A high-concurrency fusion method and system for multi-source apron data based on messages

Abstract

The present invention belongs to the technical field of civil aviation data processing, and discloses a high-concurrency fusion method and system for multi-source apron data based on messages. After receiving a binary message, the method constructs a link context, and parses and post-processes the message and its processing results by chain-calling a multi-protocol parsing processor on the line; each multi-protocol parsing processor has a built-in judger to distinguish whether the incoming data entity is processed by this multi-protocol parsing processor; after the link execution processing is completed, the final data entity object is obtained, and the binary message includes an ASTERIX message and a JT808 message. The ASTERIX message is the surface aircraft position message data, and the JT808 message is the surface vehicle position data. The present invention forms a unified management of multi-source data, forms a unified data entity structure for the analysis results of multi-source data, and solves the problem of inconsistency of multi-source data entities.

Description

Technical Field

[0001] The present invention belongs to the technical field of civil aviation data processing, and in particular relates to a message-based high-concurrency fusion method and system for multi-source apron data. Background Art

[0002] With the development of civil aviation, the number of flights at large airports has increased year by year. The traditional single-source apron data fusion method has single data and low effectiveness in specific scenarios. Surface monitoring and multi-point positioning technology are increasingly widely used. However, there are many problems in the analysis of related protocols and the selection of multi-source data effectiveness.

[0003] The apron data from different sources and protocols are received and processed separately, without combining the advantages of multi-source data to conduct analysis and judgment to obtain the most effective real-time scene data in different scenarios.

[0004] The invention patent discloses an ADS-B data fusion method (publication number CN104954063A, publication date 20150930), including: (i) obtaining all ADS-B data about a specific target from a new round of received data, the data may include the time scale and longitude and latitude about the specific target, and the longitude and latitude information in the data can be mapped to distance information; the data comes from at least one ADS-B ground receiving station, and the specific target refers to any one of all aircraft that can be received by the ADS-B ground receiving station; (ii) grouping all data of the specific target according to predetermined attributes, the predetermined attributes at least include the time scale and distance corresponding to the data; and if the attribute difference of the data is within a predetermined range, the data is divided into a data group; wherein at least one data group forms a grouped data set; (iii) sorting the grouped data sets after grouping according to the data groups; wherein each data group is provided with a timestamp, and the timestamp is provided to be the time parameter with the earliest time stamp among all the data in the data group; (iv) judging in turn in the grouped data sets after grouping whether the data about the specific target in the current data group has data that meets the requirements; if there is data that meets the requirements, executing step (v); if there is no data that meets the requirements, judging in turn whether the next group of data groups in the grouped data set has data that meets the requirements; if there is data that meets the requirements, executing step (v); if there is no data that meets the requirements in the grouped data sets, terminating the data fusion; (v) outputting the fused data of the specific target from the current data group.

[0005] According to the analysis of existing patents, there is a lack of corresponding research on the fusion of multi-source scene data. Traditional scene data does not consider true multi-source fusion, but only processes unit data and screens at the data quality level.

[0006] Through the above analysis, the problems and defects of the existing technology are: the existing technology does not have a unified multi-protocol family data analysis, cannot compare and analyze multi-source data based on different scenarios at the same time, and cannot provide reliable and accurate integrated data for apron surface operations. Summary of the invention

[0007] In order to overcome the problems existing in the related art, the disclosed embodiment of the present invention provides a method and system for high-concurrency fusion of multi-source apron data based on messages, and specifically relates to a method and system for high-concurrency fusion of multi-source apron data based on ASTERIX and JT808 messages. In particular, it relates to special data analysis and high-concurrency data fusion methods. The purpose of the present invention is to unify the analysis of multi-protocol family data, and at the same time, based on different scenarios, compare and analyze multi-source data to provide reliable and accurate fusion data for apron surface operations.

[0008] The technical scheme is as follows: a high-concurrency fusion method for multi-source apron data based on messages, which constructs a link context after receiving a binary message, and parses and post-processes the message and the processing result by chain-calling the protocol parsing processor on the line; each multi-protocol parsing processor has a built-in judger to distinguish whether the incoming data entity is processed by the protocol parsing processor; after the link execution processing is completed, the final data entity object is obtained, the binary message includes ASTERIX message and JT808 message, the ASTERIX message is the surface aircraft position message data, and the JT808 message is the surface vehicle position data; the method specifically includes the following steps:

[0009] S1, build a multi-protocol parsing processor, detect the types of messages of different protocol families through message type detectors, and parse the message content through corresponding message parsers for different types;

[0010] S2, for different source data received by the apron, a priority grid determination method is used to perform real-time, accurate and consistent analysis of different source data; the different source data received by the apron include: ADS-B data, multi-point positioning data, and field monitoring radar data;

[0011] S3, based on the parsed different source data, is packaged into the same data entity object.

