CN116528229B - 5G secure communication method and system thereof - Google Patents
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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
- H04W12/0433—Key management protocols
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/60—Context-dependent security
- H04W12/65—Environment-dependent, e.g. using captured environmental data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W56/00—Synchronisation arrangements
- H04W56/001—Synchronization between nodes
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
- H04W84/04—Large scale networks; Deep hierarchical networks
- H04W84/06—Airborne or Satellite Networks
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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
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention discloses a 5G safety communication method and a system thereof, which judge whether a condition for synchronizing a first reference signal and a second reference signal generated by a 5G ground station and a 5G unmanned aerial vehicle exists or not based on a preset rule; synchronizing the second reference signal to the first reference signal by the 5G unmanned aerial vehicle; after synchronization between the first reference signal and the second reference signal is achieved, updating a key sequence of the 5G unmanned aerial vehicle; and communicating with the 5G unmanned aerial vehicle based on the first reference signal and the key sequence. Therefore, the reference signal of the 5G unmanned aerial vehicle and the reference signal of the 5G ground station can be synchronized, the safety of communication data is improved, and the conditions that the 5G unmanned aerial vehicle is hijacked, the communication data is stolen and the like are prevented.
Description
Technical Field
The invention relates to the technical field of intelligent communication, in particular to a 5G secure communication method and a system thereof.
Background
The 5G unmanned aerial vehicle is a novel traffic tool for realizing remote control and data transmission by using a 5G communication technology, has the characteristics of high speed, low time delay, large capacity and the like, and can be widely applied to the fields of traffic logistics, security inspection, emergency rescue and the like.
However, various faults, such as abnormal speed, high deviation, excessive temperature, battery exhaustion, etc., may occur in the flight process of the 5G unmanned aerial vehicle, and these faults may affect the normal operation of the unmanned aerial vehicle, even endanger personnel safety. Therefore, real-time fault detection of the 5G unmanned aerial vehicle is an essential link.
Currently, the fault detection methods commonly used are mainly rule-based methods. The rule-based method is to set some thresholds or conditions according to the operation rule and experience knowledge of the unmanned aerial vehicle, and judge that the unmanned aerial vehicle is faulty when the state parameters of the unmanned aerial vehicle exceed the thresholds or meet the conditions. This approach is simple to implement, but requires a lot of expert knowledge and experience, and is difficult to adapt to complex and diverse flight environments. Thus, an optimized solution is desired.
Disclosure of Invention
The embodiment of the invention provides a 5G secure communication method and a system thereof, which judge whether a condition for synchronizing a first reference signal and a second reference signal generated by a 5G ground station and a 5G unmanned aerial vehicle exists or not based on a preset rule; synchronizing the second reference signal to the first reference signal by the 5G unmanned aerial vehicle; after synchronization between the first reference signal and the second reference signal is achieved, updating a key sequence of the 5G unmanned aerial vehicle; and communicating with the 5G unmanned aerial vehicle based on the first reference signal and the key sequence. Therefore, the reference signal of the 5G unmanned aerial vehicle and the reference signal of the 5G ground station can be synchronized, the safety of communication data is improved, and the conditions that the 5G unmanned aerial vehicle is hijacked, the communication data is stolen and the like are prevented.
The embodiment of the invention also provides a 5G secure communication method, which comprises the following steps:
judging whether a condition for synchronizing a first reference signal and a second reference signal generated by the 5G ground station and the 5G unmanned aerial vehicle exists or not based on a preset rule;
in response to having a condition that causes synchronization of a first reference signal and a second reference signal generated by a 5G ground station and a 5G drone, sending a reference signal synchronization instruction to the 5G drone to cause the 5G drone to synchronize the second reference signal to the first reference signal;
after synchronization between the first reference signal and the second reference signal is achieved, updating a key sequence of the 5G unmanned aerial vehicle; and
communicating with the 5G unmanned aerial vehicle based on the first reference signal and the key sequence; wherein the first reference signal is generated based on a clock signal; the reference signal synchronization instruction specifies a manner of reference signal synchronization, and the key sequence is updated according to the number of times the synchronization reference signal between the 5G drone and the 5G ground station is synchronized or according to a set time period.
The embodiment of the invention also provides a 5G secure communication system, which comprises:
A condition judgment module for judging whether a condition for synchronizing the first reference signal and the second reference signal generated by the 5G ground station and the 5G unmanned aerial vehicle exists or not based on a predetermined rule;
the system comprises an instruction sending module, a first reference signal synchronizing module and a second reference signal synchronizing module, wherein the instruction sending module is used for responding to the condition that a first reference signal and a second reference signal generated by a 5G ground station and a 5G unmanned aerial vehicle are synchronized, and sending a reference signal synchronizing instruction to the 5G unmanned aerial vehicle so that the 5G unmanned aerial vehicle can synchronize the second reference signal with the first reference signal;
a key sequence updating module, configured to update a key sequence of the 5G unmanned aerial vehicle after synchronization between the first reference signal and the second reference signal is achieved; and
a communication module for communicating with the 5G unmanned aerial vehicle based on the first reference signal and the key sequence; wherein the first reference signal is generated based on a clock signal; the reference signal synchronization instruction specifies a manner of reference signal synchronization, and the key sequence is updated according to the number of times the synchronization reference signal between the 5G drone and the 5G ground station is synchronized or according to a set time period.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
fig. 1 is a flowchart of a 5G secure communication method provided in an embodiment of the present invention.
