CN115001566A - Unmanned aerial vehicle cluster cooperative processing method - Google Patents
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- CN115001566A CN115001566A CN202210801978.2A CN202210801978A CN115001566A CN 115001566 A CN115001566 A CN 115001566A CN 202210801978 A CN202210801978 A CN 202210801978A CN 115001566 A CN115001566 A CN 115001566A
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- 238000003672 processing method Methods 0.000 title claims abstract description 11
- 238000012549 training Methods 0.000 claims abstract description 37
- 238000004891 communication Methods 0.000 claims abstract description 20
- 238000003062 neural network model Methods 0.000 claims description 19
- 238000000034 method Methods 0.000 claims description 12
- 238000012795 verification Methods 0.000 claims description 12
- 238000007781 pre-processing Methods 0.000 claims description 8
- 238000001914 filtration Methods 0.000 claims description 5
- 238000010200 validation analysis Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 1
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/03—Protecting confidentiality, e.g. by encryption
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/18502—Airborne stations
- H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/04—Key management, e.g. using generic bootstrapping architecture [GBA]
- H04W12/041—Key generation or derivation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/04—Key management, e.g. using generic bootstrapping architecture [GBA]
- H04W12/043—Key management, e.g. using generic bootstrapping architecture [GBA] using a trusted network node as an anchor
- H04W12/0431—Key distribution or pre-distribution; Key agreement
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W76/00—Connection management
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle cluster cooperative processing method, which comprises the steps that each unmanned aerial vehicle in an unmanned aerial vehicle cluster respectively generates a private key thereof to establish communication with a central host; acquiring historical driving data of each unmanned aerial vehicle; inputting historical driving data into a prediction model for training to obtain predicted driving data; the central host computer generates execution ciphertexts for each unmanned aerial vehicle based on the predicted driving data after acquiring the task information, and sends each execution cipher text to the corresponding unmanned aerial vehicle; each unmanned aerial vehicle decrypts the corresponding execution ciphertext through the private key to obtain the execution plaintext, and executes tasks based on the execution plaintext, and when each unmanned aerial vehicle establishes communication with the central host, the tasks sent by the central host are encrypted and transmitted, so that leakage of task information is avoided, and the problem that the task information issued to the node by the central host is the plaintext and is easy to be stolen and cause leakage is solved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle cluster cooperative processing method.
Background
The multi-unmanned aerial vehicle cluster cooperative control is divided into centralized control, distributed control and distributed control. The centralized control method has a unique central host, controls the global nodes and has absolute decision power. The control structure has low system complexity and low data communication pressure.
By adopting the mode, the task information issued to the node by the central host is a plaintext, and is easy to be stolen and then leaked, so that the task execution fails.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle cluster cooperative processing method, and aims to solve the problem that task information issued to a node by a central host is a plaintext and is easy to be stolen and then leaked.
In order to achieve the above object, the present invention provides an unmanned aerial vehicle cluster cooperative processing method, which comprises the following steps:
each unmanned aerial vehicle in the unmanned aerial vehicle cluster generates a private key thereof to establish communication with the central host;
acquiring historical driving data of each unmanned aerial vehicle;
inputting the historical driving data into a prediction model for training to obtain predicted driving data;
the central host computer generates execution ciphertexts for each unmanned aerial vehicle based on the predicted driving data after acquiring task information, and sends each execution cipher text to the corresponding unmanned aerial vehicle;
and each unmanned aerial vehicle decrypts the corresponding execution ciphertext through a private key to obtain an execution plaintext, and executes a task based on the execution plaintext.
The specific mode that each unmanned aerial vehicle respectively generates a private key of the unmanned aerial vehicle and establishes communication with the central host is as follows:
the unmanned aerial vehicle acquires identity information of the central host;
the unmanned aerial vehicle uses a symmetric key obtained by a random number generator;
the unmanned aerial vehicle sets the communication time length of the symmetric key and the central host;
the unmanned aerial vehicle generates a private key of the unmanned aerial vehicle to encrypt the symmetric key, the time length and the timestamp to obtain a first ciphertext;
the unmanned aerial vehicle encrypts the first ciphertext and the identity information of the unmanned aerial vehicle through the identity information of the central host to obtain a second ciphertext, and sends the second ciphertext to the central host;
the central host generates a private key of the central host to decrypt the second ciphertext to obtain a decrypted plaintext;
the central host verifies the identity information of the unmanned aerial vehicle in the decrypted plain text, and after the verification is successful, the symmetric key and the time length are obtained;
and the central host informs the unmanned aerial vehicle of successful decryption through the symmetric key, and communication establishment is completed.
The specific mode of inputting the historical driving data into a prediction model for training to obtain the predicted driving data is as follows:
acquiring a public driving data set;
constructing a neural network model;
training the neural network model by using the public driving data set to obtain a prediction model;
and inputting the historical driving data into a prediction model for training to obtain predicted driving data.
Wherein, the training of the neural network model by using the public driving data set to obtain a prediction model is as follows:
preprocessing the public driving data set to obtain a preprocessed data set;
dividing the preprocessing data set to obtain a training set and a verification set;
training the neural network model by using the training set to obtain a training model;
and verifying the training model by using the verification set, and obtaining a prediction model after the verification is passed.