[0012] Furthermore, a link context is constructed, including: adopting the reactor programming mode and asynchronous message-driven execution of working link nodes.

[0013] In step S1, the types of messages of different protocol families are detected by the message type detector. For MESSAGE_ASTERIX_DETECTER, the 8 bits from the beginning of the message and the last 8 bits from the end of the message are intercepted. If the first 8 bits and the last 8 bits are not 0x7e and the decimal value is between 000-127, it is determined to be an ASTERIX message.

[0014] For MESSAGE_JT808_DETECTER, intercept 8 bits from the beginning of the message and intercept the last 8 bits from the end of the message; if the first 8 bits and the last 8 bits are both 0x7e, it is determined to be a JT808 message.

[0015] In step S2, for ADS-B data, the response mode includes A mode, C mode and S mode; A mode includes response identity information, B mode includes response identity information and pressure altitude information; S mode includes broadcast identity information and four-dimensional information of initial position altitude; if the ADS-B response mode is not S mode, or the aircraft transponder does not have ADS-B response function, multi-point positioning data and field monitoring radar data are used;

[0016] The multi-lateration positioning data includes: receiving aircraft transponder signals through ground receivers and determining the aircraft position based on the signal reception time difference;

[0017] The field monitoring radar data includes: coverage runway, contact road and apron data.

[0018] Furthermore, the different source data received by the aircraft transponder are processed as follows:

[0019] Step 1: Prepare different source data;

[0020] Step 2: In DATA_CONFLICT_RESOLVER, multi-point positioning and ADS-B data priority grid determination methods are used to perform real-time, accurate, and consistent analysis of different source data.

[0021] In step 1, prepare different source data, including:

[0022] (1) Determine the ADS-B transponder turning off habits of different airlines based on historical ADS-B data;

[0023] (2) Draw a hotspot map of the radar location data based on the radar’s historical data to determine the radar’s detection range;

[0024] (3) Compare the multi-point positioning data with the ADS-B data, determine the difference between the multi-point positioning accuracy and the ADS-B data accuracy based on the region, and draw a regional grid.

[0025] In step 2, multi-point positioning and ADS-B data priority grid determination methods are used to perform real-time, accurate, and consistent analysis of data from different sources, including:

[0026] (i) Access the message data, parse the position data, and put it into the specified type of cache; construct three types of position caches for the aircraft position data, including: ADS-B data position cache, field monitoring radar data position cache, and multi-point data position cache;

[0027] (ii) If it is ADS-B message data, determine whether it is within the high-precision range of the field monitoring radar. If it is within the high-precision range and the time stamp of the field monitoring radar data is less than 1 second from the current time stamp, the field monitoring radar data is used; if it is not within the high-precision range of the field monitoring radar, multi-point positioning and ADS-B data priority grid are used for determination; for large airports, the GeoHash algorithm is used for search, the longitude and latitude are encoded, and the corresponding grid of the location is locked through the encoded result to complete the determination of the priority level of the location data;

[0028] (iii) If it is field surveillance radar message data, check whether it is within the high-precision range of the field surveillance radar. If it is within the warning range, use the field surveillance radar message data; if it is not within the high-precision range of the field surveillance radar and the ADS-B transponder is not turned off, determine it based on the multi-lateration positioning and ADS-B data priority grid; if the transponder is turned off, use the field surveillance radar data;

[0029] (iv) If it is multilateration message data, determine whether it is within the high-precision range of the field surveillance radar. If it is within the high-precision range and the field surveillance radar data timestamp is less than 1 second from the current timestamp, use the field surveillance radar data; otherwise, use multilateration and ADS-B data priority grid for determination;

[0030] (v) If the aircraft transponder is turned off, perform field surveillance radar, multilateration and ADS-B data multi-mode processing.

[0031] In step (v), if the aircraft transponder is turned off, the surveillance radar, multilateration and ADS-B data are processed in multiple ways, including:

[0032] (v-1) The aircraft has entered the stand and the stand center point is used as the real-time position data;

[0033] (v-2) If the aircraft has not entered the parking position and the field surveillance radar data is normal, the field surveillance radar data shall be used;

[0034] (v-3) If the aircraft has not entered the parking position and there is no surface surveillance radar data, the surface surveillance radar, multilateration and the latest timestamp data in the ADS-B data are used. If the ADS-B data timestamp is less than 1 second, multilateration and ADS-B data priority grid are used for judgment.