Fig. 2 is a schematic diagram of a system architecture of a 5G secure communication method according to an embodiment of the present invention.
Fig. 3 is a flowchart illustrating a sub-step of step 110 in a 5G secure communication method according to an embodiment of the present invention.
Fig. 4 is a flowchart of the sub-steps of step 112 in a 5G secure communication method according to an embodiment of the present invention.
Fig. 5 is a block diagram of a 5G secure communication system provided in an embodiment of the present invention.
Fig. 6 is an application scenario diagram of a 5G secure communication method provided in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
Unless defined otherwise, all technical and scientific terms used in the embodiments of the application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application.
In describing embodiments of the present application, unless otherwise indicated and limited thereto, the term "connected" should be construed broadly, for example, it may be an electrical connection, or may be a communication between two elements, or may be a direct connection, or may be an indirect connection via an intermediate medium, and it will be understood by those skilled in the art that the specific meaning of the term may be interpreted according to circumstances.
It should be noted that, the term "first\second\third" related to the embodiment of the present application is merely to distinguish similar objects, and does not represent a specific order for the objects, it is to be understood that "first\second\third" may interchange a specific order or sequence where allowed. It is to be understood that the "first\second\third" distinguishing objects may be interchanged where appropriate such that embodiments of the application described herein may be practiced in sequences other than those illustrated or described herein.
In one embodiment of the present invention, fig. 1 is a flowchart of a method for 5G secure communication provided in the embodiment of the present invention. As shown in fig. 1, a 5G secure communication method 100 according to an embodiment of the present invention includes: 110, judging whether a condition for synchronizing the first reference signal and the second reference signal generated by the 5G ground station and the 5G unmanned aerial vehicle exists or not based on a preset rule; 120, in response to having a condition that causes synchronization of a first reference signal and a second reference signal generated by a 5G ground station and a 5G drone, sending a reference signal synchronization instruction to the 5G drone to cause the 5G drone to synchronize the second reference signal with the first reference signal; 130, after synchronization between the first reference signal and the second reference signal is achieved, updating a key sequence of the 5G unmanned aerial vehicle; and, 140, communicating with the 5G unmanned aerial vehicle based on the first reference signal and the key sequence; wherein the first reference signal is generated based on a clock signal; the reference signal synchronization instruction specifies a manner of reference signal synchronization, and the key sequence is updated according to the number of times the synchronization reference signal between the 5G drone and the 5G ground station is synchronized or according to a set time period.
In the step 110, as a specific embodiment of the foregoing technical solution, the predetermined rule includes the following points:
first, 5G unmanned aerial vehicle has no trouble. The 5G unmanned aerial vehicle must be able to synchronize the reference signal in the case of intact equipment, otherwise equipment maintenance should be performed first.
Second, the 5G unmanned aerial vehicle has completed the task currently being performed. In the process of executing tasks, the 5G unmanned aerial vehicle is usually located in a space outside the 5G ground station and is in a flight state, if reference signal synchronization is wanted at this time, the 5G ground station and the unmanned aerial vehicle need to perform signal transmission and reception and the like in daily communication to perform reference signal synchronization, so that the reference signal synchronization can not only fail, but also risk that the reference signal is interfered and stolen, and therefore, the 5G unmanned aerial vehicle needs to perform reference signal synchronization again in a state of not executing tasks.
Thirdly, the 5G unmanned aerial vehicle is located in the signal synchronization area, or the 5G unmanned aerial vehicle establishes communication connection with the 5G ground station through a set communication interface, and the two requirements are met. For a 5G unmanned aerial vehicle located within the signal synchronization region: after the 5G unmanned aerial vehicle performs the task, the unmanned aerial vehicle returns to the 5G ground station, and the signal synchronization area is a set geographical area which is positioned in the 5G ground station and comprises a ground area and an air area. After the 5G unmanned aerial vehicle returns to the signal synchronization area in the 5G ground station, the distance between the 5G unmanned aerial vehicle and the 5G ground station is greatly shortened compared with the distance when the unmanned aerial vehicle is out to execute tasks, and the reference signal synchronization is performed again, so that the synchronization success rate can be improved, and the possibility that signals are interfered and stolen is avoided to a certain extent. For the 5G unmanned aerial vehicle establishes communication connection with the 5G ground station through the communication interface that sets for, after the 5G unmanned aerial vehicle has carried out the task and returns the journey, also can fall in other areas near 5G ground station, for example certain open air apron etc. can be provided with wired interface on the apron, 5G unmanned aerial vehicle is connected with the wired interface on the apron through built-in or external communication interface, communicate with 5G ground station through wired interface, and carry out reference signal synchronization process, carry out the synchronization through wired mode, can improve synchronous success rate equally to avoid the signal to be disturbed and steal to a certain extent's possibility.
