CN115065563A - Civil aviation data processing system based on block chain prediction machine - Google Patents
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Abstract
The application provides a civil aviation data processing system based on block chain prediction machine includes: a plurality of first-class servers, a plurality of second-class servers and a block chain platform; the blockchain platform comprises an intelligent contract and a prediction machine; the plurality of first servers and the plurality of second servers are in communication connection with the block chain platform; the first type of server is used for generating target events, and each target event is provided with at least two corresponding second type of servers; and the second type server is used for generating and executing the associated event corresponding to the target event corresponding to the second type server. The civil aviation data processing system can enable only the second type of server providing the public key to obtain the ciphertext data returned by the corresponding first type of server and obtain the plaintext data through the private key, and data security is guaranteed.
Description
Technical Field
The application relates to the field of data processing, in particular to a civil aviation data processing system based on a block chain prediction machine.
Background
Airports need to present daily personnel configuration, equipment configuration, etc. for the next day of emergency handling events. However, the specific data for these configurations needs to be determined by the airport based on the number of flights on the next day and the number of passengers on each flight. Each airport needs to acquire the number of flights the next day and the number of passengers on each flight to each flight driver daily. This results in each airline operator having to communicate frequent data with each airport every day. In the process, the navigation department can hardly ensure that the equipment for requesting data every time is the equipment of the airport, so that the data of the navigation department is easy to leak, and the information safety is seriously influenced.
Disclosure of Invention
In view of the above, the present application provides a civil aviation data processing system based on a block chain predictor, which at least partially solves the problems in the prior art.
According to one aspect of the application, a civil aviation data processing system based on a block chain prediction machine is provided, and comprises: a plurality of first type servers, a plurality of second type servers and a block chain platform; the block chain platform comprises an intelligent contract and a prediction machine; the plurality of first servers and the plurality of second servers are in communication connection with the block chain platform; the first type of server is used for generating target events, and each target event is provided with at least two corresponding second type of servers; the second type server is used for generating and executing the associated event corresponding to the target event corresponding to the second type server;
the blockchain platform is used for executing the following method:
s110, the intelligent contract receives the position identification A and the public key PK sent by the second type server; the location identifier a is used for indicating a location corresponding to the second type server;
s120, the intelligent contract sends A and PK to each first type server through the prediction machine;
s130, the intelligent contract receives the ciphertext target event set PK (B1), PK (B2),. multidot.,. multidot.PK (Bi),. multidot.,. multidot.PK (Bn), and Bi = (B), which are returned by each first type of server through the language prediction machine i 1 ,b i 2 ,...,b i f(i) )，b i j =(D1 i j ,D2 i j ,T1 i j ,T2 i j ,N i j ) I =1,2, ·, n, j =1,2,. ·, f (i); wherein, Bi is a target event set returned by the ith first-class server, PK (Bi) is a ciphertext obtained by encrypting Bi by using PK, and n is the number of the first-class servers; b i j F (i) the number of target event information returned by the ith first-class server; d1 i j Is b is i j Corresponding source location identification, D2 i j Is b is i j Corresponding destination location identification, T1 i j Is b is i j Corresponding event Start time, T1 i j Is b is i j Corresponding event end time, N i j Is b is i j The corresponding number of associated users; the source position identification or the target position identification of any one piece of target event information is the same as A;
s140, performing data uplink on PK (B1), PK (B2),.. cndot., PK (bi),.. cndot., PK (bn);
the second type server is used for executing the following method:
s210, obtaining PK (B1), PK (B2),.. from the blockchain platform, PK (bi), (B2),. from PK (bi), (b.from), PK (Bn), and decrypting with the private key SK to obtain B1, B2.. Bn; wherein SK corresponds to PK;
s220, acquiring a plurality of target time periods S1, S2.., Sm; m is the number of the target time periods;
s230, traversing each target event message if D1 i j = A and T1 i j E Sx, or D2 i j = A and T2 i j E Sx, let SNx = SNx + N i j Obtaining a time interval associated user number SN1, SN 2.., SNm corresponding to each target time interval; wherein, x =1, 2.,m, Sx is the x-th target time interval, and SNx is the time interval associated user number corresponding to Sx; SN1, SN 2.., initial values of SNm are all 0;
and S240, determining event execution parameters of the associated event corresponding to the xth target time interval according to SNx.
