CN112347113A

CN112347113A - Aviation data fusion method, aviation data fusion device and storage medium

Info

Publication number: CN112347113A
Application number: CN202010976642.0A
Authority: CN
Inventors: 宋德山; 范祝满
Original assignee: Beijing Zhongbing Digital Technology Group Co ltd
Current assignee: Beijing Zhongbing Digital Technology Group Co ltd
Priority date: 2020-09-16
Filing date: 2020-09-16
Publication date: 2021-02-09
Anticipated expiration: 2040-09-16
Also published as: CN112347113B

Abstract

An aviation data fusion method, an aviation data fusion device and a storage medium are provided. The aviation data fusion method comprises the following steps: loading a data fusion rule which can be dynamically updated; receiving a data stream to be fused, wherein the data stream to be fused comprises a plurality of pieces of data; determining at least one data fusion task related to the plurality of pieces of data based on the plurality of pieces of data and the data fusion rule, and selecting at least one aviation data fusion class used for the at least one data fusion task from a plurality of aviation data fusion classes included in the data fusion class library; and fusing the plurality of pieces of data by using at least one aviation data fusion class. The aviation data fusion method can be used for continuously fusing aviation data in real time.

Description

Aviation data fusion method, aviation data fusion device and storage medium

Technical Field

The embodiment of the disclosure relates to an aviation data fusion method, an aviation data fusion device and a storage medium.

Background

Aviation data fusion generally refers to fusion (e.g., automatic analysis, synthesis) under certain rules according to time sequence according to existing aviation operation data to construct a required data model, so that the data model constructed through data fusion is used for decision-making and evaluation tasks.

Disclosure of Invention

At least one embodiment of the present disclosure provides an aviation data fusion method, including: loading a data fusion rule which can be dynamically updated; receiving a data stream to be fused, wherein the data stream to be fused comprises a plurality of pieces of data; determining at least one data fusion task related to the plurality of pieces of data based on the plurality of pieces of data and the data fusion rule, and selecting at least one aviation data fusion class used for the at least one data fusion task from a plurality of aviation data fusion classes included in a data fusion class library; and fusing the plurality of pieces of data by using the at least one aviation data fusion class.

For example, in at least one example of the aviation data fusion method, the aviation data fusion method further comprises: updating the data fusion class library to add, delete or change one or more aviation data fusion classes in the data fusion class library.

For example, in at least one example of the aviation data fusion method, the aviation data fusion method further comprises: and receiving an applicable data fusion class from the updated data fusion class library so as to fuse at least one piece of data received after the update of the data fusion class library is effective.

For example, in at least one example of the aviation data fusion method, the updating the data fusion class library includes: receiving an aviation data fusion type editing request, and updating the data fusion type library according to the aviation data fusion type editing request.

For example, in at least one example of the aviation data fusion method, the loading data fusion rules includes: loading the data fusion rule from a data fusion rule base; and the data fusion rule base is configured to associate the plurality of pieces of data with corresponding aviation data fusion classes respectively through the data fusion rules.

For example, in at least one example of the airborne data fusion method, the data fusion rule includes at least one data fusion task to which a predetermined type of data relates and a path of at least one data fusion class for the at least one data fusion task in the data fusion class library.

For example, in at least one example of the aviation data fusion method, the aviation data fusion method further comprises: updating the data fusion rule base to increase at least one of a fusion rule for the newly added data type and an adjustment of the fusion rule for the predetermined type of data.

For example, in at least one example of the aviation data fusion method, the aviation data fusion method further comprises: and loading the updated data fusion rule from the updated data fusion rule base so as to fuse at least one piece of data received after the update of the data fusion rule base takes effect in the plurality of pieces of data based on the updated data fusion rule.

For example, in at least one example of the aviation data fusion method, the updating the data fusion rule base includes: receiving an aviation data fusion rule editing request; and updating the data fusion rule base according to the aviation fusion rule editing request.

For example, in at least one example of the aviation data fusion method, the aviation data fusion method further comprises: and providing an aviation data fusion rule editing interface. The receiving of the aviation data fusion rule editing request comprises: and receiving the aviation data fusion rule editing request generated according to the data fusion rule editing operation from the aviation data fusion rule editing interface.

For example, in at least one example of the aviation data fusion method, the plurality of pieces of data includes a first type of data and a second type of data; the at least one data fusion task related to the first type of data comprises a first fusion task; the at least one aviation data fusion class for the first fusion task comprises a first fusion class; the input data of the first fusion class comprises the first class data and the second class data; and the fusing the plurality of pieces of data by using the at least one aviation data fusion class, including: and fusing the first class data and the second class data by using the first fusion class.

For example, in at least one example of the aviation data fusion method, the plurality of pieces of data further includes a third type of data; the at least one data fusion task related to the first type of data further comprises a second fusion task; the at least one aviation data fusion class for the second fusion task comprises a second fusion class; the input data of the second fusion class comprises the first class data and the third class data; and fusing the plurality of pieces of data by using the at least one aviation data fusion class, further comprising: and fusing the first class data and the third class data by using the second fusion class.

For example, in at least one example of the aviation data fusion method, the receiving a data stream to be fused includes: receiving the first class of data prior to receiving the second class of data and the third class of data; the determining, based on the pieces of data and the data fusion rule, at least one data fusion task to which the pieces of data relate, and selecting at least one aviation data fusion class for the at least one data fusion task from a plurality of aviation data fusion classes included in a data fusion class library, includes: determining the first fusion task and the second fusion task related to the first type of data based on the first type of data and the data fusion rule, and loading the first fusion class and the second fusion class from the data fusion class library; the fusing the first class data and the second class data by using the first fusion class comprises: fusing the first class of data and the second class of data upon receiving the fused second class of data to obtain at least part of a first data model; and said fusing said first class of data and said third class of data using said second fusion class comprises: fusing the first class of data and the third class of data upon receiving fusing the third class of data to obtain at least a portion of a second data model.

For example, in at least one example of the aviation data fusion method, at least part of the plurality of pieces of data included in the data stream to be fused is processed data, and the plurality of pieces of data included in the data stream to be fused have a uniform unit.

At least one embodiment of the present disclosure also provides an aviation data fusion device, which includes: a processor and a memory. The memory has stored therein computer program instructions adapted to be executed by the processor, which when executed by the processor, cause the processor to perform any of the airborne data fusion methods provided by at least one embodiment of the present disclosure.

At least one embodiment of the present disclosure also provides a storage medium comprising computer program instructions stored on the storage medium. The computer program instructions, when executed by a processor, perform any of the airborne data fusion methods provided by at least one embodiment of the present disclosure.

Drawings

To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments will be briefly introduced below, and it is apparent that the drawings in the following description relate only to some embodiments of the present disclosure and are not limiting to the present disclosure.

