CN106503832A - Unmanned someone's cooperative information distribution transmission optimization method and system - Google Patents

Abstract

The present invention provides an unmanned-manned collaborative information distribution delivery optimization method and system, the method comprising: step 1, encoding each information to be distributed according to a preset encoding method and attributes of each information to be distributed, Obtain the initial solution corresponding to each information to be distributed; step 2, use the obtained initial solutions as the initial population, and use the genetic algorithm to solve the pre-set unmanned-manned collaborative information distribution transfer optimization model, so as to obtain the optimal solution; step 3, outputting the scheme corresponding to the optimal solution as the optimal scheme of the unmanned-manned collaborative information distribution transmission optimization problem. The unmanned-manned collaborative information distribution and delivery optimization method and system provided by the invention can effectively improve the accuracy of unmanned-manned collaborative information distribution and delivery.

Description

Unmanned-human collaborative information distribution delivery optimization method and system

technical field

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned-manned cooperative information distribution and transmission optimization method and system.

Background technique

In the execution of complex tasks, the high maneuverability and zero casualty rate of unmanned aerial vehicles and the fuzzy decision-making ability and strong anti-interference ability of manned aircraft present a strong complementarity. Completing complex tasks through unmanned-manned cooperation is the use of It is an important way and an effective way to improve the efficiency of the formation under the existing technical conditions. Among them, the instant communication of various information in the formation plays an important supporting role in the smooth completion of the guarantee task, so how to effectively distribute and transmit relevant information is a key issue in the process of unmanned-manned collaboration. The optimization method of unmanned-manned cooperative information distribution is to select information sources reasonably and plan the time sequence of information transmission to meet the constraints of network performance and realize the effective distribution of information between UAVs and manned machines.

At present, there are many researches on information distribution problems in the context of real-time traffic and cooperative operations at home and abroad, but there are relatively few studies on information distribution optimization problems in the context of unmanned-manned collaboration; at the same time, the research on information distribution problems mainly considers Although the influence factors such as bandwidth and communication distance in the communication network are studied, there are few related studies on the influence of factors such as delay and time window on the distribution and transmission of information in the communication network.

Contents of the invention

(1) Solved technical problems

An object of the embodiments of the present invention is to provide a method and system for optimizing unmanned-manned collaborative information distribution and delivery, so as to improve the accuracy of unmanned-manned collaborative information distribution and delivery.

(2) Technical solution

In order to achieve the above purpose, the first aspect of the present invention provides an unmanned-manned collaborative information distribution delivery optimization method, including:

In the first aspect, an embodiment of the present invention provides an unmanned-manned collaborative information distribution delivery optimization method, including:

Step 1. Encode each piece of information to be distributed according to the preset coding method and the attribute of each piece of information to be distributed, and obtain an initial solution corresponding to each piece of information to be distributed;

Step 2. Use the obtained initial solutions as the initial population, and use the genetic algorithm to solve the pre-set unmanned-manned collaborative information distribution and transmission optimization model, so as to obtain the optimal solution;

Step 3. Output the solution corresponding to the optimal solution as the optimal solution of the unmanned-manned collaborative information distribution delivery optimization problem.

In the second aspect, the embodiment of the present invention provides an unmanned-manned cooperative information distribution delivery optimization system, including:

An initial solution generation module, configured to encode each piece of information to be distributed according to a preset coding method and attributes of each piece of information to be distributed, to obtain an initial solution corresponding to each piece of information to be distributed;

The optimal solution generation module is used to use the obtained initial solutions as the initial population, and use the genetic algorithm to solve the pre-set unmanned-manned collaborative information distribution and transmission optimization model, so as to obtain the optimal solution;

An output module, configured to output the solution corresponding to the optimal solution as the optimal solution of the unmanned-manned collaborative information distribution delivery optimization problem.

(3) Beneficial effects

The unmanned-manned collaborative information distribution and delivery optimization method and system provided by the present invention can effectively improve the accuracy of unmanned-manned collaborative information distribution and delivery.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

FIG. 1 is a schematic flow diagram of an unmanned-manned collaborative information distribution delivery optimization method provided by an embodiment of the present invention;

Fig. 2 is a schematic diagram of a chromosome code in the schematic flow diagram of the unmanned-manned collaborative information distribution transmission optimization method provided by an embodiment of the present invention;

Fig. 3 is a schematic diagram of an embodiment of a partial flow in the unmanned-manned collaborative information distribution delivery optimization method provided by the present invention;

Fig. 4 is a schematic diagram of an embodiment of a partial flow in the unmanned-manned collaborative information distribution delivery optimization method provided by the present invention;

FIG. 5 is a schematic structural diagram of an unmanned-manned cooperative information distribution delivery optimization system provided by an embodiment of the present invention.

detailed description

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

In the first aspect, an embodiment of the present invention provides an unmanned-manned collaborative information distribution delivery optimization method, referring to FIG. 1, the method includes:

S1. Encode each piece of information to be distributed according to a preset coding method and the attribute of each piece of information to be distributed, and obtain an initial solution corresponding to each piece of information to be distributed;

S2. Using the obtained initial solutions as the initial population, use the genetic algorithm to solve the pre-set unmanned-manned collaborative information distribution and transmission optimization model, so as to obtain the optimal solution;

S3. Outputting the solution corresponding to the optimal solution as the optimal solution of the unmanned-manned collaborative information distribution delivery optimization problem.