In an exemplary embodiment of the present application, if D1 i j = A and T1 i j E, Sx, or D2 i j = A and T2 i j E Sx, the second class server is further configured to perform the following method:
s231, let FNx = FNx +1 to obtain the number of target events FN1, FN2, ·, FNm corresponding to each target time period; FNx is the number of target events corresponding to Sx; FN1, FN 2.., FNm all had initial values of 0;
the step S240 includes:
and S241, determining event execution parameters of the associated event corresponding to the xth target time period according to SNx and FNx.
In an exemplary embodiment of the present application, each target event information is replaced with b i j =(D1 i j ,D2 i j ,T1 i j ,T2 i j ,N i j ,Tag i j ,H i j ) Wherein, Tag i j Is b is i j Aircraft identification of the corresponding aircraft, H i j Is b is i j Corresponding event identification;
after the step S210, the second type server is further configured to perform the following method:
s211, determining the upper limit of the user quantity corresponding to each target event according to the aircraft identifier corresponding to each target event information and a preset aircraft-quantity mapping table;
s212, traversing each piece of target event information, and if the number of associated users in the target event information is greater than the corresponding upper limit of the number of users, determining the target event information as abnormal event information;
s213, acquiring historical associated user number LN1, LN 2.., LNy of historical events having the same event identifier as the abnormal event information in a time window according to the event identifier corresponding to the abnormal event information, wherein LNg is the associated user number of the g-th historical event, and g =1, 2...., y, y is the number of the historical events having the same event identifier as the abnormal event information in the time window; the end time of the time window is the current time;
s214, obtaining the fluctuation coefficient U = sqrt (sigma) y g=1 (LNg-Avg(LN1,LN2,...,LNy)) 2 Y); wherein sqrt () is a preset square root determining function, and Avg () is a preset average determining function;
s215, if U is less than K1, replacing the number of associated users corresponding to the abnormal event information with Avg (LN1, LN 2.., LNy); otherwise, LNy is used for replacing the number of the associated users corresponding to the abnormal event information; wherein, K1 is a preset judgment threshold.
In an exemplary embodiment of the present application, K1= MAX (LN1, LN2,.. LNy) -MIN (LN1, LN2,... LNy), where MAX () is a preset maximum value determination function and MIN () is a preset minimum value determination function.
In an exemplary embodiment of the present application, m ∈ [10,14 ].
In an exemplary embodiment of the present application, m = 12.
In an exemplary embodiment of the present application, the SK and PK are derived by the second class of servers according to an asymmetric cryptographic algorithm.
In an exemplary embodiment of the present application, the SK and PK are derived by the second type of server according to a symmetric encryption algorithm.
According to the civil aviation data processing system based on the block chain prediction machine, the first servers and the second servers which are connected with the block chain platform are subjected to credibility verification through the block chain platform, and credibility of the first servers and the second servers is guaranteed. In the subsequent processing process, the first type of server only needs to be connected with the block chain platform, determine a corresponding target event set according to the position identifier A sent by the block chain platform, and encrypt the target event set according to PK to obtain a ciphertext target event set. Therefore, the first type of server does not need to verify the authenticity of the second type of server, and only needs to send the corresponding ciphertext target data set to the block chain platform. Meanwhile, the block chain platform, other first servers and other second servers do not have SK, so that even if the block chain platform receives the ciphertext target data set and carries out data chaining, the block chain platform, other first servers and other second servers cannot acquire corresponding plaintext data, and data security is guaranteed.
In addition, the second type server may obtain the ciphertext target event set from the block chain platform, and decrypt the ciphertext target event set through the SK to obtain the plaintext target event set, so as to determine the event execution parameter of the associated event corresponding to each target time interval according to B1, B2.. Bn.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a block diagram of a civil aviation data processing system based on a block chain prediction machine according to this embodiment.
Detailed Description
The embodiments of the present application will be described in detail below with reference to the accompanying drawings.
