CN116567572A - Unmanned aerial vehicle self-organizing communication network space-time evolution deployment method, device, equipment and medium - Google Patents
Unmanned aerial vehicle self-organizing communication network space-time evolution deployment method, device, equipment and medium Download PDFInfo
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Abstract
The invention provides a method, a device, equipment and a medium for deploying space-time evolution of an unmanned aerial vehicle self-organizing communication network, which comprise the steps of establishing communication relations between a plurality of emergency unmanned aerial vehicles and a plurality of ground communication nodes, initializing a group, carrying out chromosome coding, solving a minimum circumcircle for the ground communication nodes corresponding to each emergency unmanned aerial vehicle, taking the circle center as the position of the emergency unmanned aerial vehicle, carrying out individual fitness evaluation, starting chromosome hybridization variation, generating new individual supplement population quantity to an original level, outputting an optimal emergency unmanned aerial vehicle communication coverage scheme if the maximum multiplication algebra is reached, and otherwise, carrying out chromosome fitness evaluation again. The invention can dynamically deploy and dynamically optimize the position of the emergency unmanned aerial vehicle according to the state change of the ground communication node, effectively support the emergency of the disaster area and ensure the normal network communication of the disaster area.
Description
Technical Field
The invention belongs to the field of geographic information science, and relates to a method, a device, equipment and a medium for deploying space-time evolution of an unmanned aerial vehicle self-organizing communication network.
Background
After serious natural disasters such as earthquake, fire and flood occur, a series of infrastructures including power, communication and the like are damaged, a communication system depending on the communication infrastructures can be paralyzed, serious threat is caused to life safety of people in a disaster area, and huge obstruction is caused to implementation of disaster rescue.
The development of ad hoc wireless communication network technology has provided emergency communication capability to cope with such extreme disasters. By means of the self-organizing wireless communication network technology, a temporary communication network for emergency response can be quickly built in disaster areas, and powerful communication guarantee is provided for natural disaster emergency.
The construction of the self-organizing wireless communication network under the emergency rescue situation is to optimize the position deployment of the unmanned aerial vehicle in real time, and is a serious difficulty in emergency in disaster areas. The actual emergency situation is quite complex due to the fact that the types and the hazard degrees of natural disasters are complex and changeable, the unmanned aerial vehicle emergency self-organizing wireless communication network coverage method for the disaster areas is also suitable for diversified rescue situations, the emergency self-organizing wireless communication network coverage algorithm has high robustness, and the unmanned aerial vehicle emergency self-organizing wireless communication network communication coverage scheme can be dynamically optimized to be capable of meeting disaster emergency tasks, so that the life and property safety of people is guaranteed to the maximum extent.
However, in the existing emergency self-organizing wireless communication network, there are the following problems:
(1) The unmanned aerial vehicle covers less ground communication nodes, so that part of ground communication nodes still cannot communicate through the unmanned aerial vehicle relay;
(2) Unmanned aerial vehicle coverage overlap degree is high, causes unmanned aerial vehicle communication resource's waste.
Aiming at the technical problems of incomplete deployment and low universality of communication nodes of an unmanned aerial vehicle of an self-organizing wireless communication network in the emergency rescue situation when the natural disasters occur at present, the technical defects are solved by providing a communication unmanned aerial vehicle space-time deployment method for disaster emergency response.
Disclosure of Invention
According to the defects of the prior art, the invention aims to provide a method, a device, equipment and a medium for deploying the space-time evolution of an unmanned aerial vehicle self-organizing communication network, which can dynamically deploy and dynamically optimize the position of an emergency unmanned aerial vehicle according to the state change of a ground communication node, effectively support the emergency of a disaster area and ensure the normal network communication of the disaster area.
In order to solve the technical problems, the invention adopts the following technical scheme:
a space-time evolution deployment method of an unmanned aerial vehicle self-organizing communication network comprises the following steps:
step S1, establishing a communication relationship between a plurality of emergency unmanned aerial vehicles and a plurality of ground communication nodes;
s2, initializing a group, and carrying out chromosome coding according to the communication relation between a plurality of emergency unmanned aerial vehicles and a plurality of ground communication nodes;
s3, solving a minimum circumscribed circle for a plurality of ground communication nodes corresponding to each emergency unmanned aerial vehicle, and taking the circle center as the position of the emergency unmanned aerial vehicle;
s4, establishing a chromosome evaluation model, determining the communication coverage task balance degree, emergency urgency degree and attenuation resistance according to the positions of all emergency unmanned aerial vehicles, evaluating individual fitness, starting chromosome selection, storing optimal individuals, and eliminating individuals which do not meet the fitness;
s5, starting chromosome hybridization and mutation, and randomly generating new individual supplementary population quantity to the original level;
s6, outputting an optimal emergency unmanned aerial vehicle communication coverage scheme if the maximum reproduction algebra is reached, otherwise, carrying out chromosome fitness evaluation again;
and S7, if the state of the ground communication nodes is changed, changing the communication relation between the plurality of emergency unmanned aerial vehicles and the plurality of ground communication nodes, and jumping to the steps S3-S6 to output an optimal emergency unmanned aerial vehicle communication coverage scheme.