[0035] In step S3, based on the parsed different source data, the data is packaged into the same data entity object, including:

[0036] (a) Vehicle location data cache generation, receiving vehicle location data messages, and updating the latest reported location data of each vehicle into the cache;

[0037] (b) If the difference between the timestamp of the last updated position of the current vehicle in the cache and the current timestamp is less than 15 seconds, the speed at the last updated position in the cache is used for inference, and the corresponding distance is offset according to the direction of the vehicle image in the cache. The expression is:

[0038] Distance = speed × (current timestamp - last updated timestamp of location data in cache)

[0039] After obtaining the current position of the vehicle, if the difference between the timestamp of the last updated position of the current vehicle in the cache and the current timestamp is greater than 15 seconds, the cache is cleared, indicating that the vehicle has been offline;

[0040] (c) The data entity is unified. The CHECK method is used to determine whether it is processed by the current operator according to the format parsed in step S2. If it is not processed by the current operator, it is handed over to the next operator for processing. The entire operator chain is executed and different location messages are constructed into a unified location entity.

[0041] Another object of the present invention is to provide a message-based multi-source apron data high-concurrency fusion system, which implements the message-based multi-source apron data high-concurrency fusion method, and the system includes:

[0042] Build a multi-protocol parsing processor module, detect the types of messages of different protocol families through message type detectors, and parse the message content through corresponding message parsers for different types;

[0043] The different source data parsing module is used to perform real-time, accurate and consistent parsing of different source data received by the apron using a priority grid determination method; the different source data received by the apron include: ADS-B data, multi-point positioning data, and field monitoring radar data;

[0044] The data entity object unification module is used to package different source data into the same data entity object based on the parsed data.

[0045] In combination with all the above-mentioned technical solutions, the beneficial effects possessed by the present invention are as follows: in terms of apron scene data reflecting the operation status of scene elements, traditional scene data reception has a single source, and generally only ADS-B data is used. In order to improve the real-time performance of scene data, multi-point positioning, field monitoring radar and ADS-B data are integrated. To ensure accuracy and consistency, the present invention solves the problem of a single fusion data source and the problem of inconsistent state data from different data sources for the same scene entity element due to the diverse sources of real-time status data. In a multi-source data fusion scenario, the present invention simultaneously receives multi-source and multi-protocol messages, solving the problem of unified parsing of different protocol families or different protocols under the same protocol family. The core of the present invention is to form a unified management of multi-source data, form a unified data entity structure based on the analysis results of multi-source data, and solve the problem of inconsistency of multi-source data entities.

[0046] The present invention realizes the comprehensive integration of apron data, provides real-time and accurate data to the outside world, and reduces the installation cost of related equipment. Through the unified release of apron data, the development cost and deployment cost of other location data related systems and functions are reduced. The present invention is the first to unify and integrate the data of apron location data transmission protocols such as ASTERIX family protocol and JT808 protocol, and ensure data consistency for multi-source duplicate data. The present invention reduces the possibility of missing real-time data, and fills the missing position data through the integration of non-homologous data. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure;

[0048] Figure 1 It is a flow chart of a high-concurrency fusion method of multi-source apron data based on messages provided by an embodiment of the present invention;

[0049] Figure 2 It is a schematic diagram of a high-concurrency fusion method for multi-source apron data based on messages provided by an embodiment of the present invention;

[0050] Figure 3 It is a schematic diagram of a multi-protocol analysis processor provided by an embodiment of the present invention;

[0051] Figure 4 It is a unified graph of data entities provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0053] The innovation of the present invention is that the present invention is based on the protocol layer for multi-source data, realizes fusion and data entity unification in high-concurrency scenarios, and analyzes the real-time validity of the data based on the historical data of the business scenario to build high-quality integrated data. The present invention has no similar patents and literature in the field of data integration. At the same time, the present invention designs an analysis link, performs compatible analysis on the ASTERIX protocol family and the JT808 protocol message, and combines the GeoHash algorithm and the behavior patterns of different airlines and different drivers to improve the real-time and accuracy of multi-source data. Through the unification of the data model structure, the design of a real-time receiving architecture for multi-protocol high-concurrency messages is realized.