The unmanned-manned collaborative information distribution and transfer optimization method provided by the present invention uses the genetic algorithm and the preset unmanned-manned collaborative information distribution and transfer optimization model to optimize the initial solution corresponding to each information to be distributed to obtain the optimal solution output. This can effectively improve the accuracy of unmanned-human collaborative information distribution.

In specific implementation, the objective function of the unmanned-human cooperative information distribution transfer optimization model here can be specifically:

The constraints can be specified as:

ET _t ≤ l _t ,t∈T;

ET _t ≥ _{e t} , t∈T;

ET _t -ST _{t ≤} D, t ∈ T;

Among them, E={<i,j>|i,j∈V,i≠j} represents the set of directed edges, where <i,j> represents the directed edge from node i to node j in the communication network topology;

W={w _ij |i,j∈V} represents the weight set of each directed edge in the graph, where w _ij represents the Euclidean distance between node i and node j.

B _v represents the maximum amount of data that node v can provide, where v represents any node in the communication network topology, v∈V;

T={1,2,...,n} represents the information set to be distributed, n represents the number of elements in the set, t represents any information to be distributed, t∈T;

[e _t , l _t ] means that the information t to be distributed needs to arrive at the information sink within this time window, e _t means the earliest arrival time, l _t means the latest arrival time;

ST _t represents the time when the information to be distributed t actually starts to be distributed from the information source, and ET _t represents the time when the information to be distributed t actually arrives at the information sink;

SN _t represents the actual information source of the information t to be distributed, and EN _t represents the information sink that needs to receive the information t to be distributed;

Indicates the transmission delay of information t to be distributed from node i to node j;

Indicates the propagation delay of information t to be distributed from node i to node j;

D represents the maximum acceptable delay in the communication network topology;

TW _t represents the bandwidth required for information t to be distributed;

NW _ij represents the maximum bandwidth that the directed edge <i,j> in the communication network topology can bear;

Decision variables defined as:

During specific implementation, step S1 may specifically include:

S10, the number n of the information to be distributed is used as the number of genes in the chromosome in the genetic algorithm, and the genes are coded in the form of quintuples, as shown in the following formula;

Gene=(Sflag, Task_start, Task_end, Stime_start, Stime_end)

Among them, Sflag indicates whether the information to be distributed is distributed, Task_start indicates the information source of the information to be distributed, Task_end indicates the information sink of the information to be distributed, Stime_start indicates the actual distribution time of the information to be distributed from the information source, and Stime_end indicates the actual arrival information of the information to be distributed time to stay. In Figure 2, a chromosome composed of 5 genes is described. Taking the first gene as an example, (1,1,2,8,9.5) means that the first information to be distributed is sent from the information source numbered 1. To the information sink numbered 2, the sending time is from the 8th second to the 9.5th second.

In addition, the calculation formula of the fitness function is f=D-M, where D is a given maximum value, and M is the corresponding total distribution time under the current chromosome code. Considering that the information to be distributed needs to meet the bandwidth, delay, time window and information source constraints of the communication network topology in the unmanned-manned information distribution model, it is also necessary to perform constraint verification on chromosomes. For the chromosomes that fail to pass the constraint verification, the penalty factor is added to the value of the objective function to make the value of the fitness function smaller, so as to remove the chromosomes that do not meet the given constraints.

Then there is the process of assigning values to each attribute in the quintuple, see Figure 3, which can specifically include:

S11, read the attributes of n pieces of information to be distributed from the attribute table of the information to be distributed, if all the information to be distributed can be distributed, that is, the value of Sflag of n genes in the chromosome is 1; in the attribute table of the information to be distributed The elements include Task_id, Datavolume, Timewindow_start, Timewindow_end, and Stime_end, where Task_id represents the serial number of the information to be distributed, Datavolume represents the data volume of the information to be distributed, Timewindow_start represents the starting point of the time window of the information to be distributed, and Timewindow_end represents the end of the time window of the information to be distributed. Stime_end represents the information sink of the information to be distributed);

S12, randomly generate the Task_start of each information to be distributed, and judge whether it needs to be forwarded, if necessary, turn to S13, otherwise, record Task_start in the node path table for information distribution and transfer, turn to S14; wherein, the node path table is used for Record the sequence of nodes passed through in the distribution of information;

S13, randomly generate the number of forwarding times and corresponding forwarding intermediate nodes, and record the number of forwarding intermediate nodes in the node path table for information distribution and transfer, and turn to S14;

S14, read the time window attributes of each information to be distributed and the node path table for information distribution and delivery, calculate the earliest sending time and latest sending time of each information to be distributed backwards, and randomly generate a Task_start within this time period, and then Calculate Stime_end forward and turn to S15;

S15, read the information sink attributes Task_end of n pieces of information to be distributed, and record Sflag, Task_start, Task_end, Stime_start, and Stime_end in the initial solution.