Further, in step S1, a space-time deployment matrix is covered by constructing an emergency unmanned aerial vehicle communicationEstablishing a communication relationship between a plurality of emergency unmanned aerial vehicles and a plurality of ground communication nodes, wherein y pq =1 indicates that the emergency unmanned aerial vehicle q and the p-th ground communication node can communicate with each other, y pq =0 indicates that communication is not possible between the emergency drone q and the p-th ground communication node.
Further, in step S2, the chromosome adopts a two-stage hierarchical chromosome structure, and is divided into control genes and parameter genes, and the number of the parameter genes is set to be W and the number of the control genes is set to be R on the assumption that the disaster area has W emergency unmanned aerial vehicles and R ground communication nodes in common.
Further, in step S3, ground communication nodes corresponding to each emergency unmanned aerial vehicle are extracted, the ground communication nodes are arranged and combined, and circumscribed circle calculation is performed by using three ground communication node coordinates combined each time, so as to solve a minimum circumscribed circle of the coverage point set.
Further, the fitness Fit is calculated by the following formula:
wherein Ω is communication coverage task balance for evaluating communication coverage task allocation balance; gamma is emergency urgency for examining whether the communication coverage and communication quality are prioritized in the region with stronger urgency;the anti-attenuation capacity is used for measuring the degree of the anti-signal attenuation of the communication link; omega_min, gamma_min,/o>The method is respectively specially designed for measuring the communication coverage task balance degree, the urgency self-adaptive capacity and the attenuation resistanceThreshold, omega 1 ，ω 2 ，ω 3 Are all weights, and omega is more than or equal to 0 1 ，ω 2 ，ω 3 Not more than 1 and omega 1 +ω 2 +ω 3 ＝1。
Further, a communication coverage task balance Ω is calculated:
wherein R is k The number of ground communication nodes linked by the emergency unmanned aerial vehicle k is R, the total number of the ground communication nodes is R, and W is the total number of the emergency unmanned aerial vehicle;
calculating emergency urgency gamma:
wherein W represents the number of emergency unmanned aerial vehicles, M represents the number of ground communication nodes covered by the kth emergency unmanned aerial vehicle, E a The emergency forcing factor of the a ground communication node covered by the k-th emergency unmanned aerial vehicle is represented, and r is the horizontal distance from the ground communication node to the emergency unmanned aerial vehicle; h is the height difference from the ground communication node to the emergency unmanned aerial vehicle;
emergency unmanned aerial vehicle communication coverageUsing classical channel model, the expression of ground received power:
wherein P' is the transmitting power of the aerial emergency unmanned aerial vehicle m; p (P) LoS And P NLoS The link probabilities under the LoS environment and the NLoS environment are respectively; η is a path loss index between the ground communication node and the emergency unmanned aerial vehicle; kappa is the additional attenuation factor for non-line-of-sight links; r is the horizontal distance from the ground communication node to the emergency unmanned aerial vehicle; h is groundThe height difference from the communication node to the emergency unmanned aerial vehicle;
wherein:
P NLoS ＝1-P LoS
wherein, the parameters delta and gamma are called S-cut parameters;
defining a function:
wherein sigma a Is the communication threshold of the ground communication node a; SNR of a The signal to noise ratio of the ground communication node a is calculated by a shannon formula;
wherein N is 0 For thermal noise density N 0 KB is the Boltz slow constant, T is the ground communication node system temperature, B a Bandwidth for ground communication node a;
the communication coverage calculation formula is therefore:
further, in the step S5, the control gene is hybridized based on sequence, and the parameter gene is hybridized at a single point; the control gene adopts exchange mutation operation, and the parameter gene adopts integer mutation operation.
An unmanned aerial vehicle self-organizing communication network space-time evolution deployment device, comprising:
the communication establishing module is used for establishing communication relations between the emergency unmanned aerial vehicle and the ground communication nodes;
the chromosome coding module is used for initializing a group and carrying out chromosome coding according to the communication relation between the emergency unmanned aerial vehicle and the ground communication nodes;
the minimum circumscribed circle solving module is used for solving the minimum circumscribed circles for a plurality of ground communication nodes corresponding to each emergency unmanned aerial vehicle and taking the circle center as the position of the emergency unmanned aerial vehicle;
the fitness evaluation module is used for establishing a chromosome evaluation model, evaluating individual fitness according to the communication coverage task balance degree, emergency urgency degree and attenuation resistance, starting chromosome selection, storing the optimal individual, and eliminating the individual which does not meet the fitness;
the hybridization mutation module is used for starting chromosome hybridization and mutation, and randomly generating new individual supplement population quantity to the original level;
the output module outputs an optimal emergency unmanned aerial vehicle communication coverage scheme if the maximum reproduction algebra is reached, otherwise, the chromosome fitness evaluation is carried out again;
and the updating module is used for changing the communication relation between the plurality of emergency unmanned aerial vehicles and the plurality of ground communication nodes if the ground communication node state changes, and outputting an optimal emergency unmanned aerial vehicle communication coverage scheme.