[0054] Embodiment 1, as Figure 1 As shown, the high-concurrency fusion method of multi-source apron data based on messages provided by an embodiment of the present invention includes:

[0055] S1, build a multi-protocol parsing processor, detect the types of messages of different protocol families through message type detectors, and parse the message content through corresponding message parsers for different types;

[0056] S2, for different source data received by the apron, a priority grid determination method is used to perform real-time, accurate and consistent analysis of different source data; the different source data received by the apron include: ADS-B data, multi-point positioning data, and field monitoring radar data;

[0057] S3, based on the parsed different source data, is packaged into the same data entity object.

[0058] Embodiment 2 parses different types of messages, supports binary message parsing of ASTERIX protocol family CAT020, CAT021, CAT010, etc. and JT808 protocol family 2011, 2013, 2019, etc., unifies the standard structure of different entity objects parsed by different protocols, and ensures the consistency of scene data entities from different sources.

[0059] The overall idea of ​​the present invention is to receive message data through a high-concurrency thread model, detect the message type according to the binary message header, and encapsulate the data entity according to the parsed content. Among them, the ASTERIX report is the location message data of the aircraft on the scene, and the JT808 report is the location data of the special vehicle on the scene. Since the data is multi-source data fusion, in order to solve the problem of different source coverage of data entities, the priority is set according to the accuracy and real-time performance of the report source to ensure data consistency and prevent the timing disorder caused by different data sources.

[0060] like Figure 2 As shown in the figure, after receiving the binary message, the link context is constructed, and the message and its processing results are parsed and post-processed by chaining the multi-protocol parsing processor on the line. Each multi-protocol parsing processor has a built-in judge to identify whether the incoming data entity is processed by this multi-protocol parsing processor. After the link execution is completed, the final data entity object is obtained. In order to meet the high concurrency performance requirements, the reactor programming mode is adopted, and the asynchronous message drives the execution of the working link node to improve the message processing concurrent throughput. When the IO multiplexing program listens and receives the event notification, it is assigned to different processors for processing according to the event type. Among them, the Reactor thread group is specifically used to listen to and respond to various IO events, such as connection establishment ready (ACCEPT), read ready (READ), write ready (WRITE), etc. When a new event is detected, it is sent to the corresponding processor for processing. The processor is used to process specific events, such as reading data, executing business logic, writing responses, and other operations.

[0061] The specific implementation is as follows.

[0062] The high-concurrency fusion method of multi-source apron data based on messages provided in an embodiment of the present invention includes:

[0063] 1.Multi-protocol parsing processor, such as Figure 3 shown.

[0064] For messages of different protocol families, the message type is detected by the message type detector, and the corresponding parser is used to parse the message. The steps are as follows:

[0065] 1) According to the message type, it is determined whether it belongs to the ASTERIX protocol family or the JT808 protocol format, and the corresponding parsing chain is executed.

[0066] 2) According to the message body, parse the corresponding protocol family protocol version, execute the corresponding parser according to the protocol version, and return the parsed string message.

[0067] The basic format of the ASTERIX message is shown in Table 1;

[0068] Table 1 Basic format of ASTERIX message

[0069]

[0070] The basic message format of Beidou JT808 is shown in Table 2;

[0071] Table 2 Beidou JT808 basic message format

[0072]

[0073] For MESSAGE_ASTERIX_DETECTER, right-shift the corresponding message value, perform a logical AND operation with 0xff and the message value, and intercept the 8 bits from the beginning of the message. Convert the corresponding message value to bytes, perform a logical AND operation with 0xff and the message value, and intercept the last 8 bits from the end of the message. According to the JT808 protocol, the head and tail identifiers are 0x7e, and according to the ASTERIX family message definition, the identification version is between 000-127.

[0074] For MESSAGE_ASTERIX_DETECTER, if the first 8 bits and the last 8 bits are not 0x7e, and the decimal value is between 000-127, it is determined to be an ASTERIX message.

[0075] For MESSAGE_JT808_DETECTER, intercept the first 8 bits of the message and the last 8 bits of the message. If the first 8 bits and the last 8 bits are both 0x7e, it is determined to be a JT808 message.

[0076] 2. Data from different sources ensure real-time, accurate and consistent correction.

[0077] The aircraft transponder receives ADS-B data, multilateration data and field surveillance radar data.

[0078] The ADS-B data includes:

[0079] There are three ADS-B data response modes: A mode, C mode and S mode. A mode responds to identity information, B mode responds to identity information and air pressure altitude information. S mode broadcasts identity information and four-dimensional information such as position altitude.