In some embodiments, step S2 can be performed in various ways, such as:

After the initial solution generation method is executed POPSIZE times, an initial population can be obtained, and then the initial population is selected by means of roulette. The crossover operation is performed on the selected population by a single-point crossover method, that is, a crossover point is randomly generated, and the parts of the two adjacent chromosomes in the current population that are coded behind this point are exchanged in turn to generate two new chromosomes . Then, according to the mutation probability, the Sflag in the chromosomal gene is mutated in a 0-1 mutation manner. Arrange the mutated population in descending order of the fitness function value, take out the first SonNum chromosomes, and arrange the parent population in ascending order of the fitness function value, take out the last FatherNum chromosomes, and combine the chromosomes of these two parts into a new generation population. Repeat the above operations until the maximum number of iterations is exceeded and the optimal solution is output.

Referring to Figure 4, the above-mentioned method can be specifically carried out according to the following steps:

S21, an initial population can be obtained after performing the initial solution generation method for a preset number of times; record the initial population as POP(1), initialize t=1, and turn to S22;

S22, calculate its fitness function for each chromosome pop _ε (t) in the population POP(t) where D is a maximum value, is the objective function value, turn to S23;

S23, judging whether the termination condition t>=MAX_ITERATION is satisfied; wherein, MAX_ITERATION represents the maximum number of iterations), if not satisfied, execute S24, otherwise turn to S29;

S24, using the roulette method to select POPSIZE chromosomes from the t generation population POP(t), thereby generating a new population NEWPOP(t), recording the best solution at this time, and turning to S25;

S25, perform a single-point crossover operation on the chromosomes in the t-th generation population NEWPOP(t), that is, randomly generate a crossover point, and sequentially exchange the parts of two adjacent chromosomes in the population behind this point to generate two new Chromosome, record the best solution at this time, transfer to STEP 6;

S26, use 0-1 mutation for the chromosome genes in the t-th generation population NEWPOP(t), that is, to mutate whether the task is executed (Sflag), given a mutation probability Pm, generate a random value in [0,1] number, if the random number is less than the mutation probability, then the gene will be mutated, otherwise, the gene will not be mutated, and the best solution at this time will be recorded, and then go to S27;

S27, perform constraint verification on the t-th generation population NEWPOP(t), that is, judge the feasibility of the solution, mainly including time window constraints, delay constraints, bandwidth constraints, and source constraints. When the chromosome does not satisfy any of the constraints, add a large integer to the objective function value as a penalty to make its fitness function value smaller, and will be eliminated during the selection operation, and turn to S28;

S28, update the t generation population NEWPOP(t), that is, arrange the population in descending order of fitness function value, take out the first SonNum chromosomes, and at the same time arrange the parent population in ascending order of fitness function value, take out After FatherNum chromosomes, these two parts of chromosomes will form a new generation of population, transfer to S29

S29, update the t generation mutant population NEWPOP(t) to form a new population, POP(t+1), let t=t+1, turn to S22;

S210, outputting an optimal solution, and the algorithm is terminated.

Further, the above-mentioned step S24 may specifically include:

Step S241, through the formula Calculate the inheritance probability of the epsilon chromosome pop _ε (t) in the t generation population POP(t) to the next generation

Step S242, through the formula Calculate the cumulative probability of the epsilon chromosome pop _ε (t) in the t generation population POP(t)

Step S243, using a random function to generate a random number r between [0,1], and judging the cumulative probability with r, if Then the εth chromosome pop _ε (t) is selected.

In the embodiment of the present invention, through the established unmanned-manned cooperative information distribution and transmission optimization model, the distribution and transmission scheme is formulated from the perspective of minimizing the total time of information distribution and transmission, which improves the efficiency of information distribution and transmission, and obtains the information distribution and transmission scheme quickly and conveniently .

In addition, the embodiment of the present invention combines the application background of the problem to design a structure to solve the problem more intuitively and facilitate people's understanding. Compared with traditional 0-1 coding and real number coding, it can better meet the needs of solving the problem.

In addition, the embodiment of the present invention designs the generation method of the initial solution according to the process of unmanned-human cooperative information distribution and transmission, which greatly improves the feasibility of the initial solution, helps to reduce the number of iterations of the genetic algorithm, reduces the running time of the program, and quickly obtains the solution the result of.

In addition, the embodiment of the present invention uses a genetic algorithm to solve the problem. The genetic algorithm is a method of searching for an optimal solution by simulating a natural evolution process, and has the advantages of high search efficiency, global optimization capability, and good robustness. , can help us quickly search for the optimal solution.