FIG. 1 is an exemplary block diagram of an aviation data fusion method provided by at least one embodiment of the present disclosure;

FIG. 2 is a schematic illustration of a portion of an aviation data fusion rules editing interface provided in at least one embodiment of the present disclosure;

FIG. 3 is a flow chart of a first example of an aviation data fusion method provided by at least one embodiment of the present disclosure;

FIG. 4 is a flow diagram of a second example of an aviation data fusion method provided by at least one embodiment of the present disclosure;

FIG. 5 is a flow chart of a third example of an aviation data fusion method provided by at least one embodiment of the present disclosure;

FIG. 6 is a schematic block diagram of an airborne data fusion apparatus provided in at least one embodiment of the present disclosure;

FIG. 7 is a schematic block diagram of a storage medium provided by at least one embodiment of the present disclosure;

FIG. 8 illustrates an exemplary scene graph of an aerial data fusion device provided by at least one embodiment of the present disclosure; and

fig. 9 illustrates an architecture of a computing device provided by at least one embodiment of the present disclosure.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings of the embodiments of the present disclosure. It is to be understood that the described embodiments are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the disclosure without any inventive step, are within the scope of protection of the disclosure.

Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. Likewise, the word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.

The inventor of the present disclosure has noted in research that, when the complexity of the data model constructed through data fusion is increased, a user may obtain more information from the data model constructed through data fusion, and thus the data model constructed through data fusion may be made to better serve decision and evaluation tasks of the user. Furthermore, the inventor of the present disclosure also noticed in research that different data models are suitable for presenting different information, and therefore, when the information that the user wants to obtain changes, if an applicable data model can be constructed based on the source data, the user can be helped to better obtain the required information based on the applicable data model.

However, the inventors of the present disclosure further noted in their research that some aviation data fusion programs can only be used to build simple data models (e.g., object models); in addition, because the current aviation data fusion program is hard coded (that is, cannot be dynamically updated), once the aviation data fusion program is online and cannot be timely modified, when the requirement of the user for data fusion is changed, the program needs to be rewritten based on the updated data fusion requirement, so that the development workload and the development cost are increased, the user cannot timely use the aviation data fusion program to construct a data model suitable for the currently-developed decision and evaluation task, and the use value of the aviation data fusion program and the dependence of the user on the aviation data fusion program are further reduced. The following is an exemplary description in conjunction with two examples.

In one example, a program for fusing data to be fused may be written based on the data fusion task to which the data to be fused relates, and then compiled (e.g., so that the program may run off the development environment) and packaged. In another example, different data fusion classes and main programs can be written based on the data fusion tasks involved by the data to be fused, and the data fusion classes and the main programs are packaged together to obtain packaged programs. However, the inventors of the present disclosure noted in their research that when the user's needs for data fusion change (e.g., new data is fused), the program needs to be rewritten based on the updated data fusion needs.

Furthermore, the inventors of the present disclosure further noted in their research that the ability to perform real-time persistent fusion of aviation data is of considerable importance due to the enormous volume of data for aviation data (e.g., due to the continual operation of airports, the continual flooding in of airport-originated data). However, the inventor of the present disclosure further noticed that, once a problem occurs in the above packed program during the running process (for example, a defect existing in the program itself causes that some type of data cannot be fused), the program needs to be stopped, the program needs to be modified, the modified program needs to be compiled and packed, the program can be run again, and the aviation data needs to be fused, so that the aviation data fusion program may not be able to fuse the aviation data continuously in real time.

At least one embodiment of the present disclosure provides an aviation data fusion method, an aviation data fusion device and a storage medium. The aviation data fusion method comprises the following steps: loading a data fusion rule which can be dynamically updated; receiving a data stream to be fused, wherein the data stream to be fused comprises a plurality of pieces of data; determining at least one data fusion task related to the plurality of pieces of data based on the plurality of pieces of data and the data fusion rule, and selecting at least one aviation data fusion class used for the at least one data fusion task from a plurality of aviation data fusion classes included in the data fusion class library; and fusing the plurality of pieces of data by using at least one aviation data fusion class.

For example, the aviation data (e.g., civil aviation data) fusion method may be implemented based on a server (e.g., a backend). For example, the aviation data fusion method may update (e.g., dynamically update) at least one of the data fusion rules and the data fusion classes in time based on the needs of the user to construct a data model that meets the needs of the user. For example, the aviation data fusion method can continuously fuse aviation data in real time.

The aviation data fusion method provided by the embodiments of the present disclosure is described in the following by way of several examples and embodiments, and as described below, different features of the specific examples and embodiments may be combined with each other without mutual conflict, so as to obtain new examples and embodiments, which also belong to the protection scope of the present disclosure.

Fig. 1 is an exemplary block diagram of an aviation data fusion method provided by at least one embodiment of the present disclosure. As shown in FIG. 1, the aviation data fusion method comprises the following steps S10-S40.

Step S10: and loading the data fusion rule which can be dynamically updated.

Step S20: a data stream to be fused is received, where the data stream to be fused includes a plurality of pieces of data.

Step S30: the method comprises the steps of determining at least one data fusion task related to a plurality of pieces of data based on the plurality of pieces of data and data fusion rules, and selecting at least one aviation data fusion class used for the at least one data fusion task from a plurality of aviation data fusion classes included in a data fusion class library.

Step S40: and fusing the plurality of pieces of data by using at least one aviation data fusion class.

For example, by loading a dynamically-updatable data fusion rule, determining a data fusion task based on the received data and the data fusion rule, and selecting and loading an applicable aviation data fusion class, the data fusion rule can be updated in the operation process of an aviation data fusion main program, so that when the data fusion requirement of a user on aviation data changes or at least one of the main program, the data fusion rule and the aviation data fusion class has a problem (for example, the main program, the data fusion rule or the aviation data fusion class has a defect that certain type of data cannot be fused), at least one of the data fusion rule and the data fusion class library can be dynamically updated in real time, so that a data model required by the user can be constructed, real-time continuous fusion of aviation data is realized, and the application range of the aviation data fusion method is improved, and reduce development effort, etc.

For example, when the data fusion requirement of the user on the aviation data changes or at least one of the main program, the data fusion rule and the aviation data fusion class has a problem, at least one of the data fusion rule and the at least one aviation data fusion class can be dynamically updated in real time in the process that the main program keeps running; before the update becomes effective, fusing the aviation data received before the update becomes effective by using the data fusion rule before the update and the aviation data fusion class, and fusing the aviation data received after the update becomes effective by using the data fusion rule after the update and/or the aviation data fusion class.

For example, the steps S10-S40 may be executed after the aviation data fusion main program is run. For example, step S10, step S30, and step S40 may be sequentially performed. For example, step S20 may be performed after step S10 is performed; for example, in the process of executing step S30 and step S40, step S20 is continuously executed.

For example, an aviation data fusion main program may receive multiple pieces of data sequentially in time. For example, the steps S30 and S40 may be performed for each piece of data of which the data stream includes at least part (e.g., all) of the plurality of pieces of data. For example, the steps S30 and S40 may be performed on each piece of data (i.e., each piece of data of at least part of the plurality of pieces of data is processed in real time) when the data stream is received, without waiting for all of the plurality of pieces of data to be received and then performing the steps S30 and S40 on the received data. For example, the plurality of pieces of data included in the data stream to be fused may be parsed and processed airport operational data.

For example, the data stream to be fused includes a plurality of pieces of data including: flight related data, airspace related data, airport related data, airline related data, air traffic control related data, weather related data, aircraft related data. For example, the data stream to be fused includes airport operational data.