It should be noted that, in the case of no conflict, the features in the following embodiments and examples may be combined with each other; moreover, all other embodiments that can be derived by one of ordinary skill in the art from the embodiments disclosed herein without making any creative effort fall within the scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
According to an aspect of the present application, there is provided a civil aviation data processing system based on a block chain prediction machine, as shown in fig. 1, including: a plurality of first-class servers (which can be navigation server), a plurality of second-class servers (which can be airport server), and a block chain platform; the blockchain platform includes an intelligent contract and a predictive engine. And the plurality of first-class servers and the plurality of second-class servers are in communication connection with the block chain platform. The first type of server is used for generating target events (flight events), and each target event is provided with at least two corresponding second type of servers (namely a server of a departure airport and a server of an arrival airport corresponding to each flight event); the second type server is used for generating and executing the associated event (which may be an emergency treatment event) corresponding to the target event corresponding to the second type server. The blockchain platform may further include a blockchain data storage server for storing data after data chaining. The first type server and the second type server can access the block chain data storage server through the block chain platform to acquire the data in the block chain data storage server.
The blockchain platform is used for executing the following method:
s110, the intelligent contract receives the position identification A and the public key PK sent by the second type server; the location identifier a is used to indicate a corresponding location (such as an airport) of the second type server. And each second-class server can obtain the corresponding public key and private key through an asymmetric encryption algorithm or a symmetric encryption algorithm.
S120, the intelligent contract sends A and PK to each first type server through the prediction machine.
S130, theThe intelligent contract receives ciphertext target event sets PK (B1), PK (B2), PK (Bi), so i 1 ,b i 2 ,...,b i f(i) )，b i j =(D1 i j ,D2 i j ,T1 i j ,T2 i j ,N i j ) I =1,2, ·, n, j =1,2,. ·, f (i); wherein, Bi is a target event set returned by the ith first-class server, PK (Bi) is a ciphertext obtained by encrypting Bi by using PK, and n is the number of the first-class servers; b i j F (i) is the jth target event information in the Bi, and f (i) is the number of target event information returned by the ith first-class server; d1 i j Is b is i j The corresponding source location identification (which may be an identification of the departure airport), D2 i j Is b is i j Corresponding destination location identification (which may be an identification of an arrival airport), T1 i j Is b is i j Corresponding event Start time (which may be aircraft takeoff time), T1 i j Is b is i j Corresponding event end time (which may be aircraft arrival time), N i j Is b is i j Corresponding number of associated users (which may be the number of users who purchased the corresponding flight). The source location identifier or the target location identifier of any one of the target event information is the same as a. Wherein, because the number of the target event information corresponding to each first-class server is different, f () is not a set processing function, and f (i) is only used for representing the number of the target event information returned by the corresponding first-class server.
S140, data chaining is performed on PK (B1), PK (B2),.., PK (bi),.., PK (bn).
The second type server is used for executing the following method:
s210, obtaining PK (B1), PK (B2),.. from the blockchain platform, PK (bi),. from PK (B2), PK (Bn) and decrypting with the private key SK, resulting in B1, B2. The SK corresponds to the PK, namely the SK and the PK are generated by the same second type server.
S220, acquiring a plurality of target time periods S1, S2.., Sm; m is the number of the target time periods. The number of the target time periods can be determined according to the relevance event of the second server, and the determined number of the rule adaptability and the specific length of each target time period are set. In this embodiment, S1, S2.., Sm are adjacent in time dimension, and the sum of the corresponding time lengths is 24 hours, specifically, m ∈ [10,14], and preferably m = 12.
S230, traversing each target event message if D1 i j = A and T1 i j E Sx, or D2 i j = A and T2 i j E Sx, let SNx = SNx + N i j Obtaining a time interval associated user number SN1, SN 2.., SNm corresponding to each target time interval; wherein x =1,2,. the m, Sx is the xth target time interval, and SNx is the time interval associated user number corresponding to Sx; SN1, SN 2.., SNm all have an initial value of 0. By D1 i j = A and T1 i j E.g. Sx, and determining the number of associated users whose source positions are the positions of the second type of server corresponding to A, and determining that the corresponding associated users are located at the positions in the target time interval. D2 i j = A and T2 i j The principle of e Sx is similar to the above principle, and is not described in detail. And in turn, it can accurately count how many associated users appear at the above-mentioned locations in each target time interval.
