CN114828139A - Communication networking behavior simulation method and system of cluster device - Google Patents
Communication networking behavior simulation method and system of cluster device Download PDFInfo
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Abstract
The application discloses a communication networking behavior simulation method and system of a cluster device, wherein the method comprises the following steps: acquiring current cluster information of cluster devices, determining target cluster information of communication networking, acquiring communication networking weight coefficients of each outgoing link of each device based on the target cluster information, and acquiring a cluster communication networking matrix according to the communication networking weight coefficients; acquiring a communication networking coefficient of each device; combining the cluster communication networking matrix and the communication networking coefficient to obtain a cluster communication networking behavior matrix; and optimizing the trunking communication networking behavior matrix according to the preset confidence level to obtain the trunking communication networking behavior optimization matrix. The method and the system realize the simulation of the communication networking behavior of the cluster device in the task execution process, and can ensure that the cluster cooperation can achieve the optimal communication networking effect.
Description
Technical Field
The present application relates to the field of intelligent communication networking of cluster devices, and in particular, to a method and a system for simulating communication networking behavior of a cluster device.
Background
Unmanned aerial vehicle has originated in the military field, has entered the rapid development phase at present, and the kind is more and more, and the application is also constantly expanding. In the military field, unmanned aerial vehicles can perform tasks such as monitoring, reconnaissance, interference, striking and the like; in civilian field, unmanned aerial vehicle can carry out tasks such as agricultural plant protection, forest fire prevention, electric power patrol and survey and meteorological detection. Because the task load of a single unmanned aerial vehicle is relatively single, the task execution capacity is limited, and the responsible task requirements and the complex task execution environment are difficult to realize; can form the unmanned aerial vehicle cluster through many unmanned aerial vehicles, through the load capacity complementation and the task coordination between the unmanned aerial vehicle in the cluster, promote the whole task execution efficiency of unmanned aerial vehicle cluster.
The unmanned aerial vehicle cluster is a complex multi-body system formed by a plurality of unmanned aerial vehicles with limited autonomous capacity. It usually does not have centralized command control center, only obtains local information through the intercommunication between the unmanned aerial vehicle in the cluster and cooperates, emerges the overall effect through the self-organizing action. The unmanned aerial vehicle cluster with the self-organizing capability can complete expected task targets in a complex task execution environment under the condition of minimum human intervention.
However, in the process of executing the task, the formation situation, the sudden threat, the electromagnetic environment and other interference factors all change at any time, the communication topology of the unmanned aerial vehicle cluster also needs to be adaptively changed to achieve the task goal, if the communication topology cannot be reconstructed in time through the cluster self-organization behavior, the cluster synergy of the unmanned aerial vehicle cluster is seriously affected, and the task goal cannot be realized.
Disclosure of Invention
In order to solve the problem of communication networking of a cluster device in a complex time-varying task environment, the application provides a communication networking behavior simulation method and system of the cluster device.
In a first aspect, the present application provides a method for simulating a communication networking behavior of a cluster device, which adopts the following technical scheme:
a communication networking behavior simulation method of a cluster device comprises the following steps:
acquiring current cluster information of a cluster device, and determining target cluster information of communication networking;
acquiring a communication networking weight coefficient of each outbound of each device based on the target cluster information, wherein the outbound is a communication link of one device pointing to other devices in the cluster device, and the communication networking weight coefficient is used for describing the probability of the communication link;
obtaining a cluster communication networking matrix according to the communication networking weight coefficient;
acquiring a communication networking coefficient of each device;
combining the cluster communication networking matrix and the communication networking coefficient to obtain a cluster communication networking behavior matrix;
and optimizing the cluster communication networking behavior matrix according to the preset confidence level to obtain the cluster communication networking behavior optimization matrix.