For example, flight related data includes: data relating to flight number, data relating to a shift, data relating to a date of execution, data relating to a departure airport, data relating to a route, data relating to a flight life cycle, data relating to a traveler, data relating to baggage, data relating to a unit.

For example, data related to passengers (i.e., passenger data) includes: the total number, the number of people in each age group, the number of people in each sex, the number of people in each cabin, the number of people in each country, whether important guests (important guest types, names and jobs) exist, the number of people needing special care, the number of active soldiers, the number of people in the current area, the number of transit passengers and the like.

For example, data related to aircraft includes: data relating to aircraft type, data relating to aircraft registration number, data relating to onboard equipment condition, data relating to manufacturer, data relating to lead time, data relating to profile data, data relating to base performance.

For example, data may be represented using name-value pairs (which may also be referred to as field-value pairs, attribute-value pairs, or key-value pairs). For example, the data type of "name" in a name-value pair is a string or character, and the data type of "value" in a name-value pair may be a string, a number, a boolean value (true or false), an array, null, or a name-value pair. For example, numbers may be represented in integer, floating point (e.g., single or double precision), or fixed point numbers. For example, { "passenger headcount" may be used: "156" indicates passenger count data for a flight.

For example, the data stream to be fused includes a plurality of pieces of data having a uniform data format. For example, "the pieces of data have a uniform data format" means that the pieces of data relate to at least one (e.g., all) of a data exchange format, a numeric representation, a time representation, a unit representation, and a name representation that are completely identical. For example, at least a portion (e.g., all) of the plurality of pieces of data included in the data stream to be fused is data after parsing and/or data processing of the data provided by the data source.

For example, the data exchange (data transfer) format of the pieces of data included in the data stream may be any one of a JSON (JavaScript object notation) format, an XML (extensible markup language) format, a binary format, and a text format. For example, the data exchange formats of the pieces of data included in the data stream to be merged may each be a JSON format.

For example, the digital representation of the plurality of pieces of data included in the data stream may be any one of an integer type, a single-precision floating point type, a double-precision floating point type, and a fixed point type.

For example, the time representation of the plurality of data included in the data stream may be XX-YY-ZZ (XX, YY, ZZ represent year, month, day, respectively), XX-YY month ZZ day, YY-ZZ-XX.

For example, the data streams to be merged include a plurality of pieces of data that are related to the same name expression. For example, for passenger headcount, the plurality of pieces of data are all represented by "headcount".

For example, the data streams to be merged include a plurality of pieces of data that are related to a uniform unit representation. For example, for altitude, the pieces of data are all represented in "feet".

For example, in step S10, a dynamically updatable data fusion rule is loaded, including: the data fusion rules (dynamically updatable data fusion rules) are loaded from a data fusion rule base. For example, the dynamically-updatable data fusion rule refers to a data fusion rule that can be updated during the operation of an aviation data fusion main program.

For example, after the aviation data fusion main program runs, the data fusion rule may be loaded to the memory from the data fusion rule base. For example, since the data fusion rule is not packaged with the main program, that is, the data fusion rule is not located in the package in which the main program is located, the data fusion rule can be updated during the operation of the main program.

For example, the data fusion rule base may be a relational database. For example, the data fusion rule base is configured to associate the pieces of data with corresponding aviation data fusion classes, respectively, via the data fusion rules.

For example, the fusion rule includes at least one data fusion task to which a predetermined type of data relates and a path of at least one data fusion class for the at least one data fusion task in the data fusion class library.

For example, the plurality of pieces of data includes a first type of data and a second type of data. For example, the pieces of data include data of type a (e.g., first type data) and data of type B (e.g., second type data). For example, assume that type a data is { "flight number": "XXXX", data of type B is { "airline": "YYYY".

For example, the at least one data fusion task to which the first type of data relates includes a first fusion task; that is, the fusion rule includes a first fusion task to which a first type of data (e.g., data of type a) relates. For example, a first fusion task is used to fuse a first class of data (e.g., data of type a) with a second class of data (e.g., data of type B) to build at least part of a first object model. For example, a first object model (e.g., flight object model) may include { "flight number": "XXXX", "airline": "YYYY".

For example, the plurality of pieces of data included in the data stream may include a plurality of pieces of data of type a (e.g., a first type of data); for example, the pieces of a-type data (e.g., first-type data) may include { "flight number": "CZ 6171" }, { "flight number": "MU 2533" }, { "flight number": "HO 1074" } and the like. For example, the plurality of pieces of data included in the data stream may include a plurality of pieces of data of type B (e.g., second-type data).

For example, the specific data contained by the first object model may change continuously as the received data increases. For example, at a first time, the specific data contained by the first object model may include { "flight number": [ "CZ 6171", "MU 2533" ], "airline": [ "southern aviation", "eastern aviation" ] }; at a second time after the first time, the specific data included in the first object model may include { "flight number": [ "CZ 6171", "MU 2533", "HO 1074" ], "airline": [ "southern aviation", "eastern aviation", "lucky aviation" ] }.

For example, the fusion rule further includes a first fusion class for the first fusion task and a path of the first fusion class in the database fusion class library. For example, the input data of the first fused class includes a first class of data and a second class of data. For example, the input data of the first fused class may also include other types of data in addition to the first class of data and the second class of data.

For example, fusing pieces of data using at least one aviation data fusion class includes: a first type of data (e.g., data of type A) is fused with a second type of data (e.g., data of type B) using a first fusion class.

For example, receiving a data stream to be fused includes: the first type of data is received before the second type of data is received. For example, determining at least one data fusion task to which the plurality of pieces of data relate based on the plurality of pieces of data and the data fusion rule, and selecting at least one aviation data fusion class for the at least one data fusion task from a plurality of aviation data fusion classes included in the data fusion class library includes: and determining a first fusion task related to the first type of data based on the first type of data and the data fusion rule, and loading a first fusion class for the first fusion task from the data fusion class library. For example, fusing the first class of data and the second class of data using the first fusion class includes: fusing the first type of data and the second type of data upon receiving the fused second type of data to obtain at least part of the first object model;

for example, the pieces of data further include a third type of data (e.g., data of type C); for example, data of type C is { "snowfall": { "start time": "xx 1: yy 1", "end time": "xx 2: yy 2" } }.

For example, the at least one data fusion task to which the first type of data relates further comprises a second fusion task; that is, the fusion rule includes a second fusion task to which the first type of data (e.g., type a data) relates. For example, the second fusion task is used to fuse a first class of data (e.g., data of type a) with a third class of data (e.g., data of type C) to build at least part of the second object model. For example, the second object model (e.g., meteorological object model) may include { "flight number": "XXXX", "snowfall": { "start time": "xx 1: yy 1", "end time": "xx 2: yy 2" } }.

For example, the plurality of pieces of data included in the data stream may include a plurality of pieces of data of type C (e.g., data of a third type), which will not be described in detail. For example, the specific data contained by the second object model may change continuously as the received data increases.

For example, the fusion rule further includes a second fusion class for the second fusion task and a path of the second fusion class in the database fusion class library. For example, the input data of the second fused class includes the first class data and the third class data. For example, the input data of the second fused class may also include other types of data in addition to the first class of data and the third class of data.

For example, fusing pieces of data using at least one aviation data fusion class, further comprising: the first class of data (e.g., data of type a) and the third class of data (e.g., data of type C) are fused using the second fusion class.