In the step 120, after the 5G ground station determines that the 5G unmanned aerial vehicle meets the condition of synchronizing the reference signal, the 5G unmanned aerial vehicle sends a reference signal synchronizing command to the 5G unmanned aerial vehicle, so that the 5G unmanned aerial vehicle synchronizes the second reference signal with the first reference signal. The reference signal synchronization command specifies the manner in which the reference signal is synchronized, for example, by which method the signal is synchronized, by which determination of whether or not to synchronize is repeated several times during the same signal synchronization process, and the like. If the second reference signal and the first reference signal at the same time are the same, the reference signals are synchronized.
As a specific embodiment of the foregoing technical solution, the 5G unmanned aerial vehicle synchronizes the second reference signal with the first reference signal, including the following steps:
and receiving a reference signal synchronization instruction sent by the 5G ground station.
The second reference signal is synchronized with the first reference signal according to the first reference signal.
According to the regulation of the reference signal synchronization instruction, whether the second reference signal is synchronous with the first reference signal or not is judged for a plurality of times within a set time, for example, 3 times of judgment on whether the second reference signal is synchronous with the first reference signal or not is respectively carried out within 100 milliseconds, and the reference signal synchronization process is completed under the condition that all the results of 3 times of judgment are that the second reference signal is synchronous with the first reference signal; in the 3-time judging process, as long as 1 judging result that the second reference signal is not synchronous with the first reference signal appears, the reference signal synchronizing process is immediately stopped, the reference signal synchronizing process is restarted, namely, the reference signal synchronizing instruction sent by the 5G ground station is received again, and then the second reference signal is synchronized with the first reference signal again according to the first reference signal. The reference signal synchronization command specifies the number of times of judging whether the second reference signal is synchronized with the first reference signal and the set time required for the judgment. In the step 130, after the 5G ground station determines that the synchronization of the reference signal is completed, a new key sequence is sent to the 5G unmanned aerial vehicle. The key sequence is a table storing a plurality of keys arranged in order according to some elements such as time. Both the 5G ground station and the 5G drone encrypt and decrypt information by acquiring keys in a key sequence.
One or more different key sequences are stored in the 5G ground station, and one key sequence can correspond to one 5G unmanned aerial vehicle or a plurality of 5G unmanned aerial vehicles, and even all 5G unmanned aerial vehicles. The 5G ground station uses a key sequence corresponding to the A135G unmanned aerial vehicle when decrypting ciphertext information sent by the A135G unmanned aerial vehicle, and the 5G ground station uses a key sequence corresponding to the A275G unmanned aerial vehicle when encrypting information which needs to be sent to the A275G unmanned aerial vehicle. Note that the key sequences corresponding to the a135G unmanned aerial vehicle and the a275G unmanned aerial vehicle may be the same key sequence.
The key sequence is updated according to the number of times the synchronization reference signal between the 5G drone and the 5G ground station is synchronized or according to a set time period. For example, each time the 5G unmanned aerial vehicle completes a task and returns to the 5G ground station, a key sequence is updated, i.e., a new key sequence is replaced, and the next time the unmanned aerial vehicle starts to perform a task, the new key sequence is used for secure communication, or 11 pm: 00 to 13:00, night 19:00 to 21: and a new key sequence is replaced for the 5G unmanned aerial vehicle between 00. By replacing the new key sequence, the randomness of the key is increased, and the cracking property of the communication data is greatly reduced.
In the step 140, after the synchronization of the reference signals is completed and the new key sequence is updated, when the 5G unmanned aerial vehicle starts again to execute the task, the 5G ground station uses the first reference signal completely synchronized with the second reference signal to query in the new key sequence to obtain the key, further decrypt the information sent by the 5G unmanned aerial vehicle, encrypt the information to be sent to the 5G unmanned aerial vehicle, and then send the encrypted information.
Fig. 2 is a schematic diagram of a system architecture of a 5G secure communication method according to an embodiment of the present invention. Fig. 3 is a flowchart illustrating a sub-step of step 110 in a 5G secure communication method according to an embodiment of the present invention. As shown in fig. 2 and 3, the predetermined rule includes that the 5G unmanned aerial vehicle is fault-free; the process for judging that the 5G unmanned aerial vehicle has no faults comprises the following steps: 111, acquiring speed values, height values, temperature values and battery electricity values of the 5G unmanned aerial vehicle at a plurality of preset time points in a preset time period; 112 extracting inter-parameter context-dependent feature vectors from the speed, altitude, temperature and battery power values at the plurality of predetermined points in time; and 113, determining whether the 5G unmanned aerial vehicle has a fault based on the context-associated feature vector.
Further, through comprehensive analysis of data of a plurality of time points, the state of the 5G unmanned aerial vehicle can be more comprehensively known, so that whether faults exist or not can be more accurately judged, and the safety and reliability of the 5G unmanned aerial vehicle are improved. Further, the 5G unmanned aerial vehicle is subjected to fault judgment in an automatic mode, so that the workload of manual judgment can be reduced, and the working efficiency is improved. Further, by extracting the context associated feature vectors among the parameters and judging based on the feature vectors, whether the 5G manned unmanned aerial vehicle has faults or not can be judged more accurately, and therefore judging accuracy is improved. Furthermore, by timely judging whether the 5G unmanned aerial vehicle has a fault or not, measures can be timely taken to repair or replace the unmanned aerial vehicle, so that the loss caused by the fault is reduced.