In a second aspect, the present invention provides an unmanned-manned collaborative information distribution delivery optimization system, see Figure 5, including:

An initial solution generating module 51, configured to encode each piece of information to be distributed according to a preset coding method and attributes of each piece of information to be distributed, to obtain an initial solution corresponding to each piece of information to be distributed;

The optimal solution generation module 52 is used to use the obtained initial solutions as the initial population, and use the genetic algorithm to solve the pre-set unmanned-manned collaborative information distribution and transmission optimization model, so as to obtain the optimal solution;

The output module 53 is configured to output the solution corresponding to the optimal solution as the optimal solution of the unmanned-manned collaborative information distribution delivery optimization problem.

Further, the objective function of the unmanned-manned cooperative information distribution delivery optimization model is:

The constraints are:

ET _t ≤ l _t ,t∈T;

ET _t ≥ _{e t} , t∈T;

ET _t -ST _{t ≤} D, t ∈ T;

Among them, E={<i,j>|i,j∈V,i≠j} represents the set of directed edges, where <i,j> represents the directed edge from node i to node j in the communication network topology;

W={w _ij |i,j∈V} represents the weight set of each directed edge in the graph, where w _ij represents the Euclidean distance between node i and node j;

B _v represents the maximum amount of data that node v can provide, where v represents any node in the communication network topology, v∈V;

T={1,2,...,n} represents the information set to be distributed, n represents the number of elements in the set, t represents any information to be distributed, t∈T;

[e _t , l _t ] means that the information t to be distributed needs to arrive at the information sink within this time window, e _t means the earliest arrival time, l _t means the latest arrival time;

ST _t represents the time when the information to be distributed t actually starts to be distributed from the information source, and ET _t represents the time when the information to be distributed t actually arrives at the information sink;

SN _t represents the actual information source of the information t to be distributed, and EN _t represents the information sink that needs to receive the information t to be distributed;

Indicates the transmission delay of information t to be distributed from node i to node j;

Indicates the propagation delay of information t to be distributed from node i to node j;

D represents the maximum acceptable delay in the communication network topology;

TW _t represents the bandwidth required for information t to be distributed;

NW _ij represents the maximum bandwidth that the directed edge <i,j> in the communication network topology can bear;

Decision variables defined as:

Further, the initial solution generation module 51 is specifically configured to execute:

S10, the number n of the information to be distributed is used as the number of genes in the chromosome in the genetic algorithm, and the genes are coded in the form of quintuples, as shown in the following formula;

Gene=(Sflag, Task_start, Task_end, Stime_start, Stime_end)

Among them, Sflag indicates whether the information to be distributed is distributed, Task_start indicates the information source of the information to be distributed, Task_end indicates the information sink of the information to be distributed, Stime_start indicates the actual distribution time of the information to be distributed from the information source, and Stime_end indicates the actual arrival information of the information to be distributed time of lodging;

S11, read the attributes of n pieces of information to be distributed from the attribute table of the information to be distributed, if all the information to be distributed can be distributed, that is, the value of Sflag of n genes in the chromosome is 1; in the attribute table of the information to be distributed The elements include Task_id, Datavolume, Timewindow_start, Timewindow_end, and Stime_end, where Task_id represents the serial number of the information to be distributed, Datavolume represents the data volume of the information to be distributed, Timewindow_start represents the starting point of the time window of the information to be distributed, and Timewindow_end represents the end of the time window of the information to be distributed. Stime_end represents the information sink of the information to be distributed);

S12, randomly generate the Task_start of each information to be distributed, and judge whether it needs to be forwarded, if necessary, turn to S13, otherwise, record Task_start in the node path table for information distribution, and turn to S14; wherein, the node path table is used for Record the sequence of nodes passed through in the distribution of information;

S13, randomly generate the number of forwarding times and corresponding forwarding intermediate nodes, and record the number of forwarding intermediate nodes in the node path table for information distribution and transfer, and turn to S14;

S14, read the time window attributes of each information to be distributed and the node path table for information distribution and transmission, calculate the earliest sending time and latest sending time of each information to be distributed, and randomly generate a Task_start within this time period, and then push forward Calculate Stime_end, turn to S15;

S15, read the information sink attributes Task_end of n pieces of information to be distributed, and record Sflag, Task_start, Task_end, Stime_start, and Stime_end in the initial solution.