By adopting the technical scheme, the cluster device can be collected by devices with cluster characteristics such as an unmanned aerial vehicle cluster, the positions of the devices in the cluster device can be changed, and the devices can be influenced by barriers, no-fly zones, sudden threats or electromagnetic interference, etc., which may cause that the cluster device can not maintain the current formation mode, the communication topology matched with the formation mode after the formation mode is changed also needs to be adjusted, otherwise, the cluster cooperation can not be carried out, so the current cluster information of the cluster device needs to be obtained, the target cluster information of the communication networking is determined, the communication networking weight coefficient of each outgoing chain of the devices is obtained based on the target cluster information, the outgoing chain is a communication link of one device pointing to other devices in the cluster device, the communication weight coefficient is used for describing the probability of the occurrence of the communication link, and according to the communication networking weight coefficient, the method comprises the steps of obtaining a trunking communication networking matrix, obtaining a communication networking coefficient of each device, obtaining a trunking communication networking behavior matrix by combining the trunking communication networking matrix and the communication networking coefficient, and optimizing the trunking communication networking behavior matrix according to preset credibility to obtain a trunking communication networking behavior optimization matrix. By dynamically acquiring cluster information, establishing a dynamic communication networking behavior matrix, further optimizing the matrix based on preset credibility, and obtaining an updated cluster communication networking behavior optimization matrix, so that target cluster information can be updated in real time when the formation situation of a cluster changes and the cluster scale changes due to the enqueue and dequeue of devices in the cluster, the cluster communication networking behavior optimization matrix is adaptively optimized under the condition that the communication topology tendency of the devices is considered, a new communication topological relation is formed, and the adaptability of the cluster scale and the formation situation is improved; and the occurrence probability of the data frame in the communication link is described by introducing a communication networking weight coefficient so as to explain the communication networking capability of the data frame, and then the most reliable communication link is screened out based on the confidence coefficient, so that the reliability and the stability of data transmission are improved.
Optionally, the obtaining current cluster information of the cluster device and determining target cluster information of a communication networking includes:
acquiring spatial position information of each device in a cluster device, wherein the spatial position information comprises longitude, latitude and height;
acquiring the physical distance between each device and other devices in the cluster device, and taking the corresponding device of which the physical distance is less than or equal to the maximum communication distance as a target device;
and determining the number and the spatial position information of the target devices as target cluster information.
Optionally, obtaining a communication networking weight coefficient of each outbound of each device based on the target cluster information includes:
according to the spatial position information of the target device, calculating to obtain the link weight of each outgoing link of the target device by combining the communication speed requirement, the communication capacity requirement, the minimum safety distance, the maximum communication distance and the electromagnetic noise interference of a communication space of the target device;
and carrying out normalization processing on the link weights of all the links to obtain a communication networking weight coefficient of each outgoing link of the target device.
Optionally, the obtaining a communication networking coefficient of each device includes:
and setting a communication networking coefficient of each device according to a preset communication topology updating requirement, wherein the communication networking coefficient is used for representing the tendency of the corresponding device to update the communication topology.
Optionally, the obtaining a cluster communication networking behavior matrix by combining the cluster communication networking matrix and the communication networking coefficient includes:
according to the communication networking coefficients of all the devices, a cluster communication retention matrix is constructed;
and obtaining a cluster communication networking behavior matrix according to the cluster communication networking matrix, the communication networking coefficient and the cluster communication maintaining matrix, wherein the cluster communication networking behavior matrix represents the communication networking behavior of the cluster device.
Optionally, the optimizing the trunking communication networking behavior matrix according to the preset confidence level to obtain a trunking communication networking behavior optimization matrix includes:
calculating a variance value between each element in the cluster communication networking behavior matrix;
calculating to obtain a preset confidence coefficient according to the variance value;
and screening all elements in the cluster communication networking behavior matrix by taking the preset confidence level as an optimization threshold value to obtain a cluster communication networking behavior optimization matrix.
Optionally, the step of screening all elements in the trunking communication networking behavior matrix with the preset confidence as an optimization threshold to obtain a trunking communication networking behavior optimization matrix includes:
in a cluster communication networking behavior matrix, sequentially selecting communication networking weight coefficients corresponding to each outgoing link of the device according to a descending order until the sum of the selected communication networking weight systems is greater than or equal to the optimization threshold;
and reserving the selected communication networking weight system, and setting the unselected communication networking weight coefficient to be 0 to obtain a cluster communication networking behavior optimization matrix.