[0080] If the ADS-B reply mode is not S mode, or the aircraft transponder does not have the ADS-B reply function and cannot provide position information, multi-point positioning data and field monitoring radar data are required.

[0081] In civil aviation apron management, after landing, different airlines have different management regulations and different operating habits of aircraft pilots, which leads to different timings for turning off the ADS-B transponder. There are the following situations:

[0082] The first is to turn off the aircraft transponder after landing on the runway.

[0083] The second method is to turn off the aircraft transponder while taxiing into the parking position after landing.

[0084] The third method is to turn off the aircraft transponder after the aircraft enters the gate. Based on the historical data received by the aircraft transponder, the historical data flight number, airline and transponder shutdown time, it is possible to determine the time when different airlines and different aircraft pilots turn off the ADS-B transponder. After the aircraft transponder is turned off, other data is used.

[0085] The multi-point positioning data includes:

[0086] Multi-point positioning receives aircraft transponder signals through ground receivers and determines the position of the aircraft based on the time difference of signal reception. In three-dimensional space, aircraft signals will be received by at least four ground base stations. The time difference of signal reception between the target signal and different ground receivers will exist as the movement of a hyperbola (surface). The target position can be determined by calculating the intersection of the hyperbola. As above, after the aircraft transponder is turned off, the position of the aircraft cannot be calculated. At this time, other data is needed.

[0087] The field monitoring radar data includes:

[0088] Airport surveillance radar has a certain coverage area, focusing on runways, access roads and aprons. When scanning the aprons, in some cases, the radar cannot detect the aircraft due to obstructions (such as buildings, large aircraft blocking small aircraft), resulting in position loss. In this case, other location data is needed.

[0089] In the prior art, for the multi-source aircraft position data on the apron, no invention can guarantee complete accuracy throughout the entire process, so it is necessary to cooperate with each other and obtain the most real-time and accurate data through comparison and judgment based on the on-site situation. Therefore, the present invention proposes the following solution to solve the problems of inconsistent aircraft position data, low timeliness and accuracy. Specifically, it includes:

[0090] Step 1: Perform the following preparations.

[0091] (1) Determine the ADS-B transponder turning off habits of different airlines based on historical ADS-B data, such as the three situations mentioned above;

[0092] (2) Based on the historical data of the field monitoring radar, a hot spot map of the field monitoring radar location data is drawn to clarify the high-accuracy range of the field monitoring radar detection position.

[0093] (3) Compare the multi-point positioning data with the ADS-B data at the same time. The touchdown time of the aircraft can be accurately known through the air traffic control landing message, and the aircraft entry time can be accurately known through the guidance berth system. The touchdown time and arrival time are used to check whether the multi-point positioning and ADS-B position corresponding time position data are accurate. Draw a regional grid and mark the accuracy of the ADS-B and multi-point data in different grids. When integrating the data, if there are both multi-point data and ADS-B data for aircraft in the grid, select the data source location data with high accuracy based on the accuracy of ADS-B and multi-point positioning marked on the grid. At the same time, complete the construction of the ADS-B and multi-point positioning priority grid, that is, divide the entire apron into different grids and mark the priority of ADS-B and multi-point positioning in different grids.

[0094] Step 2. Based on the above preparations, construct the following process in DATA_CONFLICT_RESOLVER:

[0095] (i) Access the message data, perform byte segmentation and bitwise operation to parse the position data according to the message protocol, and put it into the specified type of cache, such as ADS-B data, into the ADS-B position cache. Aircraft position data constructs three types of position caches, including: ADS-B data position cache, field monitoring radar data position cache and multi-point data position cache.

[0096] (ii) Construct a radar high-precision range based on the radar scanning radius and scanning frequency. If it is ADS-B message data, determine whether it is within the high-precision radius range of the field monitoring radar. If it is within the high-precision range and the field monitoring radar data timestamp is less than 1 second from the current timestamp, the field monitoring radar data is used.

[0097] If it is not within the high-precision range of the on-site monitoring radar, multi-point positioning and ADS-B data priority grid are used for determination. The specific process is to lock the grid and obtain the corresponding ADS-B and multi-point positioning tag attributes of the grid to determine whether ADS-B data or multi-point positioning location data will be used in the end. See step 1 for details on building the apron priority grid.