Further, the optimal solution generation module 52 is specifically used to execute:

S21, an initial population can be obtained after performing the initial solution generation method for a preset number of times; record the initial population as POP(1), initialize t=1, and turn to S22;

S22, calculate its fitness function for each chromosome pop _ε (t) in the population POP(t) where D is a maximum value, is the objective function value, turn to S23;

S23, judging whether the termination condition t>=MAX_ITERATION is satisfied; wherein, MAX_ITERATION represents the maximum number of iterations), if not satisfied, execute S24, otherwise turn to S29;

S24, using the roulette method to select POPSIZE chromosomes from the t generation population POP(t), thereby generating a new population NEWPOP(t), recording the best solution at this time, and turning to S25;

S25, perform a single-point crossover operation on the chromosomes in the t-th generation population NEWPOP(t), that is, randomly generate a crossover point, and sequentially exchange the parts of two adjacent chromosomes in the population behind this point to generate two new Chromosome, record the best solution at this time, transfer to STEP 6;

S26, use 0-1 mutation for the chromosome genes in the t-th generation population NEWPOP(t), that is, to mutate whether the task is executed (Sflag), given a mutation probability Pm, generate a random value in [0,1] number, if the random number is less than the mutation probability, then the gene will be mutated, otherwise, the gene will not be mutated, and the best solution at this time will be recorded, and then go to S27;

S27, perform constraint verification on the t-th generation population NEWPOP(t), that is, judge the feasibility of the solution, mainly including time window constraints, delay constraints, bandwidth constraints, and source constraints; when the chromosome does not satisfy any of them Constraints, then add a large integer to the objective function value as a penalty to make the fitness function value smaller, and will be eliminated when selecting an operation, go to S28;

S28, update the t generation population NEWPOP(t), that is, arrange the population in descending order of fitness function value, take out the first SonNum chromosomes, and at the same time arrange the parent population in ascending order of fitness function value, take out After the FatherNum chromosomes, the two parts of the chromosomes are combined to form a new generation population, and transferred to S29;

S29, update the t generation mutant population NEWPOP(t) to form a new population, POP(t+1), let t=t+1, turn to S22;

S210, outputting an optimal solution.

Further, the step S24 includes:

Step S241, through the formula Calculate the inheritance probability of the epsilon chromosome pop _ε (t) in the t generation population POP(t) to the next generation

Step S242, through the formula Calculate the cumulative probability of the epsilon chromosome pop _ε (t) in the t generation population POP(t)

Step S243, using a random function to generate a random number r between [0,1], and judging the cumulative probability with r, if Then the εth chromosome pop _ε (t) is selected.

It is not difficult to understand that, since the unmanned-manned collaborative information distribution and delivery optimization system introduced in the second aspect above is a system that can implement the unmanned-manned collaborative information distribution and delivery optimization method in the embodiment of the present invention, it is based on the present invention For the unmanned-manned collaborative information distribution and delivery optimization method introduced in the embodiment, those skilled in the art can understand the specific implementation of the unmanned-manned collaborative information distribution and delivery optimization system of this embodiment and its various variations. Therefore, how the unmanned-manned coordinated information distribution and delivery optimization system implements the unmanned-manned coordinated information distribution and delivery optimization method in the embodiment of the present invention will not be described in detail here. As long as a person skilled in the art implements the system adopted by the method for unmanned-human collaborative information distribution delivery optimization in the embodiment of the present invention, it all falls within the scope of protection intended by this application.

Through the above description of the implementation manners, those skilled in the art can clearly understand that each implementation manner can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic discs, optical discs, etc., including several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) execute the methods described in various embodiments or some parts of the embodiments.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure the understanding of this description.

Similarly, it should be appreciated that in the above description of exemplary embodiments of the invention, in order to streamline the disclosure and to facilitate understanding of one or more of the various inventive aspects, various features of the invention are sometimes grouped together into a single embodiment, figure , or in its description. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art can understand that the modules in the device in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment. Modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore may be divided into a plurality of sub-modules or sub-units or sub-assemblies. All features disclosed in this specification (including accompanying claims, abstract and drawings) and any method or method so disclosed may be used in any combination, except that at least some of such features and/or processes or units are mutually exclusive. All processes or units of equipment are combined. Each feature disclosed in this specification (including accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will understand that although some embodiments herein include some features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention. And form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means can be embodied by one and the same item of hardware. The use of the words first, second, and third, etc. does not indicate any order. These words can be interpreted as names.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent replacements are made to some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims (10)