Optionally, after all elements in the cluster communication networking behavior matrix are screened to obtain a cluster communication networking behavior optimization matrix, the method further includes:
and carrying out binarization processing on the cluster communication networking behavior optimization matrix for forming an updated communication topological relation.
In a second aspect, the present application provides a communication networking behavior simulation system for a cluster device, which adopts the following technical solution:
the system comprises a first acquisition module, a processing module, a second acquisition module, a communication networking behavior simulation module and an optimization module;
the first acquisition module is used for acquiring the current cluster information of the cluster device and determining the target cluster information of the communication networking;
the processing module is configured to obtain a communication networking weight coefficient of each outbound of each device based on the target cluster information, where an outbound is a communication link in which one device points to another device in the cluster device, and the communication networking weight coefficient is used to describe a probability of occurrence of the communication link;
the processing module is further used for obtaining a cluster communication networking matrix according to the communication networking weight coefficient
The second obtaining module is configured to obtain a communication networking coefficient of each device;
the communication networking behavior simulation module is used for combining the trunking communication networking matrix and the communication networking coefficient to obtain a trunking communication networking behavior matrix;
and the optimization module is used for optimizing the trunking communication networking behavior matrix according to the preset confidence level to obtain the trunking communication networking behavior optimization matrix.
In a third aspect, the present application provides an electronic device, comprising:
a processor, wherein the processor is installed with a processing program, and the processing program is used for executing the communication networking behavior simulation method of the cluster device in the first aspect.
In summary, the present application includes the following beneficial technical effects:
1. in the process that cluster devices execute tasks, each device can be influenced by obstacles, no-fly zones, sudden threats or electromagnetic interference and the like, a dynamic communication networking behavior matrix is established through dynamic acquisition of cluster information, the matrix is further optimized based on preset confidence level, and an updated cluster communication networking behavior optimization matrix is obtained, so that the target cluster information can be updated in real time when the formation situation of a cluster changes and the cluster scale changes due to the enqueue and the dequeue of the devices in the cluster, the cluster communication networking behavior optimization matrix is adaptively optimized under the consideration of the factors of the communication topology tendency updating of the devices, a new communication topological relation is formed, and the adaptability of the cluster scale and the formation situation is improved;
2. the communication networking weight coefficient is introduced to describe the occurrence probability of the data frame in the communication link so as to illustrate the communication networking capability of the data frame, and then the most reliable communication link is screened out based on the confidence coefficient, so that the reliability and the stability of data transmission are improved.
Drawings
Fig. 1 is a schematic flow chart of a communication networking behavior simulation method of a cluster device according to the present application.
Fig. 2 is a schematic flow chart of calculating a communication networking weight coefficient according to the present application.
Fig. 3 is a schematic flowchart of generating a cluster communication networking behavior matrix according to the present application.
Fig. 4 is a schematic flowchart of generating a cluster communication networking behavior optimization matrix according to the present application.
Fig. 5 is a drone cluster communication topology diagram of example one of the present application.
Fig. 6 is a topology diagram of cluster communication of drones with reduced confidence according to the first embodiment of the present application.
Fig. 7 is a communication topology change diagram of single drone enqueuing and dequeuing of the present application.
Fig. 8 is a communication topology change diagram of the unmanned aerial vehicle cluster of the present application with increasing scale.
Fig. 9 is a schematic structural diagram of a communication networking behavior simulation system of a cluster device according to the present application.
Detailed Description
For the purpose of describing the objects, technical solutions and advantages of the present application in detail, the following describes a method and a system for simulating a communication networking behavior of a cluster device according to the present application with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that "based on" in this application means "based at least in part on", i.e. if X is based on Y, then X may be a function of Y and any other factors.
The cluster devices in the present application include, but are not limited to, a cluster of unmanned devices, a cluster of intelligent robots, and a cluster formed by a plurality of other devices, which may be the same or different.
For ease of understanding, the present application will be described in detail by taking a common drone cluster as an example.