For example, receiving a data stream to be fused includes: the first type of data is received before the third type of data is received. For example, determining at least one data fusion task to which the plurality of pieces of data relate based on the plurality of pieces of data and the data fusion rule, and selecting at least one aviation data fusion class for the at least one data fusion task from a plurality of aviation data fusion classes included in the data fusion class library includes: and determining a second fusion task related to the first type of data based on the first type of data and the data fusion rule, and loading a second fusion class for the second fusion task from the data fusion class library. For example, fusing the first class of data and the third class of data using the second fusion class includes: and fusing the first type of data and the third type of data upon receiving the fused third type of data to obtain at least part of the second object model.

For example, the at least one data fusion task to which the fusion rule includes the predetermined type of data may be a multi-level fusion task. For example, the at least one data fusion task to which the predetermined type of data relates may include a first level fusion task for fusing the type a data with the type B data (e.g., second type data) to construct the first object model, and a second level fusion task for fusing the first object model with the type C data (e.g., third type data) to obtain the third object model.

For example, the source data corresponding to the plurality of pieces of data included in the data stream to be fused originates from at least two data sources (e.g., an airline data platform and an aircraft data platform). For example, at least part of the plurality of pieces of data included in the data stream to be fused is processed data and the plurality of pieces of data included in the data stream to be fused have a uniform unit. For example, the pieces of data are data that have been at least parsed; for example, at least a part (e.g., all) of the plurality of pieces of analyzed data is data processed.

For example, in step S20, receiving the data stream to be fused includes: the plurality of pieces of data are received from at least one of a program for data processing and a program for data parsing. For example, the program for data processing is configured to data-process at least part of the source data so that at least one (all) of a numerical representation, a time representation, a unit representation, and a name representation to which pieces of data (pieces of data received by the aviation data fusion program) relate is unified. It should be noted that the program for data processing and the program for data analysis may be merged with the main program and manage the main program for the aviation data.

For example, during the operation of the main program, the data stream is continuously received. For example, step S20 may be performed simultaneously in the course of performing any one of step S30 and step S40. In some examples, step S20 may also be performed during the performance of step S10, in which case the data received before performance of step S10 may be temporarily stored in a cache.

For example, a plurality of aviation data fusion classes included in the data fusion class library can be set according to a data model expected to be constructed by a user and data to be fused.

For example, at least one data fusion class is used for data fusion of a data stream including a plurality of pieces of data to construct a data model (e.g., an object model) having a predetermined structure. For example, the data model of the predetermined structure may be in JSON format. For example, the specific structure of the data model constructed based on the at least one data fusion class may be set according to the actual application requirements, and at least one embodiment of the present disclosure is not particularly limited in this respect. The following description takes a data model as an object model as an example, but at least one embodiment of the present disclosure is not limited thereto.

For example, the at least one data fusion class includes any one or any combination of a data fusion class for constructing a flight object model, a data fusion class for constructing a meteorological object model, a data fusion class for constructing an airline object model, a data fusion class for constructing an air management object model, a data fusion class for constructing an airport object model, a data fusion class for constructing an airspace object model, a data fusion class for constructing an aircraft object model, and a data fusion class for constructing a general information model. For example, a data fusion class used to build an object model may include multiple data fusion subclasses.

For example, the input data for building a data fusion class for a flight object model includes: flight number data (relationship: airline), aircraft type data (relationship: aircraft), aircraft registration number data (relationship: aircraft), airborne equipment condition data (relationship: aircraft), passenger data (relationship: airline, airport), cargo data (relationship: airline, airport), baggage data (relationship: airline, airport), crew data (relationship: airline), mission property data (relationship: air traffic control, airline), air change plan condition data (relationship: airline, air traffic control, airspace, weather, airport, aircraft), operational quality data (relationship: airline, air traffic control, airport, weather), takeoff airport data (relationship: airport, air traffic control, weather), landing airport data (relationship: airport, air traffic control, weather), air abnormal process data (relationship: airline, Airport, air tube, weather, aircraft, airspace), etc.

For example, the input data for constructing the data fusion class of the meteorological object model includes: thunderstorm data (relationship: flight, airline, air traffic control, airport, airspace), typhoon data (relationship: flight, airline, air traffic control, airport, airspace), frost data (relationship: flight, airline, air traffic control, airport, aircraft), snowfall data (relationship: flight, airline, air traffic control, airport, aircraft), sleet data (relationship: flight, airline, air traffic control, airport, aircraft), low visibility data (relationship: flight, airline, air traffic control, airport, aircraft), low cloud data (relationship: flight, airline, air traffic control, airport, aircraft), air bump and wind shear data (relationship: flight, airline, air traffic control, airport, aircraft), and the like.

For example, the aviation data fusion method further includes the following step S501.

Step S501: and updating the data fusion class library to add, delete or change one or more aviation data fusion classes in the data fusion class library.

For example, in step S501, updating the data fusion class library includes: receiving an aviation data fusion editing request, and updating a data fusion library according to the aviation data fusion editing request.

For example, a developer may execute an aviation fusion type editing operation on a terminal (e.g., a computer), and the server may receive an aviation fusion type editing request generated according to the aviation fusion type editing operation from the terminal and update the data fusion type library based on the aviation fusion type editing request.

For example, one or more newly added aviation data fusion classes can be stored in the data fusion class library by storing the newly added aviation data fusion classes in the path where the data fusion class library is located; one or more aviation data fusion classes can be changed by storing the changed aviation data fusion class in the path of the data fusion class library and replacing the aviation data fusion class with the same file name as the changed aviation data fusion class in the data fusion class library.

For example, a developer may develop one or more aviation data fusion classes on a terminal and upload the one or more aviation data fusion classes to a server, and the server may update the data fusion class library by storing the one or more aviation data fusion classes in a path where the data fusion class library is located.

In one example, the aviation data fusion method further comprises providing an aviation data fusion rule editing interface; the receiving of the aviation fusion editing request comprises the following steps: an aviation convergence class edit request generated from an aviation convergence class edit operation is received from an aviation convergence class edit interface (e.g., a graphical user interface). In another example, receiving the aviation fusion class edit request includes: and receiving an aviation fusion type editing request generated by executing (directly executing on a storage path of the data fusion type library) operations of pasting and/or deleting files.

For example, the aviation data fusion method further includes the following step S502.

Step S502: the aviation data fusion method further comprises the following steps: an applicable data fusion class is received (e.g., loaded) from the updated data fusion class library to fuse at least one of the pieces of data received after the data fusion class library update is in effect.

For example, in step S502, the applicable data fusion class refers to a data fusion class applicable to fuse at least one piece of data received after the validation of the data fusion class library update among the plurality of pieces of data.

For example, step S501 and step S502 may be performed in the order of step S501 and step S502. For example, steps S501 and S502 may be performed during any of steps S20-S40.

For example, the aviation data fusion method further includes the following step S601.

Step S601: and updating the data fusion rule base to add at least one of analysis rules, processing rules and fusion rules for the newly added data type and/or adjust at least one of analysis rules, processing rules and fusion rules for the data of the preset type.

For example, updating the data fusion rule base may include: a fusion rule for data of the D type (fourth type data) is added. For example, updating the data fusion rule base may include: adjusting at least one type of fusion rule for data of type A.