And S240, determining event execution parameters (which can be configuration parameters of preparation personnel, configuration parameters of preparation equipment and the like) of the associated event corresponding to the xth target time interval according to SNx. Each target time interval may be preset with at least one associated event, and the execution parameters of the associated event are influenced by the estimated number of associated users (i.e. the above-mentioned SN1, SN 2.., SNm) that may appear in the corresponding target time interval, or may be directly calculated according to a preset calculation formula and the above-mentioned estimated number.
In the civil aviation data processing system based on the block chain prediction machine provided by the embodiment, the first servers and the second servers which are connected with the block chain platform are subjected to credibility verification through the block chain platform, so that the credibility of the first servers and the second servers is ensured. In the subsequent processing process, the first type of server only needs to be connected with the block chain platform, determine a corresponding target event set according to the position identifier A sent by the block chain platform, and encrypt the target event set according to PK to obtain a ciphertext target event set. Therefore, the first type of server does not need to verify the authenticity of the second type of server, and only needs to send the corresponding ciphertext target data set to the block chain platform. Meanwhile, the block chain platform, other first servers and other second servers do not have SK, so that even if the block chain platform receives the ciphertext target data set and carries out data chaining, the block chain platform, other first servers and other second servers cannot acquire corresponding plaintext data. The safety of the data is ensured.
In addition, the second type of server may obtain the ciphertext target event set from the blockchain platform, and decrypt the ciphertext target event set through the SK to obtain the plaintext target event set, so as to determine the event execution parameter of the associated event corresponding to each target time period according to B1, B2.
In an exemplary embodiment of the present application, if D1 i j = A and T1 i j E Sx, or D2 i j = A and T2 i j E Sx, the second class server is further configured to perform the following method:
s231, let FNx = FNx +1 to obtain the number of target events FN1, FN2, ·, FNm corresponding to each target time period; wherein FNx is the number of target events corresponding to Sx, and the initial values of FN1, FN 2. Thus, the number of the target events corresponding to each target time interval is counted.
The step S240 includes:
and S241, determining event execution parameters of the associated event corresponding to the xth target time interval according to SNx and FNx. Since the execution area corresponding to the associated event may be multiple (e.g., a waiting hall and a ground service area), the configuration parameters of the preparation personnel and the configuration parameters of the preparation equipment in each area may be affected by various factors. Therefore, in the embodiment, the number of associated users and the number of target events corresponding to each target time interval are obtained by the method, so that the determined event execution parameters of the associated events are more accurate.
In an exemplary embodiment of the present application, each target event information is replaced with b i j =(D1 i j ,D2 i j ,T1 i j ,T2 i j ,N i j ,Tag i j ,H i j ) Wherein, Tag i j Is b is i j Aircraft identification (which may be aircraft model), H, of the corresponding aircraft i j Is b is i j The corresponding event identification (which may be a flight number). That is, in this embodiment, compared to the foregoing embodiment, each target event information further includes an aircraft identifier and an event identifier.
After step S210 and before step S230, the second type server is further configured to perform the following method:
s211, determining the upper limit of the user quantity corresponding to each target event according to the aircraft identifier corresponding to each target event information and a preset aircraft-quantity mapping table. The aircraft-quantity mapping table is used for recording the upper limit of the quantity of users (namely the maximum passenger capacity) corresponding to each aircraft identification.
S212, traversing each piece of target event information, and if the number of the associated users in the target event information is larger than the corresponding upper limit of the number of the users, determining the target event information as abnormal event information. If the number of the associated users in the certain target event information is greater than the upper limit of the number of the users corresponding to the aircraft identifier of the target event information, it is indicated that the number of the associated users is wrong. In some embodiments, the data acquisition may be directly performed again to the corresponding first type server (i.e., the first type server that sent the target event information) to obtain the correct data. In many cases, however, the data requested multiple times will be erroneous. But the second server cannot use the wrong data for subsequent processing. Therefore, in this embodiment, the following steps are also performed in this case.
And S213, acquiring historical associated user number LN1, LN 2.., LNy of historical events having the same event identifier as the abnormal event information in a time window according to the event identifier corresponding to the abnormal event information, wherein LNg is the associated user number of the g-th historical event, and g =1, 2.., y, y is the number of historical events having the same event identifier as the abnormal event information in the time window. The end time of the time window is the current time. That is, the number of history-related users who acquire the history data and whose event identifiers correspond to the abnormal event information have the same time representation of the history events (which may be completed flights).