The embodiment of the application discloses a communication networking behavior simulation method of a cluster device.
Referring to fig. 1, the method includes:
101, acquiring current cluster information of the cluster device, and determining target cluster information of the communication networking.
The cluster device takes an unmanned aerial vehicle cluster as an example, when the unmanned aerial vehicle cluster executes tasks, the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster is obtained, namely the unmanned aerial vehicles are erected, and the value of the number of the unmanned aerial vehicles is a positive integer greater than 2; acquiring spatial position information of an unmanned aerial vehicle cluster, wherein the spatial position information is represented by a spatial position vector, and the spatial position vector comprises longitude, latitude and height of the unmanned aerial vehicle cluster, for example, the spatial position information of the unmanned aerial vehicles in the cluster is represented; the physical distances between each device and other devices in the cluster device can be calculated based on the spatial position information of each device, the maximum communication distance exists between the two devices due to the influence of conditions such as communication power and the like, communication connection cannot be established if the maximum communication distance is exceeded, then the corresponding device with the physical distance less than or equal to the maximum communication distance is used as a target device, and the number and the spatial position information of the target device are determined and used as target cluster information. By dynamically acquiring the spatial position information, on one hand, the number of target devices for establishing the communication networking can be updated in time when the devices are in-line and out-of-line, and on the other hand, the change of the position information caused by the change of the formation situation can be updated in time, so that the cluster communication networking matrix is updated based on the updated number of the target devices and the spatial position information.
And 102, acquiring a communication networking weight coefficient of each outgoing chain of each device based on the target cluster information.
The specific implementation process is shown in fig. 2, and the specific step of calculating the communication networking weight coefficient includes:
201, calculating to obtain the link weight of each outgoing link of the target device according to the spatial position information of the target device and by combining the communication rate requirement, the communication capacity requirement, the minimum safety distance, the maximum communication distance and the electromagnetic noise interference of the communication space of the target device;
the outgoing chain is a communication link of the unmanned aerial vehicle pointing to other unmanned aerial vehicles in the unmanned aerial vehicle cluster, if a directed edge points to the unmanned aerial vehicle 2 from the unmanned aerial vehicle 1, that is, the unmanned aerial vehicle 1 has a communication link pointing to the unmanned aerial vehicle 2, the unmanned aerial vehicle 1 is called to have an outgoing chain, and the outgoing chain points to the unmanned aerial vehicle 2; when the target unmanned aerial vehicle has at least one outgoing link, calculating to obtain a link weight of each outgoing link of the target unmanned aerial vehicle according to spatial position information of the target unmanned aerial vehicle and by combining communication rate requirements, communication capacity requirements, minimum safety distance, maximum communication distance and electromagnetic noise interference of a communication space of the target unmanned aerial vehicle, wherein due to the addition of an influence factor of the electromagnetic noise interference of the communication space, the embodiment can perform adaptive adjustment of a communication topological relation based on the change of an electromagnetic environment where a cluster is located;
when a drone distributes communication demand between outbound links, its share is related to the current communication demand of the drone (specifically, communication rate demand, in bps), the communication capability requirement of the mission load configuration, the minimum safety distance (related to the model) of the drone cluster, the maximum communication distance (related to the mission load), the average signal-to-noise ratio in the communication space (related to the external electromagnetic environment and the interference level), and the link weight may be defined as:
the unmanned aerial vehicle is indicated with an outgoing chain pointing to the unmanned aerial vehicle, the spatial distance from the unmanned aerial vehicle to the unmanned aerial vehicle is indicated, and the link weight of each outgoing chain of the target unmanned aerial vehicle is indicated;
202, normalizing the link weights of all links to obtain a communication networking weight coefficient of each outgoing link of the target device.
In order to ensure that the weight distributed to all the outgoing links of the unmanned aerial vehicle is consistent with the communication networking requirement (the sum is 1), the link weight of each outgoing link of the target unmanned aerial vehicle is normalized to obtain the communication networking weight coefficient of the target unmanned aerial vehicle:
representing a collection of drones in the drone cluster, other than drones.