The unmanned aerial vehicle self-organizing communication network space-time evolution deployment device comprises a processor and a memory for storing a computer program capable of running on the processor, wherein the processor is used for executing the steps of the unmanned aerial vehicle self-organizing communication network space-time evolution deployment method according to any one of the above when running the computer program.
A storage medium having stored therein a computer program which, when executed by a processor, implements the steps of the unmanned aerial vehicle self-organizing communication network space-time evolution deployment method of any one of the above.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) The invention provides a method, a device, equipment and a medium for deploying the space-time evolution of an unmanned aerial vehicle self-organizing communication network, which can quickly construct an emergency unmanned aerial vehicle communication network in a disaster area when a disaster occurs, dynamically deploy and dynamically optimize the position of the emergency unmanned aerial vehicle according to the state change of a ground communication node, more effectively support the emergency of the disaster area and ensure the normal network communication in the disaster area.
(2) The invention provides a space-time evolution deployment method, device, equipment and medium for an unmanned aerial vehicle self-organizing communication network, which are used for constructing an emergency unmanned aerial vehicle communication coverage space-time deployment matrix according to chromosome coding of ground communication nodes, carrying out evolution operation on the chromosomes and guaranteeing that each ground communication node can be distributed to corresponding emergency unmanned aerial vehicle relay communication resources.
(3) According to the unmanned aerial vehicle self-organizing communication network space-time evolution deployment method, the minimum communication coverage circumcircle of the emergency unmanned aerial vehicle is established, and the ground communication nodes are distributed to the emergency unmanned aerial vehicle nearby, so that unnecessary repeated coverage of the emergency unmanned aerial vehicle is avoided.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application. The exemplary embodiments of the present invention and the descriptions thereof are for explaining the present invention and do not constitute an undue limitation of the present invention. In the drawings:
FIG. 1 is a flow chart of a communication unmanned aerial vehicle space-time deployment method for disaster emergency response;
FIG. 2 is a flow chart of an implementation process of the present invention;
FIG. 3 is a diagram of an emergency unmanned aerial vehicle communication coverage space-time deployment matrix
FIG. 4 is a diagram of a two-level hierarchical chromosome structure;
FIG. 5 is a sequence-based hybridization flow chart of control genes;
FIG. 6 is a flow chart of parametric gene single-point hybridization;
FIG. 7 is a flow chart of a control gene exchange variation;
FIG. 8 is a flow chart of parameter gene integer variation;
FIG. 9 is a training process for improving an evolutionary algorithm, with the horizontal axis being the number of iterations and the vertical axis being the optimal individual fitness;
fig. 10 is a simulation result of the drone communication coverage.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention provides a deployment method for space-time evolution of an unmanned aerial vehicle self-organizing communication network, which is shown in a figure 1 and comprises the following steps:
step S1, establishing a communication relationship between a plurality of emergency unmanned aerial vehicles and a plurality of ground communication nodes;
s2, initializing a group, and carrying out chromosome coding according to the communication relation between a plurality of emergency unmanned aerial vehicles and a plurality of ground communication nodes;
s3, solving a minimum circumscribed circle for a plurality of ground communication nodes corresponding to each emergency unmanned aerial vehicle, and taking the circle center as the position of the emergency unmanned aerial vehicle;
s4, establishing a chromosome evaluation model, determining the communication coverage task balance degree, emergency urgency degree and attenuation resistance according to the positions of all emergency unmanned aerial vehicles, evaluating individual fitness, starting chromosome selection, storing optimal individuals, and eliminating individuals which do not meet the fitness;
s5, starting chromosome hybridization and mutation, and randomly generating new individual supplementary population quantity to the original level;
s6, outputting an optimal emergency unmanned aerial vehicle communication coverage scheme if the maximum reproduction algebra is reached, otherwise, carrying out chromosome fitness evaluation again;
and S7, if the state of the ground communication nodes is changed, changing the communication relation between the plurality of emergency unmanned aerial vehicles and the plurality of ground communication nodes, and jumping to the steps S3-S6 to output an optimal emergency unmanned aerial vehicle communication coverage scheme.
The evolutionary algorithm is a meta heuristic algorithm based on natural inheritance and natural selection principles, and the core elements of the evolutionary algorithm comprise selection, hybridization and mutation. In the evolutionary algorithm, each individual consists of a gene string that constitutes a chromosome, representing a viable solution to this problem. The fitness of an evolutionary algorithm is an indicator for assessing the fitness of an individual, and the fitness function is usually determined from an objective function. The selection procedure selects a parent individual according to fitness and inherits its gene to the next generation individual. The selected parent individuals undergo chromosome hybridization operations with a certain probability to produce the next generation of individuals. In order to prevent the sinking into the local optimal solution, mutation operation is performed on the gene with a certain probability. Through multi-generation evolution, the evolution algorithm jumps out of the loop with a defined threshold or iteration number, and a solution with better quality is obtained.
