith flight data candidate set is empty, executing step S22, otherwise executing step S23;
step S21, retrieving a preset historical database based on the target flight id and the ith moment, and acquiring preset flight data corresponding to the target flight id at the corresponding historical moment as BiThe flight history data is stored in a to-be-processed data set corresponding to the target flight id, wherein the history database is used for storing a flight history track data record, and the flight history track data record comprises a data pair field consisting of the flight id, the time and preset flight data;
step S23, determining the candidate data corresponding to the data source with the highest priority in the ith flight data candidate set as BiAnd storing the data into the data set to be processed.
4. The method of claim 1,
the preset flight data structure includes a longitude data segment, a latitude data segment, an altitude data segment and a speed data segment, and the step S3 includes:
step S31, Bi-1And BiAnd decomposing corresponding longitude, latitude, altitude and speed in three preset motion states based on a preset six-dimensional state space to generate an ith state vector candidate distribution, wherein the three preset motion states comprise a motion state with an acceleration and an angular speed both equal to 0, a motion state with an acceleration not equal to 0 and an angular speed equal to 0, and a motion state with an acceleration not equal to 0 and an angular speed not equal to 0, the ith state vector is (x, y, v, a, theta, omega), x represents longitude, y represents latitude, v represents speed, a represents acceleration, theta represents a corner, and omega represents angular speed.
5. The method of claim 4,
the ith state vector candidate distribution and the ith state vector prediction distribution are both normal distributions.
6. The method of claim 5,
the step S4 includes:
step S41, determining the ith state vector with the highest distribution probability of the ith state vector candidate as the ith target vector, and generating the ith trace point based on the state parameter corresponding to the ith target vector.
7. The method of claim 6,
in step S5, generating an ith trace point based on the ith state vector prediction distribution and the ith state vector candidate distribution includes:
step S51, generating an ith intersection distribution based on the ith state vector prediction distribution and the ith state vector candidate distribution;
step S52, determining the ith state vector with the highest probability in the ith intersection distribution as the ith target vector, and generating the ith trace point based on the state parameter corresponding to the ith target vector.
8. The method of claim 7,
in step S5, updating the prediction adjustment parameter based on the ith state vector prediction distribution, the ith trace point, and Q trace points adjacent to the ith trace point includes:
step S53, determining the state vector with the highest prediction distribution probability of the ith state vector as the ith prediction state vector, and analyzing and acquiring the prediction state value corresponding to each vector dimension;
s54, acquiring a target state value corresponding to each vector dimension based on the ith track point;
step S55, determining a first variation parameter of each dimension based on the predicted state value corresponding to each vector dimension and the target state value corresponding to each vector dimension;
step S56, obtaining a target state value of each vector dimension corresponding to each adjacent point of Q track points adjacent to the ith track point, and determining a second change parameter of each vector dimension based on the target state values of each vector dimension corresponding to the ith track point and the Q track points adjacent to the ith track point;
and step S57, updating the prediction adjustment parameter of each vector dimension based on the first variation parameter and the second variation parameter.
9. An electronic device, comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being arranged to perform the method of any of the preceding claims 1-8.
10. A computer-readable storage medium having stored thereon computer-executable instructions for performing the method of any of the preceding claims 1-8.
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| CN114359350A (en) * | 2022-03-14 | 2022-04-15 | 中航信移动科技有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN116248170A (en) * | 2023-05-06 | 2023-06-09 | 中航信移动科技有限公司 | Multi-star positioning-based target aircraft identification method, equipment and storage medium |
| CN116321009A (en) * | 2023-05-06 | 2023-06-23 | 中航信移动科技有限公司 | Call sign identification method based on flight track, electronic equipment and storage medium |
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| CN116248170B (en) * | 2023-05-06 | 2023-07-28 | 中航信移动科技有限公司 | Multi-star positioning-based target aircraft identification method, equipment and storage medium |
| CN116321009B (en) * | 2023-05-06 | 2023-07-28 | 中航信移动科技有限公司 | Call sign identification method based on flight track, electronic equipment and storage medium |
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