1. A kind of unmanned-people cooperative information distribution transfer optimization method, it is characterized in that, comprises: Step 1. Encode each piece of information to be distributed according to the preset coding method and the attribute of each piece of information to be distributed, and obtain an initial solution corresponding to each piece of information to be distributed; Step 2. Use the obtained initial solutions as the initial population, and use the genetic algorithm to solve the pre-set unmanned-manned collaborative information distribution and transmission optimization model, so as to obtain the optimal solution; Step 3. Output the solution corresponding to the optimal solution as the optimal solution of the unmanned-manned collaborative information distribution delivery optimization problem. 2. according to claim 1 described unmanned-manned cooperative information distribution delivery optimization method, it is characterized in that, the objective function of described unmanned-manned cooperative information distribution delivery optimization model is: $\mathrm{<span\; class="notranslate">\; min</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; T</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; V</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; ct</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; ft</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span>$ The constraints are: ${\mathrm{<span\; class="notranslate">\; ET</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; ST</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; V</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; ct</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; ft</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; ;</span>$ ET _t ≤ l _t ,t∈T; ET _t ≥ _{e t} , t∈T; ET _t -ST _{t ≤} D, t ∈ T; $\underset{\mathrm{<span\; class="notranslate">\; t</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; NW</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; ;</span>$ $\underset{\mathrm{<span\; class="notranslate">\; j</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}\underset{\mathrm{<span\; class="notranslate">\; t</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; TW</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; ;</span>$ $\underset{\mathrm{<span\; class="notranslate">\; j</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; ;</span>$ $\underset{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{{\mathrm{<span\; class="notranslate">\; iEN</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; ;</span>$ $\underset{\mathrm{<span\; class="notranslate">\; j</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; ;</span>$ ${\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ;</span>$ Among them, E={<i,j>|i,j∈V,i≠j} represents the set of directed edges, where <i,j> represents the directed edge from node i to node j in the communication network topology; W={w _ij |i,j∈V} represents the weight set of each directed edge in the graph, where w _ij represents the Euclidean distance between node i and node j; B _v represents the maximum amount of data that node v can provide, where v represents any node in the communication network topology, v∈V; T={1,2,...,n} represents the information set to be distributed, n represents the number of elements in the set, t represents any information to be distributed, t∈T; [e _t , l _t ] means that the information t to be distributed needs to arrive at the information sink within this time window, e _t means the earliest arrival time, l _t means the latest arrival time; ST _t represents the time when the information to be distributed t actually starts to be distributed from the information source, and ET _t represents the time when the information to be distributed t actually arrives at the information sink; SN _t represents the actual information source of the information t to be distributed, and EN _t represents the information sink that needs to receive the information t to be distributed; Indicates the transmission delay of information t to be distributed from node i to node j; Indicates the propagation delay of information t to be distributed from node i to node j; D represents the maximum acceptable delay in the communication network topology; TW _t represents the bandwidth required for information t to be distributed; NW _ij represents the maximum bandwidth that the directed edge <i,j> in the communication network topology can bear; Decision variables defined as: 3. The unmanned-manned collaborative information distribution delivery optimization method according to claim 1, characterized in that, said step 1 comprises: S10, the number n of the information to be distributed is used as the number of genes in the chromosome in the genetic algorithm, and the genes are coded in the form of quintuples, as shown in the following formula; Gene=(Sflag, Task_start, Task_end, Stime_start, Stime_end) Among them, Sflag indicates whether the information to be distributed is distributed, Task_start indicates the information source of the information to be distributed, Task_end indicates the information sink of the information to be distributed, Stime_start indicates the actual distribution time of the information to be distributed from the information source, and Stime_end indicates the actual arrival information of the information to be distributed time of lodging; S11, read the attributes of n pieces of information to be distributed from the attribute table of the information to be distributed, if all the information to be distributed can be distributed, that is, the value of Sflag of n genes in the chromosome is 1; in the attribute table of the information to be distributed The elements include Task_id, Datavolume, Timewindow_start, Timewindow_end, and Stime_end, where Task_id represents the serial number of the information to be distributed, Datavolume represents the data volume of the information to be distributed, Timewindow_start represents the starting point of the time window of the information to be distributed, and Timewindow_end represents the end of the time window of the information to be distributed. Stime_end represents the information sink of the information to be distributed); S12, randomly generate the Task_start of each information to be distributed, and judge whether it needs to be forwarded, if necessary, turn to S13, otherwise, record Task_start in the node path table for information distribution, and turn to S14; wherein, the node path table is used for Record the sequence of nodes passed through in the distribution of information; S13, randomly generate the number of forwarding times and corresponding forwarding intermediate nodes, and record the number of forwarding intermediate nodes in the node path table for information distribution and transfer, and turn to S14; S14, read the time window attributes of each information to be distributed and the node path table for information distribution and transmission, calculate the earliest sending time and latest sending time of each information to be distributed, and randomly generate a Task_start within this time period, and then push forward Calculate Stime_end, turn to S15; S15, read the information sink attributes Task_end of n pieces of information to be distributed, and record Sflag, Task_start, Task_end, Stime_start, and Stime_end in the initial solution. 