Therefore, the invention provides a space-time evolution deployment method of an unmanned aerial vehicle self-organizing communication network, which comprises the steps of carrying out chromosome coding according to ground communication nodes, constructing an emergency unmanned aerial vehicle communication coverage space-time deployment matrix, carrying out evolution operation on the chromosomes, and guaranteeing that each ground communication node can be allocated to corresponding emergency unmanned aerial vehicle relay communication resources.
According to the unmanned aerial vehicle self-organizing communication network space-time evolution deployment method, the minimum communication coverage circumcircle of the emergency unmanned aerial vehicle is established, and the ground communication nodes are distributed to the emergency unmanned aerial vehicle nearby, so that unnecessary repeated coverage of the emergency unmanned aerial vehicle is avoided.
In step S1, a space-time deployment matrix is covered by constructing an emergency unmanned aerial vehicle communicationEstablishing a communication relationship between a plurality of emergency unmanned aerial vehicles and a plurality of ground communication nodes, wherein y pq =1 indicates that the emergency unmanned aerial vehicle q and the p-th ground communication node can communicate with each other, y pq =0 indicates that communication is not possible between the emergency drone q and the p-th ground communication node.
In one embodiment of the present invention, as shown in fig. 2 and 3, the 1 st ground communication node is capable of communicating with the 2 nd, 3 rd and 5 th emergency robots, but is not capable of communicating with the 1 st, 4 th and 6 th emergency robots.
In step S2, in the unmanned aerial vehicle self-organizing communication network space-time evolution deployment method provided by the invention, a chromosome structure is designed aiming at various emergency self-organizing wireless communication networks, and is highly suitable for the problem of dynamic space-time deployment and optimizing of emergency unmanned aerial vehicle communication coverage.
The essence of chromosome evolution is global optimization, a general iterative optimization method is easy to sink into a locally optimal trap to cause a dead loop phenomenon, so that iteration cannot be performed, and an evolutionary algorithm well overcomes the defect.
The chromosome evolution takes the coding of decision variables as an operation object, and can directly operate structural objects such as a set, a sequence, a matrix, a tree, a graph and the like, for example, the ground communication nodes and the emergency unmanned aerial vehicle are incorporated into the collaborative optimization process of the chromosome evolution.
Specifically, the chromosome adopts a two-stage hierarchical chromosome structure, and is divided into a control gene and a parameter gene. Setting the number of parameter genes as W and the number of control genes as R by setting up W emergency unmanned aerial vehicles and R ground communication nodes in disaster areas.
In one embodiment of the invention, the two-stage hierarchical chromosome structure is shown in fig. 4, and the chromosome structure above represents that the No. 1 emergency unmanned aerial vehicle covers the No. 2, no. 3 and No. 6 ground communication nodes, and the No. 2 emergency unmanned aerial vehicle covers the No. 1, no. 4 and No. 5 ground communication nodes.
The parameter gene codes are regulated and controlled by the control genes, the parameter genes and the control genes participate in the communication coverage optimization of the ground communication nodes together, the parameter genes and the control genes co-evolve towards the direction with high fitness together, and an optimal emergent unmanned aerial vehicle communication coverage scheme is sought.
In step S3, as shown in fig. 10, the ground communication nodes corresponding to the emergency unmanned aerial vehicle solve the minimum circumscribed circle, so as to calculate the minimum circumscribed circle covered by the corresponding emergency unmanned aerial vehicle based on the arranged and combined partial ground communication nodes, and facilitate the subsequent judgment of which communication coverage of the emergency unmanned aerial vehicle the ground communication nodes except the ground communication node constructing the minimum circumscribed circle belong to.
And extracting ground communication nodes corresponding to each emergency unmanned aerial vehicle, arranging and combining the ground communication nodes, and respectively carrying out circumscribed circle calculation according to three ground communication node coordinates combined each time to solve the minimum circumscribed circle of the coverage point set.
Three ground communication nodes are connected to form a triangle, any two sides of the triangle are selected as vertical bisectors, the intersection point of the vertical bisectors is the circle center, and the Euclidean distance from the vertex of the triangle to the circle center is the radius.
Specifically, the calculation formula is:
wherein, (x) 1 ,y 1 )、(x 2 ,y 2 )、(x 3 ,y 3 ) Coordinates of three ground communication nodes, respectively, (x) 0 ,y 0 ) Is the coordinate of the circle center, and R is the radius.
Calculating the distance Dist from each point to the circle center, judging whether the distance Dist from each other point to the circle center is smaller than the radius, if so, the point is positioned in the circumscribing circle, otherwise, discarding the combination, and continuing iteration until the minimum circumscribing circle covering the point set is solved.
The formula for solving the distance Dist between each point and the circle center is as follows:
wherein, (x, y) is the coordinate of one of the ground communication nodes corresponding to a certain emergency unmanned aerial vehicle.