And 103, obtaining a cluster communication networking matrix according to the communication networking weight coefficient.
Wherein, assuming that the unmanned plane cluster has an unmanned plane, the communication topology relationship can be represented by a matrix, and a communication topology matrix is constructed, if the unmanned plane in the unmanned plane cluster has a communication link to the unmanned plane, the communication topology matrix is set,
the communication link establishing method comprises the steps that a cluster communication networking matrix is obtained, and the weight of a data networking request sent by an unmanned aerial vehicle to the unmanned aerial vehicle to establish a communication link, namely a communication networking weight coefficient is represented.
And 104, acquiring the communication networking coefficient of each device.
The communication networking coefficient of each unmanned aerial vehicle is set according to a preset communication topology updating requirement, the communication networking coefficient is used for expressing the tendency of the corresponding unmanned aerial vehicle to update the communication topology during task execution, and a new communication networking request is generated by supposing that some probability of the unmanned aerial vehicle is in the process of executing the task; meanwhile, the probability of the unmanned aerial vehicle does not need to generate a new communication networking request, namely, the unmanned aerial vehicle can keep stable data transmission based on the current communication topology to complete the task.
And 105, combining the cluster communication networking matrix and the communication networking coefficient to obtain a cluster communication networking behavior matrix.
Specifically, as shown in fig. 3, the process of generating the trunking communication networking behavior matrix is described in detail, and the steps include:
301, constructing and obtaining a cluster communication retention matrix according to the communication networking coefficients of all the devices;
the cluster communication maintenance matrix represents the probability that the cluster of drones maintains the current communication topology, the value of each element in the matrix being, where,
and 302, obtaining a cluster communication networking behavior matrix according to the cluster communication networking matrix, the communication networking coefficient and the cluster communication maintaining matrix.
Obtaining a cluster communication networking behavior matrix which is a cluster communication networking matrix according to the cluster communication networking matrix, the communication networking coefficient and the cluster communication maintaining matrix; the cluster communication networking behavior matrix represents communication networking behavior of the unmanned aerial vehicle cluster.
In this embodiment, the communication topology is an adjustable value, and it is satisfied that the value is generally taken according to the cluster communication networking behavior and the debugging experience, for example, a typical value, that is, an unmanned aerial vehicle has a probability of 85% to generate a communication topology updating requirement, and a probability of 15% to maintain the current communication topology can complete a task.
And 106, optimizing the cluster communication networking behavior matrix according to the preset confidence level to obtain a cluster communication networking behavior optimization matrix.
The specific process of generating the trunking communication networking behavior optimization matrix is shown in fig. 4, and includes:
401, calculating variance values among elements in a cluster communication networking behavior matrix;
the variance value is obtained by calculating each element in the cluster communication networking behavior matrix;
402, calculating to obtain a preset confidence coefficient according to the variance value;
the preset confidence coefficient is expressed by a formula, the numerical value is related to the quantity scale of the unmanned aerial vehicle cluster and the space position of the unmanned aerial vehicle cluster, and according to experience, a fixed value or a proportionality coefficient of the cluster scale can be obtained according to different execution tasks and formation modes.
And 403, screening all elements in the trunking communication networking behavior matrix by taking the preset confidence level as an optimization threshold value to obtain the trunking communication networking behavior optimization matrix.
And taking the preset confidence level as an optimization threshold value, namely, taking the preset confidence level as a judgment threshold value of the reliability of the unmanned aerial vehicle communication networking, and preferentially ensuring the communication link with the maximum weight in the outgoing link. When the selected out-chain does not meet the confidence requirement, selecting the communication link with the larger weight from the rest out-chains to add to the out-chain reservation list until the sum of the reserved out-chain weights meets the optimization threshold, namely:
then, the weight values of other links of the unmanned aerial vehicle are 0, and a cluster communication networking behavior optimization matrix is obtained;
carrying out binarization processing on the cluster communication networking behavior optimization matrix, setting an element to be 1 if an unmanned aerial vehicle in the cluster has a communication link with the unmanned aerial vehicle, and otherwise, setting the element to be 0;
and recording a binary cluster communication networking behavior optimization matrix.