Wherein the proportion of the training set to the validation set is 8: 2.
wherein, the preprocessing is performed on the public driving data set, and the specific manner of obtaining the preprocessed data set is as follows:
filtering the public driving data set to obtain a filtered data set;
and adjusting the format of the filtering data set based on the input format of the neural network model to obtain a preprocessing data set.
The invention relates to an unmanned aerial vehicle cluster cooperative processing method, which is characterized in that each unmanned aerial vehicle in an unmanned aerial vehicle cluster respectively generates a private key thereof to establish communication with a central host; acquiring historical driving data of each unmanned aerial vehicle; inputting the historical driving data into a prediction model for training to obtain predicted driving data; the central host computer generates execution ciphertexts for each unmanned aerial vehicle based on the predicted driving data after acquiring task information, and sends each execution cipher text to the corresponding unmanned aerial vehicle; each unmanned aerial vehicle decrypts the corresponding execution ciphertext through a private key to obtain an execution plaintext, executes tasks based on the execution plaintext, adopts a ciphertext when each unmanned aerial vehicle establishes communication with the central host, simultaneously, the tasks sent by the central host are also the execution ciphertext generated after encryption, and the unmanned aerial vehicles corresponding to the execution ciphertext can decrypt the execution ciphertext by using the private key to generate the plaintext, so that leakage of task information is avoided, and the problem that the task information issued by the central host to a node is a plaintext and is easy to leak after being stolen is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an unmanned aerial vehicle cluster cooperative processing method provided by the present invention.
Fig. 2 is a flowchart of establishing communication between each drone in the drone cluster and the central host by generating its own private key.
Fig. 3 is a flowchart of inputting the historical driving data into a prediction model for training to obtain predicted driving data.
FIG. 4 is a flow chart of training the neural network model using the public driving dataset to obtain a predictive model.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present invention and should not be construed as limiting the present invention.
Referring to fig. 1 to 4, the present invention provides a method for cluster cooperative processing of unmanned aerial vehicles, including the following steps:
s1, each unmanned aerial vehicle in the unmanned aerial vehicle cluster respectively generates a private key thereof to establish communication with the central host;
the concrete mode is as follows:
s11, the unmanned aerial vehicle acquires the identity information of the central host;
s12, the unmanned aerial vehicle uses the symmetric key obtained by the random number generator;
s13 the drone setting a length of time for the symmetric key to communicate with the central host;
s14, the unmanned aerial vehicle generates a private key of the unmanned aerial vehicle to encrypt the symmetric key, the time length and the time stamp to obtain a first ciphertext;
s15, the unmanned aerial vehicle encrypts the first ciphertext and the identity information of the unmanned aerial vehicle through the identity information of the central host to obtain a second ciphertext, and sends the second ciphertext to the central host;
s16, the central host generates a private key thereof to decrypt the second ciphertext to obtain a decrypted plaintext;
s17, the central host verifies the identity information of the unmanned aerial vehicle in the decrypted plaintext, and after the verification succeeds, the symmetric key and the time length are obtained;
specifically, the drone and the central host can communicate only within a time period, and if the time period exceeds the time period, the steps S11 to S17 need to be executed again to perform communication connection.
And S18, the central host informs the unmanned aerial vehicle of successful decryption through the symmetric key, and communication establishment is completed.
Specifically, the drone may use the symmetric key, a new key, the new key allowed duration, and a new key timestamp to perform key replacement with the central host.
Specifically, key replacement: since communication using the same symmetric key for a long time may give an attacker the advantage of decryption, long rekeying is required to secure the system. Thus, the process of key replacement may occur at any stage when the original key is not expired or has expired. In case the original key is expired, the user and the central host need to re-perform the above key agreement procedure. For the situation that the original key is not expired, if the user wants to replace the key, the original key, the new key, the allowed time length of the new key and the timestamp are used as information.
S2, acquiring historical driving data of each unmanned aerial vehicle;
specifically, a MAP is searched according to historical working conditions, and information such as the speed, the position and the current driving path of the unmanned aerial vehicle is acquired based on the MAP to obtain historical driving data.
S3, inputting the historical driving data into a prediction model for training to obtain predicted driving data;
the concrete mode is as follows:
s31, acquiring an open driving data set;
specifically, the public driving data is crawled on a website through a crawler technology.
S32, constructing a neural network model;
s33, training the neural network model by using the public driving data set to obtain a prediction model;
the concrete method is as follows:
s331, preprocessing the public driving data set to obtain a preprocessed data set;
specifically, the public driving data set is filtered, so that redundant data in the public driving data set is filtered to obtain a filtered data set; and adjusting the format of the filtering data set based on the input format of the neural network model to obtain a preprocessed data set, and increasing the accuracy of subsequent training of the neural network model.
S332, dividing the preprocessed data set to obtain a training set and a verification set;
specifically, the ratio of the training set to the validation set is 8: 2.
s333, training the neural network model by using the training set to obtain a training model;
specifically, before the neural network model is trained, the model parameters, the learning rate and the iteration times of the neural network model are set.