[0098] For large airports, the apron area is large and the number of grids is large. The GeoHash algorithm is used to search, encode the longitude and latitude, and lock the corresponding grid of the location through the encoded result. The priority level of the location data is determined. The process of GeoHash encoding and locking the area is as follows:

[0099] Generate a GeoHash code for each region. Each region has four points and a center point. Generate five GeoHash codes, and then extract the common prefix. For the GeoHash algorithm for longitude and latitude points, see 2. The GeoHash algorithm for longitude and latitude points needs to perform three steps: Convert longitude and latitude to binary. For example, point (39.923232,116.390325). The range of latitude is (-90, 90), and its middle value is 0. For latitude 39.923232, it is in the interval (0, 90), so a 1 is obtained; the middle value of the interval (0, 90) is 45 degrees, and latitude 39.923201 is less than 45, so a 0 is obtained. Calculate in sequence to get the binary representation of latitude. Similarly, get the binary representation of longitude. For example, the binary representation of the point (39.923232, 116.390325) is (11010010110001000100, 10111000110001111001).

[0100] Combine the longitude and latitude, with longitude occupying even digits and latitude occupying odd digits. Get the binary code:

[0101] 11100 11101 00100 01111 00000 01101 01011 00001.

[0102] Perform Base32 encoding and get the result wx4g0ec2, which is the GeoHash value.

[0103] 3) After performing GeoHash on the point and obtaining the coded value, perform prefix matching with the GeoHash code of the area. If a match is found, the point is within the area.

[0104] (iii) If it is field surveillance radar message data, check whether it is within the field surveillance radar high-precision range based on the detection radius. If it is within the warning range, use the field surveillance radar message data. If it is not within the field surveillance radar high-precision range and the ADS-B transponder is not turned off, determine it based on multi-point positioning and the ADS-B data priority grid. If the transponder is turned off, use the field surveillance radar data.

[0105] (iv) If it is multi-lateration positioning message data, determine whether it is in the high-precision range of the field monitoring radar. If it is in the high-precision range and the field monitoring radar data timestamp is less than 1 second from the current timestamp, use the field monitoring radar data. Otherwise, use multi-lateration positioning and ADS-B data priority grid to determine.

[0106] (v) If the aircraft transponder is turned off in this case, proceed as follows:

[0107] (v-1) The aircraft has entered the parking stand and the center point of the parking stand is used as the real-time position data;

[0108] (v-2) If the aircraft has not entered the parking position and the field surveillance radar data is normal, the field surveillance radar data shall be used.

[0109] (v-3) If the aircraft has not entered the parking position and there is no surface surveillance radar data, the latest data with the timestamp in the surface surveillance radar, multilateration and ADS-B data will be used. If the timestamp of the ADS-B data such as multilateration data is less than 1 second, the multilateration and ADS-B data priority grid will be used for determination.

[0110] 3.Apron vehicle data mainly includes Beidou JT808 protocol data.

[0111] After the apron vehicle is ignited, the Beidou device will report the location in the JT808 message format. For the vehicle-mounted Beidou device, when receiving data sent by the server for processing (such as voice broadcast), the location data will not be uploaded, resulting in the loss of the location data of the vehicle in this time window. For this reason, the data needs to be supplemented when pushing the real-time location.

[0112] The vehicle location data completion solution is as follows:

[0113] (a) Based on the vehicle model, department to which the vehicle belongs, flight support mission and other data, the corresponding database ID is used as the cache key and the data object is used as the cache value to build the vehicle basic data cache. Receive the vehicle location data message, obtain the latest reported location data of each vehicle, obtain the unique identifier through the vehicle basic data cache, use the identifier as the cache key, and put the value into the cache.

[0114] (b) If the difference between the timestamp of the last updated position of the current vehicle in the cache and the current timestamp is less than 15 seconds, the vehicle's current position is obtained by inferring the speed at the last updated position in the cache and the corresponding distance offset according to the direction of the vehicle image in the cache. Distance = speed * (current timestamp - the last updated timestamp of the position data in the cache). If the difference between the timestamp of the last updated position of the current vehicle in the cache and the current timestamp is greater than 15 seconds, the cache is cleared, indicating that the vehicle has been offline.

[0115] (c) Data entity unification, such as Figure 4 shown.

[0116] For different parsing formats, they are packaged into the same data entity object. For the ASTERIX family of protocols and different versions of JT808 location data protocols, the parsing results of different protocols need to be encapsulated into a unified location data object. Therefore, the encapsulation of different protocols is delegated to operators corresponding to different protocol versions. The CHECK method is to determine whether the protocol version that the current operator can handle is consistent with the incoming protocol version. If it is inconsistent, it cannot be processed by the current operator, and then it will be handed over to the next operator to execute the entire operator link and construct different location messages into a unified location entity. On this basis, the external system can be better empowered.