The implementation principle of the application is as follows:
in the process that cluster devices execute tasks, each device can be influenced by obstacles, no-fly zones, sudden threats or electromagnetic interference and the like, a dynamic communication networking behavior matrix is established through dynamic acquisition of cluster information, the matrix is further optimized based on preset confidence level, and an updated cluster communication networking behavior optimization matrix is obtained, so that the target cluster information can be updated in real time when the formation situation of a cluster changes and the cluster scale changes due to the enqueue and the dequeue of the devices in the cluster, the cluster communication networking behavior optimization matrix is adaptively optimized under the consideration of the factors of the communication topology tendency updating of the devices, a new communication topological relation is formed, and the adaptability of the cluster scale and the formation situation is improved; and the occurrence probability of the data frame in the communication link is described by introducing a communication networking weight coefficient so as to illustrate the communication networking capability of the data frame, and then the most reliable communication link is screened out based on the confidence coefficient, so that the reliability and the stability of data transmission are improved. Therefore, simulation of communication networking behaviors of the cluster device in the task execution process is achieved, and the cluster cooperation can achieve the optimal communication networking effect. The simulation of the communication networking behavior of the cluster device in the task execution process is realized, and the cluster cooperation can achieve the optimal communication networking effect.
As explained below by way of an example of a cluster of drones, the number of drones is set such that, from the last drone number, the spatial position information of each drone is set as shown in table 1 below (taking a constant value 300 regardless of altitude changes):
table 1 example spatial location information
When calculating the communication networking weight coefficient, for the sake of simplicity, get, then when calculating the communication networking weight coefficient, only need to substitute the maximum communication distance, the spatial distance between two unmanned aerial vehicles and minimum safe distance into the formula, just can calculate the communication networking weight coefficient that obtains each unmanned aerial vehicle, then according to communication networking weight coefficient, obtain the trunking communication networking matrix, the trunking communication networking matrix after the normalization is:
in the above cluster communication networking matrix, the sums are summed in columns, and the sum is 1, which indicates that the communication coordination request of the drone is to be distributed on other drones of the cluster. Since the communication is considered directional in this example, the trunking communication networking matrix sums by rows, which do not have a value of 1. And (3) taking a communication networking coefficient, wherein the unmanned cluster communication networking behavior matrix is as follows:
networking behavior matrix in communication
In the column sum, the sum of which is 1, indicates that the communication demand of the cluster drone will be distributed among all drones in the cluster (including self, indicating that the drones do not need communication coordination). Because the communication is considered directional in this example, the matrix of communication networking behavior
The sums are by rows, which have a value other than 1. And selecting the confidence degree of each element variance of the communication networking behavior matrix, and performing communication topology optimization by using the confidence degree as a threshold value to obtain an optimized communication networking behavior matrix. In this example, the confidence threshold is taken as the criterion to keep the satisfactionCommunication connection meeting confidence requirement, abandoning communication connection not meeting confidence requirement, meeting confidence threshold requirement, referring to reserved communication connection, summing communication networking weights according to columns, wherein the numerical values are all larger than the confidence threshold, and after selection, performing communication networking behavior matrix
Is as follows;
binary communication networking matrix
Is recorded as: (ii) a
Communication networking matrix based on binarization
A specific unmanned aerial vehicle cluster communication topological graph is shown in fig. 5;
reducing the confidence may result in fewer communication connections between drones in the cluster, for example, reducing the confidence to a specific drone cluster communication topology as shown in fig. 6.
In the second example, the enqueue and the dequeue of a single unmanned aerial vehicle are taken as examples, and the cluster size is set as the number of the unmanned aerial vehicle, namely the change situation of the communication topology during the enqueue and the dequeue of the unmanned aerial vehicle. The spatial position information of each drone is set as shown in table 2 below (taking a constant value 300 regardless of altitude change), and the north position of the drone is a variable:
table 2 example two-space position information
Taking fig. 7 is a communication topology change diagram of single drone enqueuing and dequeuing, where the X axis represents longitude, the Y axis represents latitude, and the dotted line is longitude coordinates 2200 of the drone, it can be seen that as the Y axis coordinates of the drone change, during enqueuing and gradual dequeuing, the communication topology changes according to its position until it exceeds the communication distance, which results in that the communication topology is called an isolated node.