Specifically, in step 111, a speed value, a height value, a temperature value, and a battery power value of the 5G unmanned aerial vehicle at a plurality of predetermined time points within a predetermined period of time are obtained. Aiming at the technical problems, the technical conception of the application is to comprehensively utilize multi-parameter time sequence data of the 5G unmanned aerial vehicle, namely speed values, altitude values, temperature values and battery electricity values at a plurality of preset time points, and to combine deep learning and artificial intelligence technology to perform automatic fault detection, thereby improving the efficiency and the adaptability of fault detection and reducing the dependence on expert experience.
Specifically, in the technical scheme of the application, the speed value, the height value, the temperature value and the battery power value of the monitored 5G unmanned aerial vehicle at a plurality of preset time points in a preset time period are firstly obtained. More specifically, the speed value may reflect whether the flight speed of the unmanned aerial vehicle is normal or abnormal speed variation; the height value can help to monitor whether the height of the unmanned aerial vehicle meets the requirement, so that collision with other objects or false entering into a no-fly area can be avoided, and meanwhile, whether the unmanned aerial vehicle has abnormal change of the height can be detected; the temperature value can monitor whether the temperature of the unmanned aerial vehicle is normal, avoid overheat or supercooling, and reflect the performance and adaptability of the unmanned aerial vehicle in different environments; the battery state of unmanned aerial vehicle can be detected to battery electric quantity value, avoids the electric quantity not enough to influence unmanned aerial vehicle's operation. In the actual data acquisition process, sensors, such as an accelerometer, a gyroscope, a temperature sensor, a battery power detector and the like, can be installed on the unmanned aerial vehicle to acquire and transmit required data.
The 5G unmanned aerial vehicle is an unmanned aerial vehicle which uses 5G technology for communication and control. The system can communicate and control with the ground control center in real time through a 5G network, and has the characteristics of high speed, low delay and high reliability. The 5G unmanned aerial vehicle can be used for various applications, such as the fields of aerial photography, logistics distribution, agricultural plant protection, emergency rescue and the like. Because of their high speed, high efficiency, low cost and low risk, 5G unmanned aerial vehicles have become an important development in the future aviation field. Meanwhile, the safety and reliability of the 5G unmanned aerial vehicle are very important, and a series of safety measures are required to be adopted to ensure the safe operation of the unmanned aerial vehicle.
In the present application, sensors are used to measure speed values, altitude values, temperature values, and battery power values at a plurality of predetermined time points within a predetermined period of time. Wherein in this process there is a certain link between the speed value, the altitude value, the temperature value and the battery power value. For example, when the unmanned aerial vehicle is flying faster, the battery power may be consumed faster and the temperature may also rise. Also, as the unmanned aerial vehicle fly height increases, the speed of flight may slow down and the temperature may decrease. Therefore, a context correlation analysis is required for these parameters in order to extract the feature vectors between them. These feature vectors may be used in subsequent decision making processes.
Specifically, in step 112, a context-dependent feature vector between parameters is extracted from the speed value, the altitude value, the temperature value, and the battery power value at the plurality of predetermined time points. Fig. 4 is a flowchart of the substep of step 112 in the 5G secure communication method according to the embodiment of the present application, where, as shown in fig. 4, extracting the context-associated feature vector between parameters from the speed value, the altitude value, the temperature value and the battery power value at the plurality of predetermined time points includes: 1121, performing data structuring processing on the speed values, the altitude values, the temperature values and the battery power values of the plurality of preset time points to obtain a multi-parameter time sequence input matrix; 1122, performing multi-scale feature sensing on the multi-parameter time sequence input matrix to obtain a multi-scale multi-parameter time sequence associated feature map; and 1123, extracting associated features of the multi-scale multi-parameter time sequence associated feature map to obtain the context associated feature vector among the parameters.
Through the steps, the method can be as follows: 1. the judgment accuracy is improved: by extracting the context association feature vectors among the parameters, the relationship among different parameters can be more comprehensively analyzed and judged, so that the judgment accuracy is improved. 2. The workload of manual judgment is reduced: the traditional judging method needs manual intervention, is time-consuming and labor-consuming, and can automatically extract the feature vectors, so that the workload of manual intervention is reduced. 3. The loss caused by faults is reduced: through timely discovery and processing unmanned aerial vehicle's trouble, can avoid the loss that the trouble brought, guarantee unmanned aerial vehicle's safe operation. 4. The safety and reliability of the 5G unmanned aerial vehicle are improved: through improving the judgment accuracy and finding out faults in time, the safety and reliability of the 5G unmanned aerial vehicle can be improved, and the safe operation of the unmanned aerial vehicle is ensured.
The method for extracting the context correlation characteristic vector among the parameters from the speed value, the height value, the temperature value and the battery electric quantity value at a plurality of preset time points is an effective method, can improve the safety and the reliability of the 5G unmanned aerial vehicle, reduce the workload of manual intervention and reduce the loss caused by faults.