For example, in step S601, updating the data fusion rule base includes: receiving an aviation data fusion rule editing request; and updating the data fusion rule base according to the aviation fusion rule editing request.

For example, a developer, a technical support person, or a user may perform an aviation fusion rule editing operation on a terminal (e.g., a computer), and a server may receive an aviation fusion rule editing request generated according to the aviation fusion rule editing operation from the terminal and update a data fusion rule base based on the aviation fusion rule editing request.

For example, the aviation data fusion method further comprises providing an aviation data fusion rule editing interface; the receiving of the aviation data fusion rule editing request comprises the following steps: and receiving an aviation data fusion rule editing request generated according to the data fusion rule editing operation from an aviation data fusion rule editing interface. For example, by providing the aviation data fusion rule editing interface, technical support personnel or users can be enabled to execute the aviation fusion rule editing operation via the aviation data fusion rule editing interface, thereby further reducing the development workload, and solving at least one problem (if any) of the main program, the data fusion rule and the aviation data fusion class library more quickly and/or shortening the time required for responding to the change of the customer requirement.

Fig. 2 is a schematic diagram of a portion of an aviation data fusion rule editing interface provided in at least one embodiment of the present disclosure. For example, as shown in fig. 2, a sub-rule for constructing a flight object may be added via the aviation data fusion rule editing interface. For example, a sub-rule for building a flight object may associate input data for building the flight object with an airline data fusion class for building the flight object.

For example, the aviation data fusion method further includes the following step S602.

Step S602: and loading the updated data fusion rule from the updated data fusion rule base so as to fuse at least one piece of data received after the update of the data fusion rule base takes effect in the plurality of pieces of data by using the updated data fusion rule.

For example, step S601 and step S602 may be performed in the order of step S601 and step S602. For example, step S601 and step S602 may be performed in the process of performing any of step S20-step S40.

For example, the aviation data fusion method further includes the following step S001. For example, step S001 may be performed before performing steps S10-S40.

Step S001: and operating the aviation data fusion main program.

For example, the aviation data fusion method further includes at least one (e.g., all) of the following steps S701-S703. For example, steps S701 to S703 may be performed before step S001 is performed.

Step S701: and receiving a plurality of aviation data fusion classes, and storing the plurality of aviation data fusion classes in a data fusion class library.

For example, in step S701, the developer may write a plurality of aviation data fusion classes, which are provided to and stored in the data fusion class library, e.g., which may be provided to the data fusion class library via a server. For example, the data fusion class library may be a portion of the database corresponding to the specified path.

Step S702: and receiving the data fusion rule capable of being dynamically updated, and storing the data fusion rule capable of being dynamically updated in a data fusion rule base.

For example, in step S702, a developer, a technical support staff, or a user may configure a data fusion rule on an aviation data fusion rule editing interface of a terminal (e.g., a computer), and a server may receive the configured data fusion rule from the aviation data fusion rule editing interface and store the configured data fusion rule in a data fusion rule base.

Step S703: and receiving an aviation data fusion main program. For example, a developer can develop an aviation data fusion main program at a terminal (e.g., a computer), and compile and package the aviation data fusion main program after the aviation data fusion main program is developed; then, the server can receive the aviation data fusion main program compiled and packaged from the terminal.

For example, because the plurality of aviation data fusion classes and the data fusion rules are not packaged together with the main program, that is, the plurality of aviation data fusion classes and the data fusion rules are not located in the program package in which the main program is located; in this case, during the initial stage of the main program running, it is not necessary to load a plurality of aviation data fusion classes in the memory, but the aviation data fusion class matching with the received data may be determined and loaded based on the received data.

Fig. 3 is a flowchart of a first example of an aviation data fusion method provided by at least one embodiment of the present disclosure. A first example of an aviation data fusion method provided by at least one embodiment of the present disclosure is illustrated below with reference to fig. 3. It should be noted that, for convenience of description, fig. 3 also illustrates data parsing and data processing performed on the source data. For example, the plurality of pieces of data included in the data stream may include processed data 1, processed data 2, … processed data N shown in fig. 3; for example, the plurality of pieces of data included in the data stream may also include data that has been parsed but not processed.

For example, as shown in fig. 3, the aviation data fusion method includes: and calling an applicable data fusion class (namely, a data fusion class matched with the plurality of pieces of data included in the data stream) according to the aviation data fusion rule (for example, according to the aviation data fusion rule and the plurality of pieces of data included in the data stream), and performing data fusion on at least the analyzed data (for example, at least one of the analyzed and data-processed data and the analyzed and data-unprocessed data) to obtain fused data. For example, as shown in FIG. 3, the data stream includes pieces of data relating to fusion task 1, fusion task 2, … …, and fusion task M; the fusion task 1, the fusion tasks 2 and … … and the fusion task M are respectively used for constructing fusion data 1, fusion data 2 and … … and fusion data M; the data fusion classes for fusion task 1, fusion task 2, … …, and fusion task M are a first data fusion class, a second data fusion class, and a … … mth data fusion class, respectively. For example, as shown in fig. 3, the input data of the first data fusion class includes processed data 1 and processed data M, for example, the input data of the first data fusion class further includes at least one of other processed data and parsed but unprocessed data; the input data of the second data fusion class includes processed data 2 as well as other data (e.g., processed data or/and parsed but unprocessed data); … … the input data of the mth data fusion class includes processed data M as well as other data (e.g., processed data or/and parsed but unprocessed data).

For example, at least a portion (e.g., all) of fused data 1, fused data 2, … …, and fused data M may be transmitted to and stored in a database; for example, at least a portion (e.g., all) of fused data 1, fused data 2, … …, and fused data M may be transmitted to and stored in a database. For example, the fused data 1, the fused data 2, … …, and the fused data M may be referred to as a first data model (e.g., a first object model), a second data model (e.g., a second object model), … …, and an mth data model (e.g., an mth object model), respectively.

For example, at least portions of the fused data 1, the fused data 2, … …, and the fused data M may be further fused (e.g., second-time fusion, third-time fusion … …) to obtain a desired data model (e.g., object model) through data fusion. For example, at least a portion (e.g., all) of the fused data 1, the fused data 2, … …, and the fused data M may be provided to a downstream program such that the downstream program may perform further data processing on the fused data 1, the fused data 2, … …, and at least a portion (e.g., all) of the fused data M.

Fig. 4 is a flowchart of a first example of an aviation data fusion method provided by at least one embodiment of the present disclosure. A second example of an aviation data fusion method provided by at least one embodiment of the present disclosure is illustrated below in conjunction with fig. 4.

For example, as shown in fig. 4, the aviation data fusion method includes the following steps 1 and 2.

Step 1: writing an aviation data fusion class, configuring aviation data fusion rules, and uploading a compiled and packaged program (for example, an aviation data fusion main program) file to a server for execution.

For example, in step 1, an aviation data fusion class and configuration aviation data fusion rules may be written based on a target data model (e.g., a fusion model or an object model desired by a user). For example, configuring the aviation data fusion rules includes configuring the calculation rules and the data sources for building the plurality of name (field) -value pairs included in the target data model.