And 3, simulating a natural selection mechanism, and judging the gene quality of each individual by calculating the fitness. The excellent individuals are reserved, the low-fitness individuals are eliminated, and the population can be guided to evolve towards the direction with better fitness, so that the optimal solution is more approximated. Chromosome fitness Fit is calculated from the following formula:
wherein Ω is communication coverage task balance for evaluating communication coverage task allocation balance; gamma is emergency urgency for examining whether the communication coverage and communication quality are prioritized in the region with stronger urgency;the anti-attenuation capacity is used for measuring the degree of the anti-signal attenuation of the communication link; omega_min, gamma_min,/o>The method is respectively specifically designed for measuring the communication coverage task balance degree, the urgency self-adaptive capacity, the threshold value of the attenuation resistance capacity and omega 1 ，ω 2 ，ω 3 Are all weights, and omega is more than or equal to 0 1 ，ω 2 ，ω 3 Not more than 1 and omega 1 +ω 2 +ω 3 ＝1。
Chromosome selection roulette selection, wherein the probability that each individual is selected is proportional to its fitness value. The greater the probability that an individual with greater fitness is selected, the less the probability that an individual with lesser fitness is selected.
Let the fitness value of a certain individual be denoted as f (x i ) The probability of the portion being selected is p (x i ) The cumulative probability is q (x i ) The corresponding calculation formula is:
the communication coverage task balance omega is used for guaranteeing the balance of communication task distribution of the emergency unmanned aerial vehicle, is measured by the number of ground communication nodes with communication network coverage, and calculates the communication coverage task balance:
wherein R is k The number of the ground communication nodes linked by the emergency unmanned aerial vehicle k is R total number of the ground communication nodes.
The emergency urgency γ is described by an emergency urgency factor e. Emergency urgency factor E for each ground communication point a The device is manually arranged and can be divided into multiple stages according to the actual situation. Emergency urgency is defined as:
wherein W represents the number of emergency unmanned aerial vehicles, M represents the number of ground communication nodes covered by the kth emergency unmanned aerial vehicle, E a The emergency forcing factor of the a ground communication node covered by the k-th emergency unmanned aerial vehicle is represented, and r is the horizontal distance from the ground communication node to the emergency unmanned aerial vehicle; h is the height difference from the ground communication node to the emergency unmanned aerial vehicle.
Emergency unmanned aerial vehicle communication coverageUsing classical channel model, the expression of ground received power:
wherein P' is the transmitting power of the aerial emergency unmanned aerial vehicle m; p (P) LoS And P NLoS The link probabilities under the LoS environment and the NLoS environment are respectively; η is a path loss index between the ground communication node and the emergency unmanned aerial vehicle; kappa is the additional attenuation factor for non-line-of-sight links; r is the horizontal distance from the ground communication node to the emergency unmanned aerial vehicle; h is ground communication node to emergencyThe unmanned aerial vehicle's difference in height.
Wherein:
P NLoS ＝1-P LoS
where the parameters delta and gamma are referred to as the S-cut parameters.
Defining a function:
wherein sigma a Is the communication threshold of the ground communication node a; SNR of a The signal to noise ratio of the ground communication node a is calculated by a shannon formula.
Wherein N is 0 For thermal noise density N 0 KB is the Boltz slow constant, T is the ground communication node system temperature, B a Bandwidth for ground communication node a;
the communication coverage calculation formula is therefore:
step S5 combines the quality genes through the process of chromosomal hybridization, increasing the probability of producing a higher quality individual. And a new individual is generated through a mutation operation with a certain probability, so that the algorithm has the capability of jumping out of the local optimum, and the algorithm is more approximate to the global optimum. And the requirement of iterative population individual number is met by randomly supplementing new individuals, and meanwhile, the probability of the algorithm falling into local optimum is reduced to a certain extent in the process of adding new genes to the population.
The control genes adopt the sequence-based hybridization Operation (OBX), see fig. 5, and the sequence-based crossover can keep the corresponding relation between the selected control gene values and the parameter gene values thereof, so that the distribution relation between part of ground communication nodes and the emergency unmanned aerial vehicle is ensured, and meanwhile, the distribution relation is prevented from falling into local optimum.
The parameter genes adopt single-point hybridization, see fig. 6, and the single-point hybridization has a certain intrinsic meaning for selecting the position of the crossing point (such as a coverage strategy of the emergency unmanned aerial vehicle), so that the single-point crossing can cause less damage.
The sequence-based hybridization procedure is as follows: randomly selecting a plurality of genes in a pair of parent chromosomes, wherein the selected positions of the two chromosome genes are consistent;
and (3) finding the position of the selected gene in the chromosome of the parent 1 in the chromosome of the parent 2, and then combining the rest genes in the parent 2 with the selected genes in the parent 1 to obtain a offspring 1, wherein the offspring 2 is sequentially combined by other genes.
The single-point hybridization process is as follows: randomly selecting hybrid points on parent chromosomes, disconnecting the two parent chromosomes from the hybrid points, and then recombining to obtain two new offspring chromosomes.