First, for step 1121, data structuring is performed on the speed values, the altitude values, the temperature values and the battery power values at the plurality of predetermined time points to obtain a multi-parameter time sequence input matrix. It comprises the following steps: and arranging the speed values, the altitude values, the temperature values and the battery power values of the plurality of preset time points into the multi-parameter time sequence input matrix according to the time dimension and the sample dimension.
And then, arranging the speed values, the altitude values, the temperature values and the battery power values of the plurality of preset time points into a multi-parameter time sequence input matrix according to the time dimension and the sample dimension. That is, the data are subjected to data structuring processing, so that the multi-parameter time sequence input matrix can integrate time sequence data of different parameters, the correlation between the parameters and the time is reserved, and meanwhile, the data are in accordance with the input form of a subsequent module.
In one embodiment of the present application, the speed value, the altitude value, the temperature value, and the battery level value at each time point are arranged in time order according to a time dimension to form a time sequence. And arranging the time sequences according to the time dimension to form a multi-parameter time sequence input matrix. And arranging the speed value, the height value, the temperature value and the battery electric quantity value of each time point according to the sample dimension, and arranging the speed value, the height value, the temperature value and the battery electric quantity value according to the sample sequence to form a sample sequence. And arranging a plurality of sample sequences according to the sample dimension to form a multi-parameter time sequence input matrix.
The time dimension refers to a dimension in which data is arranged in time order. In time series data, the time dimension is very important, and can reflect the change trend and periodicity of the data. For example, in the secure communication of the 5G unmanned aerial vehicle, a speed value, a height value, a temperature value, and a battery power value at a plurality of predetermined time points are arranged in a time dimension so as to extract a context-associated feature vector between parameters. The sample dimension refers to the dimension in which data is arranged by different samples. In machine learning, a data set is typically divided into a plurality of samples, each sample containing a plurality of features. In training the model, the features of different samples need to be arranged according to the sample dimension for batch processing. For example, in secure communications of 5G unmanned aerial vehicles, data of multiple unmanned aerial vehicles may be arranged in sample dimensions for batch processing and model training.
Then, for step 1122, the multi-parameter timing input matrix is subjected to multi-scale feature sensing to obtain a multi-scale multi-parameter timing correlation feature map. It comprises the following steps: and the multi-parameter time sequence input matrix passes through a multi-scale feature sensor comprising a first convolutional neural network model and a second convolutional neural network model to obtain the multi-scale multi-parameter time sequence associated feature map.
It is contemplated that the multi-parameter time series input matrix may have different feature distributions at different time spans, that is, the individual parameters may exhibit different patterns of variation at different times. Therefore, in the technical scheme of the application, the multi-parameter time sequence input matrix passes through a multi-scale feature sensor comprising a first convolutional neural network model and a second convolutional neural network model to obtain a multi-scale multi-parameter time sequence associated feature map. That is, the multi-scale feature sensor is constructed by using a convolutional neural network model with convolution kernels of different scales to capture feature information of different scales in the multi-parameter time sequence input matrix, and multiple variation modes in the data are more comprehensively captured.
In particular, for multi-parameter time series input matrices, when feature extraction is performed using smaller convolution kernels, the network model is more concerned about local variations, such as instantaneous variations of parameters in the time dimension and local correlation information between parameters. When larger convolution kernels are used for feature extraction, the range of interest of the network model is larger, such as long-term dependence between multiple parameters. That is, the multi-scale features of the multi-parameter timing input matrix may include variations on different time scales and correlations between the features. For example, the speed, altitude, temperature and battery power values of the drone vary in different time scales during operation, and there may be an inter-influencing, inter-dependent relationship between them. Therefore, in the technical scheme of the application, the characteristics of the multi-parameter time sequence input matrix can be more comprehensively captured by using convolution kernels with different scales, so that the prediction performance of the model is improved.
The multi-scale feature sensor comprising a first convolutional neural network model and a second convolutional neural network model is a model capable of capturing feature information under different scales in a multi-parameter time sequence input matrix, and consists of two convolutional neural network models, namely the first convolutional neural network model and the second convolutional neural network model. The first convolutional neural network model is a convolutional neural network model with a smaller convolutional kernel for capturing characteristic information in a shorter time span in the multi-parameter time sequence input matrix. This model may capture local features in the data, such as short-term trends and periodic changes in the data. The second convolutional neural network model is a convolutional neural network model with a larger convolutional kernel for capturing characteristic information at a longer time span in the multi-parameter time-series input matrix. This model may capture global features in the data, such as long-term trends and seasonal variations in the data.
By combining the two models, the multi-scale feature sensor can more comprehensively capture various change modes in the data, and the accuracy and the robustness of the models are improved.
Finally, for step 1123, the multi-scale multi-parameter timing correlation feature map is subjected to correlation feature extraction to obtain the inter-parameter context correlation feature vector. It comprises the following steps: expanding each feature matrix of the multi-scale multi-parameter time sequence associated feature graph along a channel dimension into a plurality of local feature vectors; and passing the plurality of local feature vectors through a context feature extractor based on a converter model to obtain the inter-parameter context associated feature vector.