For example, in step 1, the aviation data fusion class and the aviation data fusion rule are not packaged together with the aviation data fusion main program; correspondingly, when the server runs the main aviation data fusion program, all aviation data fusion classes do not need to be loaded to the memory, and the required aviation data fusion classes can be called when data fusion is carried out on the data.

For example, the aviation data fusion method further includes uploading (e.g., via a server) the aviation data fusion class and the aviation data fusion rule to a database.

For example, the aviation data fusion method further includes receiving a plurality of pieces of data included in the data stream (i.e., the access data shown in fig. 4), and calling an aviation data fusion class to perform data fusion based on the aviation data fusion rule and the plurality of pieces of data included in the data stream, so as to obtain fused data. For example, the source data corresponding to the plurality of pieces of data included in the data stream is airport operation data received from kafka.

Step 2: and updating the model result to the database.

For example, in step 2, after the data fusion is successfully performed, the fused data (i.e., the constructed data model) may be saved to the database. For example, the fused data is saved in a database, which is beneficial to data tracing. For example, the fused data may be sent to a downstream program for further data processing.

For example, after the data fusion execution fails, error information may be recorded, so that a developer, a technical support person, or a user updates at least one of the aviation data fusion rules and the aviation data fusion class library based on the recorded error information. For example, in other examples of the aviation data fusion method provided in at least one embodiment of the present disclosure, error information may also be recorded, and details are not described again.

For example, the aviation data fusion method further comprises: the results of the aviation data fusion procedure are viewed from a database or the results (e.g., further processed data) are received from a downstream procedure.

Fig. 5 is a flowchart of a first example of an aviation data fusion method provided by at least one embodiment of the present disclosure. A third example of an aviation data fusion method provided by at least one embodiment of the present disclosure is illustrated below with reference to fig. 5.

For example, as shown in fig. 5, the aviation data fusion method includes the following steps 1.1 to 1.4.

Step 1.1: writing an aviation data fusion class and configuring aviation data fusion rules (e.g., data fusion rules).

For example, in step 1.1, an aviation data fusion class and configuration aviation data fusion rules may be written based on a target data model (e.g., a fusion model or an object model desired by a user). For example, configuring the aviation data fusion rule includes configuring a path of an aviation data fusion class involved in the fusion computation and a data source (e.g., a source of input data of the aviation data fusion class) for building a plurality of name (field) -value pairs of the target data model.

For example, the compiled aviation data fusion class may be provided to a database (e.g., a database-included data fusion rules library); the method comprises the steps that paths of aviation data fusion classes and aviation data fusion rules are configured in a database, and the aviation data fusion rules are loaded into a memory from the database (for example, a data fusion rule base included in the database) after a program (for example, an aviation data fusion main program) runs.

Step 1.2: and (5) packing and operating the program.

For example, in step 1.2, the written main program may be compiled and packaged, and the compiled and packaged program file may be uploaded to a server for execution.

For example, as shown in fig. 5, the aviation data fusion method further includes updating the aviation data fusion rule during the operation of the main program, and performing fusion calculation based on the updated aviation data fusion rule.

Step 1.3: accessing data and fusing the data.

For example, in step 1.3, after the program starts to work, data (e.g., various airport operation data, various flight operation data) is received in real time; and searching a fusion model (a data model constructed through fusion calculation) corresponding to the data according to the type of the data. For example, the fusion model corresponding to the data is a fusion model (a data model constructed by fusion computation) constructed by a fusion computation (e.g., an aviation data fusion class that can be triggered by the data) that can be triggered by the data.

For example, step 1.3 also includes performing calculations for each fused field and triggering subsequent fields of the relevant field to perform calculations. For example, "calculating each fused field and triggering subsequent fields of the relevant field" may refer to calculating input data related to an aviation data fusion class that can be triggered by the received data.

Step 1.4: updating and correcting the fusion model.

For example, in step 1.4, after the data fusion is successfully performed, an updated fusion model may be constructed based on the updated data fusion rules. For example, if the fusion calculation is problematic and the updated fusion model cannot be successfully constructed, the alarm information is recorded for subsequent processing.

For example, the aviation data fusion method further includes saving (e.g., updating) the fused data (e.g., JSON data) to a database (e.g., a fusion model table included in the database). For example, the fused data (e.g., JSON data) may also be sent to downstream programs for further processing.

For example, in the above process, once the program runs, various flight operation data can be received in real time, data fusion can be performed on the various flight operation data, and the fusion rule can be updated according to the application requirement, so as to obtain an updated fusion model.

At least one embodiment of the present disclosure also provides an aviation data fusion device. Fig. 6 is a schematic block diagram of an airborne data fusion apparatus provided in at least one embodiment of the present disclosure. As shown in fig. 6, the aviation data fusion apparatus includes: a processor and a memory. The memory has stored therein computer program instructions adapted to be executed by the processor, which when executed by the processor, cause the processor to perform a method of airborne data fusion as provided by at least one embodiment of the present disclosure.

For example, the aviation data fusion device can update (e.g., dynamically update) at least one of the data fusion rules and the data fusion classes in time based on the needs of the user to build a data model that meets the needs of the user. For example, the aviation data fusion device can continuously fuse aviation data in real time.

For example, the processor is, for example, a Central Processing Unit (CPU), a graphics processor GPU, a Tensor Processor (TPU), or other form of processing unit with data processing capability and/or instruction execution capability, for example, the processor may be implemented as a general purpose processor, and may also be a single chip microcomputer, a microprocessor, a digital signal processor, a dedicated image processing chip, a field programmable logic array, or the like. For example, the memory may include at least one of volatile memory and non-volatile memory, e.g., the memory may include Read Only Memory (ROM), a hard disk, flash memory, etc. Accordingly, the memory may be implemented as one or more computer program products, which may include various forms of computer-readable storage media on which one or more computer program instructions may be stored. The processor may execute the program instructions to perform any of the methods of airborne data fusion provided by at least one embodiment of the present disclosure. The memory may also store various other applications and various data, such as various data used and/or generated by the applications, etc.

At least one embodiment of the present disclosure also provides a storage medium (e.g., a non-transitory storage medium). Fig. 7 is a schematic block diagram of a storage medium provided by at least one embodiment of the present disclosure. As shown in fig. 7, the storage medium includes computer program instructions stored on the storage medium. The computer program instructions, when executed by the processor, perform a method of aviation data fusion provided by at least one embodiment of the present disclosure.

For example, the storage medium may update (e.g., dynamically update) at least one of the data fusion rules and the data fusion classes in time based on the needs of the user to build a data model that satisfies the needs of the user. For example, the storage medium may continuously fuse the aviation data in real time.

For example, a storage medium may take many forms, including a tangible storage medium, a carrier wave medium, or a physical transmission medium. The stable storage media may include: optical or magnetic disks, and other computer or similar devices, capable of implementing the system components described in the figures. Unstable storage media may include dynamic memory, such as the main memory of a computer platform, etc. Tangible transmission media may include coaxial cables, copper cables, and fiber optics, such as the wires that form a bus within a computer system. Carrier wave transmission media may convey electrical, electromagnetic, acoustic, or light wave signals, and so on. These signals may be generated by radio frequency or infrared data communication methods. Common storage media (e.g., computer-readable media) include hard disks, floppy disks, magnetic tape, any other magnetic medium; CD-ROM, DVD-ROM, any other optical medium; punch cards, any other physical storage medium containing a pattern of holes; RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge; a carrier wave transmitting data or instructions, a cable or connection transmitting a carrier wave, any other data which can be read by a computer and/or computer program instructions (e.g., program code).