Example three takes the gradual increase of the size of the unmanned aerial vehicle cluster as an example, specifically from the increase to illustrate the adaptability to different cluster sizes, and the spatial position information is from the increase to the following table 3 (taking a constant value 300 regardless of the height variation);
TABLE 3 example three-dimensional position information
Fig. 8 is a communication topology change diagram of the cluster of drones with increasing scale, wherein the X axis represents longitude and the Y axis represents latitude, and a new communication topology needs to be constructed for each increase of drones.
In the above embodiment, a communication networking behavior simulation method of a cluster device is described in detail, and a communication networking behavior simulation system of a cluster device to which the method is applied is described below by an embodiment, as shown in fig. 9, the present application provides a communication networking behavior simulation system of a cluster device, including:
a first obtaining module 901, a processing module 902, a second obtaining module 903, a communication networking behavior simulation module 904 and an optimization module 905;
a first obtaining module 901, configured to obtain current cluster information of a cluster device, and determine target cluster information of a communication networking;
a processing module 902, configured to obtain, based on target cluster information, a communication networking weight coefficient of each outbound of each device, where an outbound is a communication link in which one device points to another device in a cluster device, and the communication networking weight coefficient is used to describe a probability of occurrence of the communication link;
the processing module 902 is further configured to obtain a cluster communication networking matrix according to the communication networking weight coefficient
A second obtaining module 903, configured to obtain a communication networking coefficient of each device;
a communication networking behavior simulation module 904, configured to combine the cluster communication networking matrix and the communication networking coefficient to obtain a cluster communication networking behavior matrix;
and the optimizing module 905 is configured to optimize the trunking communication networking behavior matrix according to the preset confidence level to obtain the trunking communication networking behavior optimizing matrix.
The implementation principle of the application is as follows: in the process that cluster devices execute tasks, each device can be influenced by obstacles, no-fly zones, sudden threats or electromagnetic interference and the like, a dynamic communication networking behavior matrix is established through dynamic acquisition of cluster information, the matrix is further optimized based on preset confidence level, and an updated cluster communication networking behavior optimization matrix is obtained, so that the target cluster information can be updated in real time when the formation situation of a cluster changes and the cluster scale changes due to the enqueue and the dequeue of the devices in the cluster, the cluster communication networking behavior optimization matrix is adaptively optimized under the consideration of the factors of the communication topology tendency updating of the devices, a new communication topological relation is formed, and the adaptability of the cluster scale and the formation situation is improved; and the occurrence probability of the data frame in the communication link is described by introducing a communication networking weight coefficient so as to illustrate the communication networking capability of the data frame, and then the most reliable communication link is screened out based on the confidence coefficient, so that the reliability and the stability of data transmission are improved.
The present application also provides an electronic device having a processor with a processing program installed thereon, the processing program being configured to execute the above-described communication networking behavior simulation method for a cluster device.
The foregoing is a preferred embodiment of the present application and is not intended to limit the scope of the present application in any way, and any features disclosed in this specification (including the abstract and drawings) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
Claims (10)
1. A communication networking behavior simulation method of a cluster device is characterized by comprising the following steps:
acquiring current cluster information of a cluster device, and determining target cluster information of communication networking;
acquiring a communication networking weight coefficient of each outbound of each device based on the target cluster information, wherein the outbound is a communication link of one device pointing to other devices in the cluster device, and the communication networking weight coefficient is used for describing the probability of the communication link;
obtaining a cluster communication networking matrix according to the communication networking weight coefficient;
acquiring a communication networking coefficient of each device;
combining the cluster communication networking matrix and the communication networking coefficient to obtain a cluster communication networking behavior matrix;
and optimizing the cluster communication networking behavior matrix according to the preset confidence level to obtain the cluster communication networking behavior optimization matrix.