The control genes adopt exchange mutation operation, see figure 7, the control genes number the ground communication nodes, and the exchange mutation can ensure that a single communication node is only allocated to a single emergency unmanned aerial vehicle, and can prevent the ground communication node allocation strategy from falling into local optimum.
Wherein ground communication nodes a, b are exchanged with a probability, wherein a, b e 1, m.
The parameter genes adopt integer variation operation, see figure 8, and the parameter gene values are integers, so that the integer variation in a certain range can allocate new ground communication nodes for the emergency unmanned aerial vehicle, and the ground communication node allocation strategy is prevented from falling into local optimum.
Wherein, the variation of the emergency unmanned aerial vehicle c is that d meets c, and d is E [1, W ].
The process of crossover mutation is as follows: randomly selecting a mutation on a control gene locus to b, and searching b genes on genes and converting the b genes into a after conversion.
The process of integer variation is as follows: randomly selecting a point c on the gene position of the parameter gene, converting the point c into d, wherein c is equal to d.
In the process of the communication coverage space-time deployment chromosome evolution of the emergency unmanned aerial vehicle, randomly generated individuals are supplemented in the population after hybridization variation, the population quantity returns to the front of selection, the evolutionary algorithm is prevented from being 'early matured' in the dynamic deployment optimization process and is converged to a local optimal solution too early, and further the next step of chromosome hybridization, variation and fitness evaluation is participated.
And 5, calculating the fitness of the chromosome, wherein all ground communication nodes work normally, the chromosome is directly evolved to the optimal fitness of the global individual, and the optimal emergency unmanned aerial vehicle communication coverage scheme can be output after the iteration is completed.
In step S6, the method for correcting the optimal space-time deployment matrix of the emergency self-organizing wireless communication network is that if the states (number and positions) of the ground communication nodes change, new ground communication node information is imported based on the last generation population obtained by previous iteration, the iteration times are reset, the algorithm is restarted, and the result after the iteration is the corrected result.
The maximum number of reproduction algebra is empirically preset to a value such as 100, 500, 1000.
As shown in fig. 10, in an embodiment of the present invention, after the iteration number reaches a certain value, the optimal fitness reaches a certain value, which proves that the evolution process of the present invention severely limits the deployment model of the unmanned aerial vehicle self-organizing communication network, and ensures that the evolution cost is minimum while seeking the optimal coverage scheme of the emergency unmanned aerial vehicle.
The invention also provides a device for deploying the space-time evolution of the unmanned aerial vehicle self-organizing communication network, which comprises the following components:
the emergency unmanned aerial vehicle communication coverage space-time deployment matrix construction module is used for constructing an emergency unmanned aerial vehicle communication coverage space-time deployment matrix;
the chromosome coding module is used for initializing groups and carrying out chromosome coding according to the space-time deployment matrix covered by the emergency unmanned aerial vehicle communication
The minimum circumscribed circle solving module is used for solving the minimum circumscribed circles for a plurality of ground communication nodes corresponding to each emergency unmanned aerial vehicle and taking the circle center as the position of the emergency unmanned aerial vehicle;
the fitness evaluation module is used for establishing a chromosome evaluation model, evaluating individual fitness according to the communication coverage task balance degree, emergency urgency degree and attenuation resistance, starting chromosome selection, storing the optimal individual, and eliminating the individual which does not meet the fitness;
the hybridization mutation module is used for starting chromosome hybridization and mutation, and randomly generating new individual supplement population quantity to the original level;
the output module outputs an optimal emergency unmanned aerial vehicle communication coverage scheme if the maximum reproduction algebra is reached, otherwise, the chromosome fitness evaluation is carried out again;
and the updating module is used for correcting the space-time deployment matrix of the communication coverage of the emergency unmanned aerial vehicle if the state of the ground communication node changes, and outputting an optimal communication coverage scheme of the emergency unmanned aerial vehicle.
The unmanned aerial vehicle self-organizing communication network space-time evolution deployment device comprises a processor and a memory for storing a computer program capable of running on the processor, wherein the processor is used for executing the steps of the unmanned aerial vehicle self-organizing communication network space-time evolution deployment method according to any one of the above when running the computer program.
The memory in the embodiment of the invention is used for storing various types of data so as to support the operation of the unmanned aerial vehicle self-organizing communication network space-time evolution deployment equipment. Examples of such data include: any computer program for operation on a drone ad hoc communication network space-time evolution deployment device.
The unmanned aerial vehicle self-organizing communication network space-time evolution deployment method disclosed by the embodiment of the invention can be applied to a processor or realized by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In the implementation process, each step of the unmanned aerial vehicle self-organizing communication network space-time evolution deployment method can be completed through an integrated logic circuit of hardware in a processor or an instruction in a software form. The processor may be a general purpose processor, a digital signal processor (DSP, digital SignalProcessor), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiment of the invention can be directly embodied in the hardware of the decoding processor or can be implemented by combining hardware and software modules in the decoding processor. The software module can be located in a storage medium, the storage medium is located in a memory, the processor reads information in the memory, and the steps of the unmanned aerial vehicle self-organizing communication network space-time evolution deployment method provided by the embodiment of the invention are completed by combining hardware of the software module.