4. The unmanned-manned collaborative information distribution delivery optimization method according to claim 1, characterized in that, said step 2 comprises: S21, record the initial population as POP(1), and initialize t=1, turn to S22; S22, calculate its fitness function for each chromosome pop _ε (t) in the population POP(t) where D is a maximum value, is the objective function value, turn to S23; S23, judging whether the termination condition t>=MAX_ITERATION is satisfied; wherein, MAX_ITERATION represents the maximum number of iterations), if not satisfied, execute S24, otherwise turn to S29; S24, using the roulette method to select POPSIZE chromosomes from the t generation population POP(t), thereby generating a new population NEWPOP(t), recording the best solution at this time, and turning to S25; S25, perform a single-point crossover operation on the chromosomes in the t-th generation population NEWPOP(t), that is, randomly generate a crossover point, and sequentially exchange the parts of two adjacent chromosomes in the population behind this point to generate two new Chromosome, record the best solution at this time, transfer to STEP 6; S26, use 0-1 mutation for the chromosome genes in the t-th generation population NEWPOP(t), that is, to mutate whether the task is executed (Sflag), given a mutation probability Pm, generate a random value in [0,1] number, if the random number is less than the mutation probability, then the gene will be mutated, otherwise, the gene will not be mutated, and the best solution at this time will be recorded, and then go to S27; S27, perform constraint verification on the t-th generation population NEWPOP(t), that is, judge the feasibility of the solution, mainly including time window constraints, delay constraints, bandwidth constraints, and source constraints; when the chromosome does not satisfy any of them Constraints, then add a large integer to the objective function value as a penalty to make the fitness function value smaller, and will be eliminated when selecting an operation, go to S28; S28, update the t generation population NEWPOP(t), that is, arrange the population in descending order of fitness function value, take out the first SonNum chromosomes, and at the same time arrange the parent population in ascending order of fitness function value, take out After the FatherNum chromosomes, the two parts of the chromosomes are combined to form a new generation population, and transferred to S29; S29, update the t generation mutant population NEWPOP(t) to form a new population, POP(t+1), let t=t+1, turn to S22; S210, outputting an optimal solution. 5. The unmanned-manned collaborative information distribution delivery optimization method according to claim 4, characterized in that, said step S24 comprises: Step S241, through the formula Calculate the inheritance probability of the epsilon chromosome pop _ε (t) in the t generation population POP(t) to the next generation Step S242, through the formula Calculate the cumulative probability of the epsilon chromosome pop _ε (t) in the t generation population POP(t) Step S243, using a random function to generate a random number r between [0,1], and judging the cumulative probability with r, if Then the εth chromosome pop _ε (t) is selected. 6. An unmanned-manned cooperative information distribution delivery optimization system, characterized in that it comprises: An initial solution generation module, configured to encode each piece of information to be distributed according to a preset coding method and attributes of each piece of information to be distributed, to obtain an initial solution corresponding to each piece of information to be distributed; The optimal solution generation module is used to use the obtained initial solutions as the initial population, and use the genetic algorithm to solve the pre-set unmanned-manned collaborative information distribution and transmission optimization model, so as to obtain the optimal solution; An output module, configured to output the solution corresponding to the optimal solution as the optimal solution of the unmanned-manned collaborative information distribution delivery optimization problem. 7. According to the described unmanned-manned cooperative information distribution delivery optimization system of claim 6, it is characterized in that, the objective function of the described unmanned-human cooperative information distribution delivery optimization model is: $\mathrm{<span\; class="notranslate">\; min</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; T</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; V</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; ct</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; ft</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span>$ The constraints are: ${\mathrm{<span\; class="notranslate">\; ET</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; ST</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; V</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; ct</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; ft</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; ;</span>$ ET _t ≤ l _t ,t∈T; ET _t ≥ _{e t} , t∈T; ET _t -ST _{t ≤} D, t ∈ T; $\underset{\mathrm{<span\; class="notranslate">\; t</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; NW</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; ;</span>$ $\underset{\mathrm{<span\; class="notranslate">\; j</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}\underset{\mathrm{<span\; class="notranslate">\; t</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; TW</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; ;</span>$ $\underset{\mathrm{<span\; class="notranslate">\; j</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; ;</span>$ $\underset{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{{\mathrm{<span\; class="notranslate">\; iEN</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; ;</span>$ $\underset{\mathrm{<span\; class="notranslate">\; j</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; ;</span>$ ${\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ;</span>$ Among them, E={<i,j>|i,j∈V,i≠j} represents the set of directed edges, where <i,j> represents the directed edge from node i to node j in the communication network topology; W={w _ij |i,j∈V} represents the weight set of each directed edge in the graph, where w _ij represents the Euclidean distance between node i and node j; B _v represents the maximum amount of data that node v can provide, where v represents any node in the communication network topology, v∈V; T={1,2,...,n} represents the information set to be distributed, n represents the number of elements in the set, t represents any information to be distributed, t∈T; [e _t , l _t ] means that the information t to be distributed needs to arrive at the information sink within this time window, e _t means the earliest arrival time, l _t means the