Computer program instructions (e.g., program code) for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

In some examples, the functionality described by at least one embodiment of the disclosure may also be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

Fig. 8 illustrates an exemplary scene graph of an aviation data fusion apparatus provided by at least one embodiment of the present disclosure. As shown in fig. 8, the aviation data fusion apparatus 300 may include a terminal 310, a network 320, a server 330, and a database 340. For example, the aviation data fusion device illustrated in fig. 8 may be implemented to provide an aviation data fusion method according to at least one embodiment of the present disclosure.

For example, the terminal 310 may be the computer 310-1, the portable terminal 310-2 shown in fig. 8, but at least one embodiment of the present disclosure is not limited thereto. It will be appreciated that the terminal may also be any other type of electronic device capable of performing the receiving, processing and displaying of data, which may include any one or any combination of a desktop computer, a laptop computer, a tablet computer, a cell phone.

For example, the terminal 310 may display at least one of an aviation data fusion rule editing interface and an aviation fusion class editing interface. For example, server 330 or database 340 may receive an aviation data fusion rule editing request via an aviation data fusion rule editing interface.

For example, the network 320 may be a single network, or a combination of at least two different networks. For example, the network 320 may include, but is not limited to, one or a combination of local area networks, wide area networks, public networks, private networks, the internet, mobile communication networks, and the like.

For example, the server 330 may be a single server or a group of servers, and each server in the group of servers is connected via a wired network or a wireless network. The wired network may communicate by using twisted pair, coaxial cable, or optical fiber transmission, for example, and the wireless network may communicate by using 3G/4G/5G mobile communication network, bluetooth, Zigbee, or WiFi, for example. The present disclosure is not limited herein as to the type and function of the network. The one group of servers may be centralized, such as a data center, or distributed. The server may be local or remote. For example, the server 330 may be a general-purpose server or a dedicated server, may be a virtual server or a cloud server, and the like.

For example, database 340 includes a data fusion class library and a data fusion rule library. For example, database 340 may also include a portion for storing fused data. For example, database 340 may be used to store various data utilized, generated, and output from the operation of terminal 310 and server 330. Database 340 may be interconnected or in communication with server 330 or a portion of server 330 via network 320, or directly interconnected or in communication with server 330, or in a combination of both. In some embodiments, database 340 may be a stand-alone device. In other embodiments, the database 340 may also be integrated in at least one of the terminal 310 and the server 340. For example, the database 340 may be provided on the terminal 310, or may be provided on the server 340. For another example, the database 340 may be distributed, and a part of the database may be provided in the terminal 310 and another part of the database may be provided in the server 340.

For example, at least one of an aviation data fusion rules editing interface and an aviation fusion class editing interface may be displayed. For example, the server 330 may receive an aviation data fusion rule editing request via the aviation data fusion rule editing interface, and dynamically update the aviation data fusion rule in the database 340 based on the aviation data fusion rule editing request.

For example, a developer may write an aviation data fusion main program through the terminal 310, compile and package the aviation data fusion main program, and upload the aviation data fusion main program to a server through a network; the developer can write a plurality of aviation data fusion classes through the terminal 310, and transfer and store the plurality of aviation data fusion classes into a data fusion class library included in the database 340 through a network; developers, technical support personnel or users can edit the data fusion rules through the aviation data fusion rule editing interface of the terminal 310, and the edited data fusion rules are transmitted to and stored in the data fusion rule base included in the database 340 through the network.

For example, the server may run an aviation data fusion main program, and the aviation data fusion main program loads a dynamically updatable data fusion rule from a data fusion rule base included in the database 340 to the memory; the server can receive a plurality of pieces of data included in a data stream from at least one of a program for data processing and a program for data parsing, determine at least one data fusion task related to the plurality of pieces of data based on the plurality of pieces of data included in the data stream and a data fusion rule, select at least one aviation data fusion class for the at least one data fusion task from a plurality of aviation data fusion classes included in a data fusion class library, and fuse the plurality of pieces of data by using the at least one aviation data fusion class; the fused data is stored in database 340. For example, the fused data may also be sent to and further processed by downstream programs.

For example, after the server runs the main aviation data fusion program, if a problem occurs in the fusion of the aviation data, the problem is recorded, and an updating operation may be performed on at least one of the data fusion rule base and the data fusion class base during the running of the main aviation data fusion program based on the recorded problem, and after the update becomes effective, at least one piece of data received after the update becomes effective in the plurality of pieces of data is fused by using the updated at least one of the data fusion rule base and the updated data fusion class base.

For example, in a case where the main program receives data with a new type (that is, the data fusion class library does not include a fusion class for fusing data with a new type), the main program may record a problem that the data with a new type cannot be fused, the user or technical support personnel may feed the problem back to a developer, the developer may develop a fusion class for fusing the data with a new type, and provide the fusion class for fusing the data with a new type to the database (for example, to the database via a server) to update the data fusion class library; then, a user or a technical support person can perform data fusion rule editing operation through an aviation data fusion rule editing interface displayed by the terminal, and the server can receive an aviation data fusion rule editing request generated according to the data fusion rule editing operation from the aviation data fusion rule editing interface and update a data fusion rule base according to the aviation data fusion rule editing request; the updated data fusion class library and the updated data fusion rule library may then be employed to perform data fusion on the data received by the server after the update takes effect (e.g., the data with the new type described above).

The method according to embodiments of the present application may also be implemented by means of the architecture of a computing device 400 shown in fig. 9.

Fig. 9 illustrates an architecture of a computing device 400 provided by at least one embodiment of the present disclosure. As shown in fig. 9, computing device 400 may include a bus 410, one or at least two CPUs 420, a Read Only Memory (ROM)430, a Random Access Memory (RAM)440, a communication port 450 connected to a network, input/output components 460, a hard disk 470, and the like. A storage device (e.g., ROM 430 or hard disk 470) in computing device 400 may store instructions and various related data or files corresponding to the aviation data fusion method provided by at least one embodiment of the present disclosure. The computing device 400 may also include a human user interface 480. Of course, the architecture shown in FIG. 9 is merely exemplary, and one or at least two components of the computing device shown in FIG. 9 may be omitted when implementing different devices, as desired.

For example, the device or the program module of the aviation data fusion method provided in at least one embodiment of the present disclosure may be run on various operating systems (for example, operating systems including but not limited to Windows, Linux, IOS, or Android), thereby increasing the application range of the aviation data fusion method, the aviation data fusion device, and the storage medium provided in at least one embodiment of the present disclosure.

For example, the aviation data fusion method, the aviation data fusion device and the storage medium provided by at least one embodiment of the present disclosure may perform data fusion on aviation data in a programmable configurable dynamically updatable manner, for example, the calculation rule may be dynamically updated during the operation of an aviation data fusion main program. For example, the aviation data fusion method, the aviation data fusion device and the storage medium provided by at least one embodiment of the disclosure can realize online updating of data sources or calculation rules.