Because the convolutional neural network model is limited by a convolutional kernel and cannot capture global semantic association feature distribution information, in the technical scheme of the application, each feature matrix of the multi-scale multi-parameter time sequence association feature graph along the channel dimension is firstly unfolded into a plurality of local feature vectors so as to convert data into an input form conforming to a subsequent model. And then the local feature vectors are based on a context feature extractor of the converter model to obtain inter-parameter context correlation feature vectors. That is, the self-attention ideas of the converter are utilized for global context-dependent semantic extraction of the plurality of local feature vectors. In this way, the inter-parameter contextual relevance feature vector can better reflect the relevance between different parameters.
Specifically, passing the plurality of local feature vectors through a context feature extractor based on a converter model to obtain the inter-parameter context associated feature vector, comprising: passing the plurality of local feature vectors through a context feature extractor based on a converter model to obtain an initial inter-parameter context associated feature vector; cascading the plurality of local feature vectors into a cascaded local feature vector; and carrying out partial sequence semantic fragment enrichment fusion on the cascade local feature vector and the initial inter-parameter context correlation feature vector to obtain the inter-parameter context correlation feature vector.
Further, in the technical solution of the present application, when the inter-parameter context correlation feature vector is obtained by the context feature extractor of the converter model based on the plurality of local feature vectors, the inter-parameter context correlation feature vector may focus on the context correlation of the channel dimension of the multi-parameter sample-time-series cross correlation feature, so, in order to highlight the time-series local correlation in the numerical dimensions of the speed value, the altitude value, the temperature value and the battery power value at the plurality of predetermined time points, the inter-parameter context correlation feature vector may be preferably optimized by fusing the plurality of local feature vectors and the inter-parameter context correlation feature vector.
When fusing the local feature vectors and the inter-parameter context correlation feature vectors, considering that the inter-parameter context correlation feature vectors are obtained by cascading the local feature vectors based on the context feature vectors obtained by the context feature extractor of the converter model, the inter-parameter context correlation feature vectors also have serialization distribution attributes based on the local segment semantics.
Therefore, in order to promote the fusion effect of the plurality of local feature vectors and the context-associated feature vector between the initial parameters, the applicant of the present application cascades the plurality of local feature vectors to obtain cascade local feature vectors, for example, denoted as And the initial inter-parameter context associated feature vector, e.g. denoted +.>Performing segment enrichment fusion of local sequence semantics to obtain optimized inter-parameter context correlation feature vectors, e.g. denoted +.>The method is specifically expressed as follows:
is a feature vector +>And feature vector->Distance matrix between, i.e.)>，/>Andare all column vectors, and +.>Is a weight super parameter.
Here, the partial sequence semantic segment enrichment fuses the coding effect of the sequence-based segment feature distribution on the directional semantics in the predetermined distribution direction of the sequence to embed similarity between sequence segments as a re-weighting factor for inter-sequence association, thereby capturing the similarity between sequences based on the feature representation (feature appearance) at each segment level, realizing the cascade of partial feature vectorsAnd the initial inter-parameter context associated feature vector +.>Is fused by enrichment of the local fragment level semantics of (1) so as to promote the context-associated feature vector among optimized parameters ++>For the cascade local feature vector +.>And the initial inter-parameter context associated feature vector +.>To promote the optimized inter-parameter context associated feature vector +. >Is characterized by the expression of (3).
Specifically, in step 113, it is determined whether there is a failure of the 5G unmanned aerial vehicle based on the inter-parameter context-associated feature vector. It comprises the following steps: and the context association feature vector among the parameters passes through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the monitored 5G unmanned aerial vehicle has faults or not.
And then, the context correlation feature vector among the parameters passes through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the monitored 5G unmanned aerial vehicle has a fault or not. The classifier is a machine learning model for classifying data, and can judge which classification label the data belongs to according to the feature vector. In this scenario, the inter-parameter contextual feature vector is input into the classifier to automatically map it into the corresponding classification label, i.e. "monitored 5G unmanned aerial vehicle is faulty" or "monitored 5G unmanned aerial vehicle is not faulty". Thus, the monitored 5G unmanned aerial vehicle can be intelligently subjected to fault detection.
A classifier is a machine learning algorithm that is used to classify input data into different categories. In the method, the classifier is used for mapping the context correlation feature vector between the parameters to one of the fault and non-fault categories so as to judge whether the monitored 5G unmanned aerial vehicle has a fault or not. Common classifiers include Support Vector Machines (SVMs), decision trees, neural networks, etc. In a specific implementation, a proper classifier can be selected according to the characteristics and actual requirements of the data set, and is optimized and evaluated through a cross-validation method and the like.
And passing the context-associated feature vectors among the parameters through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the monitored 5G unmanned aerial vehicle has faults or not and comprises the following steps: performing full-connection coding on the context-associated feature vectors among the parameters by using a plurality of full-connection layers of the classifier to obtain coded classification feature vectors; and passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
In summary, the 5G secure communication method 100 according to the embodiment of the present invention is illustrated, which can synchronize the reference signal of the 5G unmanned aerial vehicle with the reference signal of the 5G ground station, thereby increasing the security of the communication data and preventing the situations of hijacking the 5G unmanned aerial vehicle and stealing the communication data.