S214, obtaining the fluctuation coefficient U = sqrt (sigma) y g=1 (LNg-Avg(LN1,LN2,...,LNy)) 2 Y); wherein sqrt () is a preset square root determining function, and Avg () is a preset average determining function.
S215, if U is less than K1, replacing the number of associated users corresponding to the abnormal event information with Avg (LN1, LN 2.., LNy); otherwise, LNy is used for replacing the number of the associated users corresponding to the abnormal event information; wherein, K1 is a preset judgment threshold. Where K1= MAX (LN1, LN 2., LNy) -MIN (LN1, LN 2., LNy), where MAX () is a preset maximum value determining function and MIN () is a preset minimum value determining function.
Through the steps, if U is less than K1, the fluctuation of the number of history-related users in a plurality of history events corresponding to abnormal event information is small. At this time, the number of associated users corresponding to the abnormal event information is replaced by Avg (LN1, LN 2.., LNy), so that the number of associated users with the abnormality in the abnormal event information can be modified by taking historical change conditions into consideration.
If U is greater than or equal to K1, it is indicated that the historical number of associated users fluctuates greatly, and at this time, if Avg (LN1, LN 2.., LNy) is used to replace the number of associated users corresponding to the abnormal event information, the number of associated users used last may be too large or too small, and the final event execution parameters are inaccurate.
In this case, the present embodiment replaces the number of associated users corresponding to the abnormal event information with LNy, that is, performs the subsequent processing by using the number of history associated users of the latest history event. Since the fluctuation of the abnormal event information in a relatively close time is relatively small even in the case of a large fluctuation, replacing LNy with the number of associated users corresponding to the abnormal event information enables the finally determined event execution parameter to be more accurate.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method or program product. Accordingly, various aspects of the present application may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device according to this embodiment of the present application. The electronic device is only an example, and should not bring any limitation to the function and the scope of use of the embodiments of the present application.
The electronic device is in the form of a general purpose computing device. Components of the electronic device may include, but are not limited to: the at least one processor, the at least one memory, and a bus connecting the various system components (including the memory and the processor).
Wherein the storage stores program code executable by the processor to cause the processor to perform steps according to various exemplary embodiments of the present application described in the "exemplary methods" section above.
The memory may include readable media in the form of volatile memory, such as Random Access Memory (RAM) and/or cache memory, and may further include Read Only Memory (ROM).
The storage may also include a program/utility having a set (at least one) of program modules including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The bus may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.
The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface. Also, the electronic device may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via a network adapter. The network adapter communicates with other modules of the electronic device over the bus. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, various aspects of the present application may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present application described in the "exemplary methods" section above of this specification, when the program product is run on the terminal device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like 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 computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the present application, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A civil aviation data processing system based on a block chain prediction machine is characterized by comprising: a plurality of first-class servers, a plurality of second-class servers and a block chain platform; the blockchain platform comprises an intelligent contract and a prediction machine; the first servers and the second servers are in communication connection with the block chain platform; the first type of server is used for generating target events, and each target event is provided with at least two corresponding second type of servers; the second type server is used for generating and executing the associated event corresponding to the target event corresponding to the second type server;
the blockchain platform is used for executing the following method:
s110, the intelligent contract receives the position identification A and the public key PK sent by the second type server; the location identifier a is used for indicating a location corresponding to the second type server;
s120, the intelligent contract sends A and PK to each first type server through the prediction machine;