In an exemplary embodiment, the unmanned aerial vehicle ad hoc communication network space-time evolution deployment apparatus may be implemented by one or more application specific integrated circuits (ASIC, application Specific Integrated Circuit), DSPs, programmable logic devices (PLD, programmable Logic Device), complex programmable logic devices (CPLD, complex Programmable LogicDevice), FPGAs, general purpose processors, controllers, microcontrollers (MCU, micro Controller Unit), microprocessors (Microprocessor), or other electronic elements for performing the aforementioned methods.
It will be appreciated that the memory can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Wherein the nonvolatile Memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM, programmable Read-Only Memory), erasable programmable Read Only Memory (EPROM, erasable Programmable Read-Only Memory), electrically erasable programmable Read Only Memory (EEPROM, electrically Erasable Programmable Read-Only Memory), magnetic random access Memory (FRAM, ferromagnetic random access Memory), flash Memory (Flash Memory), magnetic surface Memory, optical disk, or compact disk Read Only Memory (CD-ROM, compact Disc Read-Only Memory); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be random access memory (RAM, random AccessMemory), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (SRAM, static Random Access Memory), synchronous static random access memory (SSRAM, synchronous Static Random Access Memory), dynamic random access memory (DRAM, dynamic Random Access Memory), synchronous dynamic random access memory (SDRAM, synchronousDynamic Random Access Memory), double data rate synchronous dynamic random access memory (ddr sdram, double Data Rate Synchronous Dynamic Random Access Memory), enhanced synchronous dynamic random access memory (ESDRAM, enhanced Synchronous Dynamic Random Access Memory), synchronous link dynamic random access memory (SLDRAM, syncLink Dynamic Random Access Memory), direct memory bus random access memory (DRRAM, direct Rambus Random Access Memory). The memory described by embodiments of the present invention is intended to comprise, without being limited to, these and any other suitable types of memory.
A storage medium, comprising a storage medium,
it will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The unmanned aerial vehicle self-organizing communication network space-time evolution deployment method is characterized by comprising the following steps of:
step S1, establishing a communication relationship between a plurality of emergency unmanned aerial vehicles and a plurality of ground communication nodes;
s2, initializing a group, and carrying out chromosome coding according to the communication relation between a plurality of emergency unmanned aerial vehicles and a plurality of ground communication nodes;
s3, solving a minimum circumscribed circle for a plurality of ground communication nodes corresponding to each emergency unmanned aerial vehicle, and taking the circle center as the position of the emergency unmanned aerial vehicle;
s4, establishing a chromosome evaluation model, determining the communication coverage task balance degree, emergency urgency degree and attenuation resistance according to the positions of all emergency unmanned aerial vehicles, evaluating individual fitness, starting chromosome selection, storing optimal individuals, and eliminating individuals which do not meet the fitness;
s5, starting chromosome hybridization and mutation, and randomly generating new individual supplementary population quantity to the original level;
s6, outputting an optimal emergency unmanned aerial vehicle communication coverage scheme if the maximum reproduction algebra is reached, otherwise, carrying out chromosome fitness evaluation again;
and S7, if the state of the ground communication nodes is changed, changing the communication relation between the plurality of emergency unmanned aerial vehicles and the plurality of ground communication nodes, and jumping to the steps S3-S6 to output an optimal emergency unmanned aerial vehicle communication coverage scheme.
2. The unmanned aerial vehicle self-organizing communication network space-time evolution deployment method according to claim 1, wherein the method comprises the following steps:
in step S1, a space-time deployment matrix is covered by constructing an emergency unmanned aerial vehicle communicationEstablishing a communication relationship between a plurality of emergency unmanned aerial vehicles and a plurality of ground communication nodes, wherein y pq =1 indicates that the emergency unmanned aerial vehicle q and the p-th ground communication node can communicate with each other, y pq =0 indicates that communication is not possible between the emergency drone q and the p-th ground communication node.
3. The unmanned aerial vehicle self-organizing communication network space-time evolution deployment method according to claim 1, wherein the method comprises the following steps:
in step S2, the chromosome adopts a two-stage hierarchical chromosome structure, and is divided into control genes and parameter genes, and the number of the parameter genes is set to be W and the number of the control genes is set to be R on the assumption that W emergency unmanned aerial vehicles and R ground communication nodes are shared in a disaster area.
4. The unmanned aerial vehicle self-organizing communication network space-time evolution deployment method according to claim 1, wherein the method comprises the following steps:
in step S3, ground communication nodes corresponding to each emergency unmanned aerial vehicle are extracted, the ground communication nodes are arranged and combined, and circumscribed circle calculation is performed by using three ground communication node coordinates combined each time, so as to solve a minimum circumscribed circle of the coverage point set.