Fig. 5 is a block diagram of a 5G secure communication system provided in an embodiment of the present invention. As shown in fig. 5, the 5G secure communication system includes: a condition judgment module 210 for judging whether or not there is a condition for synchronizing the first reference signal and the second reference signal generated by the 5G ground station and the 5G unmanned aerial vehicle based on a predetermined rule; an instruction sending module 220, configured to send a reference signal synchronization instruction to the 5G unmanned aerial vehicle in response to a condition that enables synchronization of a first reference signal and a second reference signal generated by the 5G ground station and the 5G unmanned aerial vehicle, so that the 5G unmanned aerial vehicle synchronizes the second reference signal with the first reference signal; a key sequence updating module 230, configured to update a key sequence of the 5G unmanned aerial vehicle after synchronization between the first reference signal and the second reference signal is achieved; and a communication module 240 for communicating with the 5G unmanned aerial vehicle based on the first reference signal and the key sequence; wherein the first reference signal is generated based on a clock signal; the reference signal synchronization instruction specifies a manner of reference signal synchronization, and the key sequence is updated according to the number of times the synchronization reference signal between the 5G drone and the 5G ground station is synchronized or according to a set time period.
It will be appreciated by those skilled in the art that the specific operation of the respective steps in the above-described 5G secure communication system has been described in detail in the above description of the 5G secure communication method with reference to fig. 1 to 4, and thus, repetitive description thereof will be omitted.
As described above, the 5G secure communication system 100 according to the embodiment of the present invention can be implemented in various terminal devices, such as a server or the like for 5G secure communication. In one example, the 5G secure communication system 100 according to embodiments of the present invention may be integrated into a terminal device as a software module and/or hardware module. For example, the 5G secure communication system 100 may be a software module in the operating system of the terminal device, or may be an application developed for the terminal device; of course, the 5G secure communication system 100 may also be one of a number of hardware modules of the terminal device.
Alternatively, in another example, the 5G secure communication system 100 and the terminal device may be separate devices, and the 5G secure communication system 100 may be connected to the terminal device through a wired and/or wireless network and transmit the interactive information in a agreed data format.
Fig. 6 is an application scenario diagram of a 5G secure communication method provided in an embodiment of the present invention. As shown in fig. 6, in this application scenario, first, a speed value (e.g., C1 as illustrated in fig. 6), a height value (e.g., C2 as illustrated in fig. 6), a temperature value (e.g., C3 as illustrated in fig. 6), and a battery power value (e.g., C4 as illustrated in fig. 6) of the 5G unmanned aerial vehicle (e.g., M as illustrated in fig. 6) at a plurality of predetermined time points within a predetermined period of time are acquired; the obtained speed value, altitude value, temperature value, and battery power value are then input into a server (e.g., S as illustrated in fig. 6) deployed with a 5G secure communication algorithm, wherein the server is capable of processing the speed value, altitude value, temperature value, and battery power value based on the 5G secure communication algorithm to determine whether the 5G unmanned aerial vehicle is malfunctioning.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. A 5G secure communication method, comprising:
judging whether a condition for synchronizing a first reference signal and a second reference signal generated by the 5G ground station and the 5G unmanned aerial vehicle exists or not based on a preset rule;
in response to having a condition that causes synchronization of a first reference signal and a second reference signal generated by a 5G ground station and a 5G drone, sending a reference signal synchronization instruction to the 5G drone to cause the 5G drone to synchronize the second reference signal to the first reference signal;
after synchronization between the first reference signal and the second reference signal is achieved, updating a key sequence of the 5G unmanned aerial vehicle; and
communicating with the 5G unmanned aerial vehicle based on the first reference signal and the key sequence; wherein the first reference signal is generated based on a clock signal; the reference signal synchronization instruction prescribes a reference signal synchronization mode, and the key sequence is updated according to the number of times of synchronization of the synchronization reference signal between the 5G unmanned aerial vehicle and the 5G ground station or according to a set time period;
wherein the predetermined rule includes that the 5G unmanned aerial vehicle is fault-free; the process for judging that the 5G unmanned aerial vehicle has no faults comprises the following steps:
Acquiring speed values, height values, temperature values and battery electric values of the 5G unmanned aerial vehicle at a plurality of preset time points in a preset time period;
extracting context-associated feature vectors among parameters from the speed values, the altitude values, the temperature values and the battery power values at the plurality of predetermined time points; and
determining whether the 5G manned unmanned aerial vehicle has a fault or not based on the context-associated feature vector among the parameters;
wherein extracting the inter-parameter context-dependent feature vector from the speed value, the altitude value, the temperature value, and the battery power value at the plurality of predetermined time points comprises:
carrying out data structuring processing on the speed values, the height values, the temperature values and the battery electric quantity values of the plurality of preset time points to obtain a multi-parameter time sequence input matrix;
performing multi-scale feature sensing on the multi-parameter time sequence input matrix to obtain a multi-scale multi-parameter time sequence associated feature map; and
extracting associated features of the multi-scale multi-parameter time sequence associated feature map to obtain context associated feature vectors among the parameters;
based on the inter-parameter context correlation feature vector, determining whether the 5G unmanned aerial vehicle has a fault comprises:
And the context association feature vector among the parameters passes through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the monitored 5G unmanned aerial vehicle has faults or not.