And S334, verifying the training model by using the verification set, and obtaining a prediction model after verification is passed.
Specifically, if the verification fails, the model parameters of the neural network model are adjusted and then the training is performed again.
And S34, inputting the historical driving data into a prediction model for training to obtain predicted driving data.
S4, after acquiring task information, the central host generates execution ciphertexts for each unmanned aerial vehicle based on the predicted driving data, and sends each execution cipher text to the corresponding unmanned aerial vehicle;
s5, each unmanned aerial vehicle decrypts the corresponding execution ciphertext through a private key to obtain an execution plaintext, and executes a task based on the execution plaintext.
The invention relates to an unmanned aerial vehicle cluster cooperative processing method, which is characterized in that each unmanned aerial vehicle in an unmanned aerial vehicle cluster respectively generates a private key thereof to establish communication with a central host; acquiring historical driving data of each unmanned aerial vehicle; inputting the historical driving data into a prediction model for training to obtain predicted driving data; the central host computer generates execution ciphertexts for each unmanned aerial vehicle based on the predicted driving data after acquiring task information, and sends each execution cipher text to the corresponding unmanned aerial vehicle; each unmanned aerial vehicle decrypts the corresponding execution ciphertext through a private key to obtain an execution plaintext, and executes tasks based on the execution plaintext, each unmanned aerial vehicle adopts a ciphertext when establishing communication with the central host, meanwhile, the tasks sent by the central host are the execution ciphertexts generated after encryption, the unmanned aerial vehicles corresponding to the execution ciphertexts can decrypt the execution ciphertexts by using the private key to generate the plaintext, leakage of task information is avoided, and the problem that the task information issued by the central host to a node is the plaintext and is easy to leak after being stolen is solved.
Although the above disclosure is only a preferred embodiment of the unmanned aerial vehicle cluster cooperative processing method of the present invention, it is needless to say that the scope of the present invention is not limited thereto, and those skilled in the art can understand that all or part of the processes of the above embodiment can be implemented, and the equivalent changes made according to the claims of the present invention still belong to the scope covered by the present invention.
Claims (6)
1. An unmanned aerial vehicle cluster cooperative processing method is characterized by comprising the following steps:
each unmanned aerial vehicle in the unmanned aerial vehicle cluster generates a private key thereof to establish communication with the central host;
acquiring historical driving data of each unmanned aerial vehicle;
inputting the historical driving data into a prediction model for training to obtain predicted driving data;
the central host computer generates execution ciphertexts for each unmanned aerial vehicle based on the predicted driving data after acquiring task information, and sends each execution cipher text to the corresponding unmanned aerial vehicle;
and each unmanned aerial vehicle decrypts the corresponding execution ciphertext through a private key to obtain an execution plaintext, and executes a task based on the execution plaintext.
2. The unmanned aerial vehicle cluster coprocessing method of claim 1,
the specific mode that each unmanned aerial vehicle respectively generates a private key thereof to establish communication with the central host is as follows:
the unmanned aerial vehicle acquires identity information of the central host;
the unmanned aerial vehicle uses a symmetric key obtained by a random number generator;
the unmanned aerial vehicle sets the communication time length of the symmetric key and the central host;
the unmanned aerial vehicle generates a private key of the unmanned aerial vehicle to encrypt the symmetric key, the time length and the timestamp to obtain a first ciphertext;
the unmanned aerial vehicle encrypts the first ciphertext and the identity information of the unmanned aerial vehicle through the identity information of the central host to obtain a second ciphertext, and sends the second ciphertext to the central host;
the central host generates a private key of the central host to decrypt the second ciphertext to obtain a decrypted plaintext;
the central host computer verifies the identity information of the unmanned aerial vehicle in the decrypted plaintext, and after the identity information is successfully verified, the symmetric key and the time length are obtained;
and the central host informs the unmanned aerial vehicle of successful decryption through the symmetric key, and communication establishment is completed.
3. The unmanned aerial vehicle cluster coprocessing method of claim 2,
the specific mode of inputting the historical driving data into a prediction model for training to obtain the predicted driving data is as follows:
acquiring a public driving data set;
constructing a neural network model;
training the neural network model by using the public driving data set to obtain a prediction model;
and inputting the historical driving data into a prediction model for training to obtain predicted driving data.
4. The unmanned aerial vehicle cluster coprocessing method of claim 3,
the specific way of using the public driving data set to train the neural network model to obtain a prediction model is as follows:
preprocessing the public driving data set to obtain a preprocessed data set;
dividing the preprocessing data set to obtain a training set and a verification set;
training the neural network model by using the training set to obtain a training model;
and verifying the training model by using the verification set, and obtaining a prediction model after the verification is passed.
5. The unmanned aerial vehicle cluster coprocessing method of claim 4,
the proportion of the training set to the validation set is 8: 2.
6. the unmanned aerial vehicle cluster coprocessing method of claim 5,
the method for preprocessing the public driving data set to obtain the preprocessed data set comprises the following specific steps:
filtering the public driving data set to obtain a filtered data set;
and adjusting the format of the filtering data set based on the input format of the neural network model to obtain a preprocessed data set.
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