s130, the intelligent contract receives each first type server return through the prediction machineCiphertext target event set PK (B1), PK (B2), PK (Bi), PK (bn), Bi = (B) i 1 ,b i 2 ,...,b i f(i) )，b i j =(D1 i j ,D2 i j ,T1 i j ,T2 i j ,N i j ) I =1,2, ·, n, j =1,2,. ·, f (i); wherein, Bi is a target event set returned by the ith first-class server, PK (Bi) is a ciphertext obtained by encrypting Bi by using PK, and n is the number of the first-class servers; b is a mixture of i j F (i) the number of target event information returned by the ith first-class server; d1 i j Is b is i j Corresponding source location identification, D2 i j Is b is i j Corresponding destination location identification, T1 i j Is b is i j Corresponding event Start time, T1 i j Is b is i j Corresponding event end time, N i j Is b is i j The corresponding number of associated users; the source position identification or the target position identification of any one piece of target event information is the same as A;
s140, performing data uplink on PK (B1), PK (B2),.. cndot., PK (bi),.. cndot., PK (bn);
the second type server is used for executing the following method:
s210, obtaining PK (B1), PK (B2),.. from the blockchain platform, PK (bi), (B2),. from PK (bi), (b.from), PK (Bn), and decrypting with the private key SK to obtain B1, B2.. Bn; wherein SK corresponds to PK;
s220, acquiring a plurality of target time periods S1, S2.., Sm; m is the number of the target time periods;
s230, traversing each target event message if D1 i j = A and T1 i j E Sx, or D2 i j = A and T2 i j E Sx, let SNx = SNx + N i j Obtaining a time interval associated user number SN1, SN 2.., SNm corresponding to each target time interval; wherein x =1,2,. the m, Sx is the xth target time interval, and SNx is the time interval associated user number corresponding to Sx; SN (SN)1, SN 2.., the initial values of SNm are all 0;
and S240, determining event execution parameters of the associated event corresponding to the xth target time interval according to SNx.
2. Civil aviation data processing system according to claim 1, characterized in that D1 is present i j = A and T1 i j E, Sx, or D2 i j = A and T2 i j E Sx, the second class server is further configured to perform the following method:
s231, let FNx = FNx +1 to obtain the number of target events FN1, FN2, ·, FNm corresponding to each target time period; FNx is the number of target events corresponding to Sx; FN1, FN 2.., FNm all had initial values of 0;
the step S240 includes:
and S241, determining event execution parameters of the associated event corresponding to the xth target time period according to SNx and FNx.
3. The civil aviation data processing system of claim 1, wherein each target event message is replaced with b i j =(D1 i j ,D2 i j ,T1 i j ,T2 i j ,N i j ,Tag i j ,H i j ) Wherein, Tag i j Is b is i j Aircraft identification of the corresponding aircraft, H i j Is b is i j A corresponding event identifier;
after the step S210 and before the step S230, the second type server is further configured to perform the following method: s211, determining the upper limit of the user quantity corresponding to each target event according to the aircraft identifier corresponding to each target event information and a preset aircraft-quantity mapping table;
s212, traversing each piece of target event information, and if the number of associated users in the target event information is greater than the corresponding upper limit of the number of users, determining the target event information as abnormal event information;
s213, acquiring historical associated user number LN1, LN 2.., LNy of historical events having the same event identifier as the abnormal event information in a time window according to the event identifier corresponding to the abnormal event information, wherein LNg is the associated user number of the g-th historical event, and g =1, 2...., y, y is the number of the historical events having the same event identifier as the abnormal event information in the time window; the end time of the time window is the current time;
s214, obtaining the fluctuation coefficient U = sqrt (sigma) y g=1 (LNg-Avg(LN1,LN2,...,LNy)) 2 Y); wherein sqrt () is a preset square root determining function, and Avg () is a preset average determining function;
s215, if U is less than K1, replacing the number of associated users corresponding to the abnormal event information with Avg (LN1, LN 2.., LNy); otherwise, LNy is used for replacing the number of the associated users corresponding to the abnormal event information; wherein, K1 is a preset judgment threshold.
4. Civil aviation data processing system according to claim 3,
k1= MAX (LN1, LN 2.,. LNy) -MIN (LN1, LN 2.,. LNy), where MAX () is a preset maximum value determining function and MIN () is a preset minimum value determining function.
5. Civil aviation data processing system according to claim 1, characterised in that m ∈ [10,14 ].
6. Civil aviation data processing system according to claim 1, characterized in that m = 12.
7. The civil aviation data processing system of claim 1, wherein SK and PK are derived by the second class of servers according to an asymmetric cryptographic algorithm.
8. The civil aviation data processing system of claim 1, wherein SK and PK are derived by the second type of server according to a symmetric encryption algorithm.
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