2. The method according to claim 1, wherein the obtaining current cluster information of the cluster device and determining target cluster information of the communication networking comprises:
acquiring spatial position information of each device in a cluster device, wherein the spatial position information comprises longitude, latitude and height;
acquiring the physical distance between each device and other devices in the cluster device, and taking the corresponding device of which the physical distance is less than or equal to the maximum communication distance as a target device;
and determining the number and the spatial position information of the target devices as target cluster information.
3. The method according to claim 2, wherein obtaining a communication networking weight coefficient of each outbound link of each device based on the target cluster information comprises:
calculating to obtain the link weight of each outgoing link of the target device according to the spatial position information of the target device and by combining the communication speed requirement, the communication capacity requirement, the minimum safety distance, the maximum communication distance and the electromagnetic noise interference of a communication space of the target device;
and carrying out normalization processing on the link weights of all the links to obtain a communication networking weight coefficient of each outgoing link of the target device.
4. The method according to claim 1, wherein the obtaining communication networking coefficients of each device comprises:
and setting a communication networking coefficient of each device according to a preset communication topology updating requirement, wherein the communication networking coefficient is used for representing the tendency of the corresponding device to update the communication topology.
5. The method according to claim 1, wherein the obtaining a cluster communication networking behavior matrix by combining the cluster communication networking matrix and the communication networking coefficient includes:
according to the communication networking coefficients of all the devices, a cluster communication retention matrix is constructed;
and obtaining a cluster communication networking behavior matrix according to the cluster communication networking matrix, the communication networking coefficient and the cluster communication maintaining matrix, wherein the cluster communication networking behavior matrix represents the communication networking behavior of the cluster device.
6. The method according to claim 1, wherein the optimizing the trunking communication networking behavior matrix according to a preset confidence level to obtain a trunking communication networking behavior optimization matrix comprises:
calculating a variance value between each element in the cluster communication networking behavior matrix;
calculating to obtain a preset confidence coefficient according to the variance value;
and screening all elements in the cluster communication networking behavior matrix by taking the preset confidence level as an optimization threshold value to obtain a cluster communication networking behavior optimization matrix.
7. The method according to claim 6, wherein the step of screening all elements in the cluster communication networking behavior matrix with the preset confidence as an optimization threshold to obtain the cluster communication networking behavior optimization matrix comprises:
in a cluster communication networking behavior matrix, sequentially selecting communication networking weight coefficients corresponding to each outgoing link of the device according to a descending order until the sum of the selected communication networking weight systems is greater than or equal to the optimization threshold;
and reserving the selected communication networking weight system, and setting the unselected communication networking weight coefficient to be 0 to obtain a cluster communication networking behavior optimization matrix.
8. The method according to claim 6, wherein the step of screening all elements in the cluster communication networking behavior matrix to obtain a cluster communication networking behavior optimization matrix further comprises:
and carrying out binarization processing on the cluster communication networking behavior optimization matrix for forming an updated communication topological relation.
9. A communication networking behavior simulation system of a cluster device, comprising:
the system comprises a first acquisition module, a processing module, a second acquisition module, a communication networking behavior simulation module and an optimization module;
the first acquisition module is used for acquiring the current cluster information of the cluster device and determining the target cluster information of the communication networking;
the processing module is configured to obtain a communication networking weight coefficient of each outbound of each device based on the target cluster information, where an outbound is a communication link in which one device points to another device in the cluster device, and the communication networking weight coefficient is used to describe a probability of occurrence of the communication link;
the processing module is further used for obtaining a cluster communication networking matrix according to the communication networking weight coefficient
The second obtaining module is configured to obtain a communication networking coefficient of each device;
the communication networking behavior simulation module is used for combining the trunking communication networking matrix and the communication networking coefficient to obtain a trunking communication networking behavior matrix;
and the optimization module is used for optimizing the trunking communication networking behavior matrix according to the preset confidence level to obtain the trunking communication networking behavior optimization matrix.
10. An electronic device, comprising:
a processor having installed thereon a handler for performing the method of communication networking behavior simulation of a cluster apparatus of any of claims 1-8.
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