5. The unmanned aerial vehicle self-organizing communication network space-time evolution deployment method according to claim 3, wherein the deployment method comprises the following steps:
chromosome fitness Fit is calculated from the following formula:
wherein Ω is communication coverage task balance for evaluating communication coverage task allocation balance; gamma is emergency urgency for examining whether the communication coverage and communication quality are prioritized in the region with stronger urgency;the anti-attenuation capacity is used for measuring the degree of the anti-signal attenuation of the communication link; omega_min, gamma_min,/o>The method is respectively specifically designed for measuring the communication coverage task balance degree, the urgency self-adaptive capacity, the threshold value of the attenuation resistance capacity and omega 1 ，ω 2 ，ω 3 Are all weights, and omega is more than or equal to 0 1 ，ω 2 ，ω 3 Not more than 1 and omega 1 +ω 2 +ω 3 ＝1。
6. The unmanned aerial vehicle self-organizing communication network space-time evolution deployment method according to claim 5, wherein the method comprises the following steps:
calculating the communication coverage task balance degree omega:
wherein R is k The number of ground communication nodes linked by the emergency unmanned aerial vehicle k is R, the total number of the ground communication nodes is R, and W is the total number of the emergency unmanned aerial vehicle;
calculating emergency urgency gamma:
wherein W represents the number of emergency unmanned aerial vehicles, M represents the number of ground communication nodes covered by the kth emergency unmanned aerial vehicle, E a The emergency forcing factor of the a ground communication node covered by the k-th emergency unmanned aerial vehicle is represented, and r is the horizontal distance from the ground communication node to the emergency unmanned aerial vehicle; h is the height difference from the ground communication node to the emergency unmanned aerial vehicle;
emergency unmanned aerial vehicle communication coverageUsing classical channel model, the expression of ground received power:
wherein P' is the transmitting power of the aerial emergency unmanned aerial vehicle m; p (P) LoS And P NLoS The link probabilities under the LoS environment and the NLoS environment are respectively; η is a path loss index between the ground communication node and the emergency unmanned aerial vehicle; kappa is the additional attenuation factor for non-line-of-sight links; r is the horizontal distance from the ground communication node to the emergency unmanned aerial vehicle; h is the height difference from the ground communication node to the emergency unmanned aerial vehicle;
wherein:
P NLoS ＝1-P LoS
wherein, the parameters delta and gamma are called S-cut parameters;
defining a function:
wherein sigma a Is the communication threshold of the ground communication node a; SNR of a The signal to noise ratio of the ground communication node a is calculated by a shannon formula;
wherein N is 0 For thermal noise density N 0 KB is the Boltz slow constant, T is the ground communication node system temperature, B a Bandwidth for ground communication node a;
the communication coverage calculation formula is therefore:
7. the unmanned aerial vehicle self-organizing communication network space-time evolution deployment method according to claim 2, wherein the method comprises the following steps:
in the step S5, the control genes are hybridized based on sequence, and the parameter genes are hybridized in a single point; the control gene adopts exchange mutation operation, and the parameter gene adopts integer mutation operation.
8. The utility model provides an unmanned aerial vehicle self-organizing communication network space-time evolution deployment device which characterized in that includes:
the communication establishing module is used for establishing communication relations between the emergency unmanned aerial vehicle and the ground communication nodes;
the chromosome coding module is used for initializing a group and carrying out chromosome coding according to the communication relation between the emergency unmanned aerial vehicle and the ground communication nodes;
the minimum circumscribed circle solving module is used for solving the minimum circumscribed circles for a plurality of ground communication nodes corresponding to each emergency unmanned aerial vehicle and taking the circle center as the position of the emergency unmanned aerial vehicle;
the fitness evaluation module is used for establishing a chromosome evaluation model, evaluating individual fitness according to the communication coverage task balance degree, emergency urgency degree and attenuation resistance, starting chromosome selection, storing the optimal individual, and eliminating the individual which does not meet the fitness;
the hybridization mutation module is used for starting chromosome hybridization and mutation, and randomly generating new individual supplement population quantity to the original level;
the output module outputs an optimal emergency unmanned aerial vehicle communication coverage scheme if the maximum reproduction algebra is reached, otherwise, the chromosome fitness evaluation is carried out again;
and the updating module is used for changing the communication relation between the plurality of emergency unmanned aerial vehicles and the plurality of ground communication nodes if the ground communication node state changes, and outputting an optimal emergency unmanned aerial vehicle communication coverage scheme.
9. The utility model provides an unmanned aerial vehicle self-organizing communication network space-time evolution deployment equipment which characterized in that: a memory comprising a processor and a computer program for storing a computer program capable of running on the processor, the processor being adapted to perform the steps of the unmanned aerial vehicle ad hoc communication network space-time evolution deployment method of any one of the preceding claims 1-7 when running the computer program.
10. A storage medium having a computer program stored therein, wherein the computer program, when executed by a processor, implements the steps of the unmanned aerial vehicle self-organizing communication network space-time evolution deployment method of any one of the preceding claims 1-7.
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