latest arrival time; ST _t represents the time when the information to be distributed t actually starts to be distributed from the information source, and ET _t represents the time when the information to be distributed t actually arrives at the information sink; SN _t represents the actual information source of the information t to be distributed, and EN _t represents the information sink that needs to receive the information t to be distributed; Indicates the transmission delay of information t to be distributed from node i to node j; Indicates the propagation delay of information t to be distributed from node i to node j; D represents the maximum acceptable delay in the communication network topology; TW _t represents the bandwidth required for information t to be distributed; NW _ij represents the maximum bandwidth that the directed edge <i,j> in the communication network topology can bear; Decision variables defined as: 8. The unmanned-manned collaborative information distribution delivery optimization system according to claim 6, characterized in that, the initial solution generation module is specifically used to execute: S10, the number n of the information to be distributed is used as the number of genes in the chromosome in the genetic algorithm, and the genes are coded in the form of quintuples, as shown in the following formula; Gene=(Sflag, Task_start, Task_end, Stime_start, Stime_end) Among them, Sflag indicates whether the information to be distributed is distributed, Task_start indicates the information source of the information to be distributed, Task_end indicates the information sink of the information to be distributed, Stime_start indicates the actual distribution time of the information to be distributed from the information source, and Stime_end indicates the actual arrival information of the information to be distributed time of lodging; S11, read the attributes of n pieces of information to be distributed from the attribute table of the information to be distributed, if all the information to be distributed can be distributed, that is, the value of Sflag of n genes in the chromosome is 1; in the attribute table of the information to be distributed The elements include Task_id, Datavolume, Timewindow_start, Timewindow_end, and Stime_end, where Task_id represents the serial number of the information to be distributed, Datavolume represents the data volume of the information to be distributed, Timewindow_start represents the starting point of the time window of the information to be distributed, and Timewindow_end represents the end of the time window of the information to be distributed. Stime_end represents the information sink of the information to be distributed); S12, randomly generate the Task_start of each information to be distributed, and judge whether it needs to be forwarded, if necessary, turn to S13, otherwise, record Task_start in the node path table for information distribution, and turn to S14; wherein, the node path table is used for Record the sequence of nodes passed through in the distribution of information; S13, randomly generate the number of forwarding times and corresponding forwarding intermediate nodes, and record the number of forwarding intermediate nodes in the node path table for information distribution and transfer, and turn to S14; S14, read the time window attributes of each information to be distributed and the node path table for information distribution and transmission, calculate the earliest sending time and latest sending time of each information to be distributed, and randomly generate a Task_start within this time period, and then push forward Calculate Stime_end, turn to S15; S15, read the information sink attributes Task_end of n pieces of information to be distributed, and record Sflag, Task_start, Task_end, Stime_start, and Stime_end in the initial solution. 9. The unmanned-manned cooperative information distribution delivery optimization system according to claim 6, characterized in that, the optimal solution generation module is specifically used to execute: S21, record the initial population as POP(1), and initialize t=1, turn to S22; S22, calculate its fitness function for each chromosome pop _ε (t) in the population POP(t) where D is a maximum value, is the objective function value, turn to S23; S23, judging whether the termination condition t>=MAX_ITERATION is satisfied; wherein, MAX_ITERATION represents the maximum number of iterations), if not satisfied, execute S24, otherwise turn to S29; S24, using the roulette method to select POPSIZE chromosomes from the t generation population POP(t), thereby generating a new population NEWPOP(t), recording the best solution at this time, and turning to S25; S25, perform a single-point crossover operation on the chromosomes in the t-th generation population NEWPOP(t), that is, randomly generate a crossover point, and sequentially exchange the parts of two adjacent chromosomes in the population behind this point to generate two new Chromosome, record the best solution at this time, transfer to STEP 6; S26, use 0-1 mutation for the chromosome genes in the t-th generation population NEWPOP(t), that is, to mutate whether the task is executed (Sflag), given a mutation probability Pm, generate a random value in [0,1] number, if the random number is less than the mutation probability, then the gene will be mutated, otherwise, the gene will not be mutated, and the best solution at this time will be recorded, and then go to S27; S27, perform constraint verification on the t-th generation population NEWPOP(t), that is, judge the feasibility of the solution, mainly including time window constraints, delay constraints, bandwidth constraints, and source constraints; when the chromosome does not satisfy any of them Constraints, then add a large integer to the objective function value as a penalty to make the fitness function value smaller, and will be eliminated when selecting an operation, go to S28; S28, update the t generation population NEWPOP(t), that is, arrange the population in descending order of fitness function value, take out the first SonNum chromosomes, and at the same time arrange the parent population in ascending order of fitness function value, take out After the FatherNum chromosomes, the two parts of the chromosomes are combined to form a new generation population, and transferred to S29; S29, update the t generation mutant population NEWPOP(t) to form a new population, POP(t+1), let t=t+1, turn to S22; S210, outputting an optimal solution. 10. The unmanned-manned collaborative information distribution delivery optimization system according to claim 9, characterized in that, the step S24 comprises: Step S241, through the formula Calculate the inheritance probability of the epsilon chromosome pop _ε (t) in the t generation population POP(t) to the next generation Step S242, through the formula Calculate the cumulative probability of the epsilon chromosome pop _ε (t) in the t generation population POP(t) Step S243, using a random function to generate a random number r between [0,1], and judging the cumulative probability with r, if Then the εth chromosome pop _ε (t) is selected.