2. The 5G secure communication method of claim 1, wherein data structuring the speed value, the altitude value, the temperature value, and the battery power value at the plurality of predetermined time points to obtain a multi-parameter time sequence input matrix comprises:
and arranging the speed values, the altitude values, the temperature values and the battery power values of the plurality of preset time points into the multi-parameter time sequence input matrix according to the time dimension and the sample dimension.
3. The 5G secure communication method of claim 2, wherein performing multi-scale feature sensing on the multi-parameter timing input matrix to obtain a multi-scale multi-parameter timing correlation feature map, comprising:
and the multi-parameter time sequence input matrix passes through a multi-scale feature sensor comprising a first convolutional neural network model and a second convolutional neural network model to obtain the multi-scale multi-parameter time sequence associated feature map.
4. A 5G secure communications method according to claim 3, wherein performing associated feature extraction on the multi-scale multi-parameter timing associated feature map to obtain the inter-parameter context associated feature vector comprises:
Expanding each feature matrix of the multi-scale multi-parameter time sequence associated feature graph along a channel dimension into a plurality of local feature vectors; and
the plurality of local feature vectors are passed through a context feature extractor based on a converter model to obtain the inter-parameter context associated feature vector.
5. The method of 5G secure communication of claim 4, wherein passing the plurality of local feature vectors through a transducer model-based contextual feature extractor to obtain the inter-parameter contextual feature vector comprises:
passing the plurality of local feature vectors through a context feature extractor based on a converter model to obtain an initial inter-parameter context associated feature vector;
cascading the plurality of local feature vectors into a cascaded local feature vector; and
and carrying out partial sequence semantic fragment enrichment fusion on the cascade local feature vector and the initial inter-parameter context association feature vector to obtain the inter-parameter context association feature vector.
6. The 5G secure communication method of claim 5, wherein performing a partial sequence semantic fragment enrichment fusion of the concatenated local feature vector and the initial inter-parameter context correlation feature vector to obtain the inter-parameter context correlation feature vector, comprises: carrying out partial sequence semantic fragment enrichment fusion on the cascade local feature vector and the initial inter-parameter context correlation feature vector by using the following optimization formula to obtain the inter-parameter context correlation feature vector;
Wherein, the optimization formula is:
wherein ,for the cascade of local feature vectors, +.>For the initial inter-parameter context associated feature vector +.>For the transpose of the initial inter-parameter context-associated feature vector,/for the transpose of the initial inter-parameter context-associated feature vector>For the distance matrix between the cascade local feature vectors and the context-dependent feature vectors between the initial parameters +.> and />Are all column vectors, and +.>Is a weight superparameter,/->Representing matrix multiplication +.>Representing addition by position.
7. A 5G secure communication system, comprising:
a condition judgment module for judging whether a condition for synchronizing the first reference signal and the second reference signal generated by the 5G ground station and the 5G unmanned aerial vehicle exists or not based on a predetermined rule;
the system comprises an instruction sending module, a first reference signal synchronizing module and a second reference signal synchronizing module, wherein the instruction sending module is used for responding to the condition that a first reference signal and a second reference signal generated by a 5G ground station and a 5G unmanned aerial vehicle are synchronized, and sending a reference signal synchronizing instruction to the 5G unmanned aerial vehicle so that the 5G unmanned aerial vehicle can synchronize the second reference signal with the first reference signal;
a key sequence updating module, configured to update a key sequence of the 5G unmanned aerial vehicle after synchronization between the first reference signal and the second reference signal is achieved; and
A communication module for communicating with the 5G unmanned aerial vehicle based on the first reference signal and the key sequence; wherein the first reference signal is generated based on a clock signal; the reference signal synchronization instruction prescribes a reference signal synchronization mode, and the key sequence is updated according to the number of times of synchronization of the synchronization reference signal between the 5G unmanned aerial vehicle and the 5G ground station or according to a set time period;
wherein the predetermined rule includes that the 5G unmanned aerial vehicle is fault-free; the process for judging that the 5G unmanned aerial vehicle has no faults comprises the following steps:
acquiring speed values, height values, temperature values and battery electric values of the 5G unmanned aerial vehicle at a plurality of preset time points in a preset time period;
extracting context-associated feature vectors among parameters from the speed values, the altitude values, the temperature values and the battery power values at the plurality of predetermined time points; and
determining whether the 5G manned unmanned aerial vehicle has a fault or not based on the context-associated feature vector among the parameters;
wherein extracting the inter-parameter context-dependent feature vector from the speed value, the altitude value, the temperature value, and the battery power value at the plurality of predetermined time points comprises:
Carrying out data structuring processing on the speed values, the height values, the temperature values and the battery electric quantity values of the plurality of preset time points to obtain a multi-parameter time sequence input matrix;
performing multi-scale feature sensing on the multi-parameter time sequence input matrix to obtain a multi-scale multi-parameter time sequence associated feature map; and
extracting associated features of the multi-scale multi-parameter time sequence associated feature map to obtain context associated feature vectors among the parameters;
based on the inter-parameter context correlation feature vector, determining whether the 5G unmanned aerial vehicle has a fault comprises:
and the context association feature vector among the parameters passes through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the monitored 5G unmanned aerial vehicle has faults or not.
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