[0117] Through the above embodiments, it can be seen that the present invention provides a high-concurrency fusion method and system implementation of multi-source apron data based on ASTERIX and JT808 messages. Through this method, multi-source and multi-protocol data of civil aviation aprons can be analyzed, and the real-time, accuracy and consistency of the fused data can be guaranteed. Through the implementation of this solution, the integrity and availability of apron data are improved, and it serves as the basis for apron operation decision-making, enabling the development of the civil aviation industry.

[0118] Embodiment 3, the embodiment of the present invention provides a message-based multi-source apron data high-concurrency fusion system, including:

[0119] Construct a multi-protocol parsing processor module to detect the message type of different protocol family messages through the message type detector, and parse the message content through the corresponding message parser according to the message type;

[0120] A different source data parsing module is used to perform real-time, accurate and consistent parsing of different source data received by an aircraft transponder using multilateration and ADS-B data priority grid determination methods; the different source data received by the aircraft transponder include ADS-B data, multilateration data and field monitoring radar data;

[0121] The data entity object unification module is used to package different source data into the same data entity object based on the parsed data.

[0122] After the system was put into operation at the airport, it received 6-7 million position data per day. The system function was stable and the CPU utilization rate was about 30%, which fully met the performance requirements of the airport production system. At the same time, it provided position data to 11 systems such as the apron system, perimeter system, and emergency drill system, greatly reducing the cost of redundant system function development and deployment, reducing costs and increasing efficiency. The data accuracy, real-time performance, and stability were significantly improved, and the use of position data for alarm judgment was more accurate, further improving apron safety and operating efficiency.

[0123] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the protection scope of the present invention.

Claims

1. A high-concurrency fusion method for multi-source apron data based on messages, characterized in that: After receiving the binary message, the method constructs a link context, and parses and post-processes the message and the processing result by chain-calling the protocol parsing processor on the line; each multi-protocol parsing processor has a built-in judger to distinguish whether the incoming data entity is processed by the protocol parsing processor; After the link execution processing is completed, the final data entity object is obtained, and the binary message includes an ASTERIX message and a JT808 message. The ASTERIX message is the surface aircraft position message data, and the JT808 message is the surface vehicle position data. The method specifically includes the following steps: S1, build a multi-protocol parsing processor, detect the types of messages of different protocol families through message type detectors, and parse the message content through corresponding message parsers for different types; S2, for different source data received by the apron, a priority grid determination method is used to perform real-time, accurate and consistent analysis of different source data; the different source data received by the apron include: ADS-B data, multi-point positioning data, and field monitoring radar data; S3, based on the parsed different source data, is packaged into the same data entity object; In step S2, for ADS-B data, the response mode includes A mode, C mode and S mode; A mode includes response identity information, B mode includes response identity information and pressure altitude information; S mode includes broadcast identity information and four-dimensional information of initial position altitude; if the ADS-B response mode is not S mode, or the aircraft transponder does not have ADS-B response function, multi-point positioning data and field monitoring radar data are used; The multi-lateration positioning data includes: receiving aircraft transponder signals through ground receivers and determining the aircraft position based on the signal reception time difference; The field monitoring radar data includes: runway, contact road and apron data; The data from different sources received by the aircraft transponder are processed as follows: Step 1: Prepare different source data; Step 2: In DATA_CONFLICT_RESOLVER, multi-point positioning and ADS-B data priority grid determination methods are used to perform real-time, accurate, and consistent analysis of different source data; In step 2, multi-point positioning and ADS-B data priority grid determination methods are used to perform real-time, accurate, and consistent analysis of data from different sources, including: (i) Accessing message data, parsing position data, and placing them into a specified type of cache; constructing three types of position caches for aircraft position data, including: ADS-B data position cache, field surveillance radar data position cache, and multi-point data position cache; (ii) If it is ADS-B message data, determine whether it is within the high-precision range of the field monitoring radar. If it is within the high-precision range and the time stamp of the field monitoring radar data is less than 1 second from the current time stamp, the field monitoring radar data is used; if it is not within the high-precision range of the field monitoring radar, multi-point positioning and ADS-B data priority grid are used for determination; for large airports, the GeoHash algorithm is used for search, the longitude and latitude are encoded, and the location corresponding grid is locked through the encoded result to complete the determination of the priority level of the location data; (iii) If it is field surveillance radar message data, check whether it is within the field surveillance radar high-precision range. If it is within the warning range, use the field surveillance radar message data; if it is not within the field surveillance radar high-precision range and the ADS-B transponder is not turned off, determine based on multilateration and ADS-B data priority grid; if the transponder is turned off, use the field surveillance radar data; (iv) If it is multilateration data, determine whether it is within the high-precision range of the field surveillance radar. If it is within the high-precision range and the time stamp of the field surveillance radar data is less than 1 second from the current time stamp, use the field surveillance radar data; otherwise, use multilateration and ADS-B data priority grid for determination; (v) If the aircraft transponder is turned off, conduct surveillance radar, multilateration and ADS-B data multi-mode processing; In step (v), if the aircraft transponder is turned off, the surveillance radar, multilateration and ADS-B data are processed in multiple ways, including: (v-1) The aircraft has entered the stand and the stand center point is used as the real-time position data; (v-2) If the aircraft has not entered the parking position and the field surveillance radar data is normal, the field surveillance radar data shall be used; (v-3) If the aircraft has not entered the parking position and there is no surface surveillance radar data, the surface surveillance radar, multilateration and the latest timestamp data in the ADS-B data are used. If the ADS-B data timestamp is less than 1 second, multilateration and ADS-B data priority grid are used for determination.