For example, the aviation data fusion method, the aviation data fusion device, and the storage medium provided in at least one embodiment of the present disclosure are particularly suitable for aviation data fusion application scenarios in which multiple data sources (that is, source data corresponding to multiple pieces of data originate from multiple data sources) have complex fusion rules, require dynamic updating of calculation rules, and have high requirements for real-time performance.

For example, the aviation data fusion method, the aviation data fusion device and the storage medium provided by at least one embodiment of the present disclosure may perform data fusion calculation for constructing multiple fusion models based on various types of civil aviation operating data, and dynamically load and update the fusion calculation rules during the data fusion calculation, so that the fusion models (the data models constructed through the fusion calculation) can be continuously evolved and modified in real time.

For example, the aviation data fusion method, the aviation data fusion device and the storage medium provided by at least one embodiment of the present disclosure may access various flight operation data in real time, and trigger the field of the corresponding fusion model to perform calculation for specific data, thereby implementing real-time update. For example, the aviation data fusion method, the aviation data fusion device and the storage medium provided by at least one embodiment of the disclosure can also dynamically update the model calculation rule.

For example, the aviation data fusion method, the aviation data fusion device and the storage medium provided by at least one embodiment of the present disclosure may configure the data source and the data fusion class path for performing calculation of each field in the fusion model in the database, and the calculation logic of the data source and the data fusion class may be determined by the business scenario. After the program runs, all calculation rules are loaded from the database, various types of civil aviation data after treatment (for example, after data processing) are accessed, fusion model calculation rules which can be triggered by the data are searched according to the data types, the calculation of each fusion field is carried out, subsequent fields of related fields are triggered to carry out calculation, and the result is stored in the database or sent to other programs at the downstream.

For example, the aviation data fusion method, the aviation data fusion device and the storage medium provided by at least one embodiment of the present disclosure can enable the fusion model (the data model constructed through the fusion calculation) to be continuously evolved and modified in real time by dynamically updating the data fusion rule.

At least one embodiment of the present disclosure provides an aviation data fusion method, an aviation data fusion apparatus, and a storage medium that also allow parallel computing.

Although the present disclosure has been described in detail hereinabove with respect to general illustrations and specific embodiments, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the embodiments of the disclosure. Accordingly, such modifications and improvements are intended to be within the scope of this disclosure, as claimed.

The above description is intended to be exemplary of the present disclosure, and not to limit the scope of the present disclosure, which is defined by the claims appended hereto.

Claims

1. An aviation data fusion method, comprising:

loading a data fusion rule which can be dynamically updated;

receiving a data stream to be fused, wherein the data stream to be fused comprises a plurality of pieces of data;

determining at least one data fusion task related to the plurality of pieces of data based on the plurality of pieces of data and the data fusion rule, and selecting at least one aviation data fusion class used for the at least one data fusion task from a plurality of aviation data fusion classes included in a data fusion class library;

and fusing the plurality of pieces of data by using the at least one aviation data fusion class.

2. The aerial data fusion method of claim 1, further comprising:

updating the data fusion class library to add, delete or change one or more aviation data fusion classes in the data fusion class library.

3. The aerial data fusion method of claim 2 further comprising:

and receiving an applicable data fusion class from the updated data fusion class library so as to fuse at least one piece of data received after the update of the data fusion class library is effective.

4. The airborne data fusion method of claim 2 wherein said updating said data fusion class library comprises:

receiving an aviation data fusion-like editing request, an

And updating the data fusion class library according to the aviation fusion class editing request.

5. The aerial data fusion method of claim 1 wherein the loading data fusion rules comprises:

loading the data fusion rule from a data fusion rule base; and

the data fusion rule base is configured to associate the pieces of data with corresponding aviation data fusion classes respectively via the data fusion rules.

6. The airborne data fusion method of claim 5,

the data fusion rule includes at least one data fusion task to which a predetermined type of data relates and a path of at least one data fusion class for the at least one data fusion task in the data fusion class library.

7. The aerial data fusion method of claim 6 further comprising:

updating the data fusion rule base to increase at least one of a fusion rule for the newly added data type and an adjustment of the fusion rule for the predetermined type of data.

8. The aerial data fusion method of claim 7, further comprising:

and loading the updated data fusion rule from the updated data fusion rule base so as to fuse at least one piece of data received after the update of the data fusion rule base takes effect in the plurality of pieces of data based on the updated data fusion rule.

9. The airborne data fusion method of claim 7 wherein said updating said data fusion rule base comprises:

receiving an aviation data fusion rule editing request; and

and updating the data fusion rule base according to the aviation fusion rule editing request.

10. The aerial data fusion method of claim 9, further comprising: providing an aviation data fusion rule editing interface,

wherein, the receiving the aviation data fusion rule editing request comprises:

and receiving the aviation data fusion rule editing request generated according to the data fusion rule editing operation from the aviation data fusion rule editing interface.

11. The airborne data fusion method of any of claims 1-10 wherein said plurality of pieces of data includes a first type of data and a second type of data;

the at least one data fusion task related to the first type of data comprises a first fusion task;

the at least one aviation data fusion class for the first fusion task comprises a first fusion class;

the input data of the first fusion class comprises the first class data and the second class data; and

the fusing the plurality of pieces of data by using the at least one aviation data fusion class includes: and fusing the first class data and the second class data by using the first fusion class.

12. The aerial data fusion method of claim 11 wherein the plurality of pieces of data further comprises a third type of data;

the at least one data fusion task related to the first type of data further comprises a second fusion task;

the at least one aviation data fusion class for the second fusion task comprises a second fusion class;

the input data of the second fusion class comprises the first class data and the third class data; and

the fusing the plurality of pieces of data by using the at least one aviation data fusion class further includes: and fusing the first class data and the third class data by using the second fusion class.

13. The airborne data fusion method of claim 12 wherein said receiving a data stream to be fused comprises: receiving the first class of data prior to receiving the second class of data and the third class of data;

the determining, based on the pieces of data and the data fusion rule, at least one data fusion task to which the pieces of data relate, and selecting at least one aviation data fusion class for the at least one data fusion task from a plurality of aviation data fusion classes included in a data fusion class library, includes: determining the first fusion task and the second fusion task related to the first type of data based on the first type of data and the data fusion rule, and loading the first fusion class and the second fusion class from the data fusion class library;

the fusing the first class data and the second class data by using the first fusion class comprises: fusing the first class of data and the second class of data upon receiving the fused second class of data to obtain at least part of a first data model; and

the fusing the first class data and the third class data with the second fusion class comprises: fusing the first class of data and the third class of data upon receiving fusing the third class of data to obtain at least a portion of a second data model.

14. The airborne data fusion method of any of claims 1-10 wherein at least a portion of the plurality of pieces of data included in said data stream to be fused is processed data and said plurality of pieces of data included in said data stream to be fused have uniform units.

15. An airborne data fusion apparatus comprising: a processor and a memory, wherein the memory has stored therein computer program instructions adapted to be executed by the processor, which when executed by the processor, cause the processor to perform the airborne data fusion method of any of claims 1-14.

16. A storage medium comprising computer program instructions stored on the storage medium,

wherein the computer program instructions, when executed by a processor, perform the airborne data fusion method of any of claims 1-14.