2. The high-concurrency fusion method for multi-source apron data based on messages according to claim 1 is characterized in that: Constructing a link context includes: using the reactor programming model and asynchronous message-driven execution of working link nodes.

3. The high-concurrency fusion method for multi-source apron data based on messages according to claim 1 is characterized in that: In step S1, the types of messages of different protocol families are detected by the message type detector. For MESSAGE_ASTERIX_DETECTER, the 8 bits from the beginning of the message and the last 8 bits from the end of the message are intercepted. If the first 8 bits and the last 8 bits are not 0x7e and the decimal value is between 000-127, it is determined to be an ASTERIX message. For MESSAGE_JT808_DETECTER, intercept 8 bits from the beginning of the message and intercept the last 8 bits from the end of the message; if the first 8 bits and the last 8 bits are both 0x7e, it is determined to be a JT808 message.

4. The high-concurrency fusion method for multi-source apron data based on messages according to claim 1 is characterized in that: In step 1, prepare different source data, including: (1) Determine the ADS-B transponder turning off habits of different airlines based on historical ADS-B data; (2) Draw a hotspot map of the radar location data based on the radar’s historical data to determine the radar’s detection range; (3) Compare the multi-point positioning data with the ADS-B data, determine the difference between the multi-point positioning accuracy and the ADS-B data accuracy based on the region, and draw a regional grid.

5. The method for high-concurrency fusion of multi-source apron data based on messages according to claim 1 is characterized in that: In step S3, based on the parsed different source data, the data is packaged into the same data entity object, including: (a) Vehicle location data cache generation, receiving vehicle location data messages, and updating the latest reported location data of each vehicle into the cache; (b) If the difference between the timestamp of the last updated position of the current vehicle in the cache and the current timestamp is less than 15 seconds, the vehicle is inferred based on the speed when the last updated position in the cache is inferred, and the vehicle image in the cache is offset by the corresponding distance in the direction. The expression is: Distance = speed × (current timestamp - last updated timestamp of location data in cache) After obtaining the current position of the vehicle, if the difference between the timestamp of the last updated position of the current vehicle in the cache and the current timestamp is greater than 15 seconds, the cache is cleared, indicating that the vehicle has been offline; (c) The data entity is unified. The CHECK method is used to determine whether it is processed by the current operator according to the format parsed in step S2. If it is not processed by the current operator, it is handed over to the next operator for processing. The entire operator link is executed and different location messages are constructed into a unified location entity.

6. A high-concurrency fusion system for multi-source apron data based on messages, characterized in that: The system implements the high-concurrency fusion method for multi-source apron data based on messages as described in any one of claims 1 to 5, and the system includes: Construct a multi-protocol parsing processor module, detect the types of messages of different protocol families through message type detectors, and parse the message content through corresponding message parsers for different types; The different source data parsing module is used to perform real-time, accurate and consistent parsing of different source data received by the apron using a priority grid determination method; the different source data received by the apron include: ADS-B data, multi-point positioning data, and field monitoring radar data; The data entity object unification module is used to package different source data into the same data entity object based on the parsed data.

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