Mobile Ad Hoc Network Multipath Routing Method, Device, Storage Medium and Communication Method

Abstract

The present invention discloses a mobile ad hoc network multi-path routing method, device, storage medium and communication method, which relates to the field of mobile communication and is used to improve at least one of the calculation efficiency, accuracy, objectivity and optimal solution diversity of multi-path routing. The present invention includes: constructing a multi-objective optimization model for path routing, where the multi-objective optimization model takes the indicators characterizing the path service quality as the optimization objectives; solving the multi-objective optimization model to obtain a non-dominated solution set; and then determining the multi-path routing according to the non-dominated solution set. The present invention improves the objectivity, accuracy, calculation efficiency and diversity of the optimal solution set of multi-path routing, as well as improves the path routing service quality of the satellite Internet and mobile ad hoc network integrated communication, and improves the overall link transmission efficiency and resource utilization rate.

Description

Technical Field

[0001] The present invention relates to the field of mobile communications, and in particular, to a multi-path routing method, device, storage medium, and communication method for mobile ad hoc networks. Background Art

[0002] In scenarios where a large number of ad hoc network devices cooperate, it is necessary to construct multi-path routing to improve the QoS (Quality of Service) communication quality of the overall Internet transmission link, and to improve the link transmission efficiency and resource utilization rate. Compared with the single-path routing method, multi-path routing not only increases the transmission bandwidth, but also provides multiple paths for the same type of service, and can achieve higher service quality.

[0003] The multi-path routing problem is mostly solved by transforming it into a multi-objective optimization problem and then using algorithms such as particle swarm optimization, ant colony optimization, and genetic algorithm. These methods have problems such as long iteration time, low algorithm efficiency, non-convergence of the algorithm, and lack of diversity in the optimal solution set. In addition, the process of solution mainly depends on the prior knowledge and experience of researchers in the selection of model parameters, and is seriously affected by subjectivity. Summary of the Invention

[0004] The object of the present invention is to provide a multi-path routing method, device, storage medium, and communication method for mobile ad hoc networks to improve at least one of the computational efficiency, accuracy, objectivity, and diversity of the optimal solution of multi-path routing for all or part of the above problems.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A multi-path routing method for mobile ad hoc networks, which includes:

[0007] Construct a multi-objective optimization model for path routing, where the multi-objective optimization model uses an index representing the quality of path service as the optimization objective; the multi-objective optimization model is represented in a stage description form;

[0008] Divide the multi-objective optimization model into multi-stage routing problems for solution to obtain a non-dominated solution set;

[0009] Determine the multi-path routing according to the non-dominated solution set.

[0010] The present invention also provides a communication method, in which a first terminal in a first subnet communicates with a second terminal in a second subnet, and the first subnet and the second subnet communicate through a satellite Internet; the above multi-path routing method for mobile ad hoc networks is used to determine the multi-path routing between the first terminal and the satellite Internet terminal in the first subnet.

[0011] The present invention also provides a storage medium storing a computer program, and running the computer program can execute the above-mentioned mobile ad hoc network multi-path routing method.

[0012] The present invention also provides a mobile ad hoc network multi-path routing device, including a processor and a storage medium. The storage medium stores a computer program, and when the processor runs the computer program in the storage medium, it executes the above-mentioned mobile ad hoc network multi-path routing method.

[0013] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows:

[0014] The present invention constructs a multi-objective optimization model for path routing with the index characterizing the path service quality as the optimization goal. This multi-objective optimization model does not require model parameters or coefficients, and will not cause different multi-objective optimization results due to different designers, avoiding the error terms introduced by the subjective thinking of the designers and improving the objectivity of multi-path routing. The present invention iteratively solves the non-dominated solution set of the multi-objective optimization model in stages, and derives the solution of the original problem using the solution of the phased routing problem, improving the accuracy of multi-path routing. In the process of solving the multi-objective optimization model in stages, the present invention uses a buffer mechanism to temporarily store the non-dominated solution set of the previous stage, reducing the repeated calculation process, improving the calculation efficiency of the method, and saving storage space. The present invention determines the multi-path routing based on the non-dominated solution set, making the optimal solution set of the multi-path routing diverse. The present invention improves the path routing service quality of the satellite Internet and mobile ad hoc network integrated communication, and improves the overall link transmission efficiency and resource utilization rate. Description of the Drawings

[0015] The present invention will be described by way of examples with reference to the drawings, where:

[0016] Figure 1 is a possible path routing diagram between the source node and the destination node provided by an embodiment of the present application.

[0017] Figure 2 is a flowchart of the mobile ad hoc network multi-path routing method provided by an embodiment of the present application.

[0018] Figure 3 is a flowchart of the single-stage routing problem solving method provided by an embodiment of the present application.

[0019] Figure 4 is a flowchart of the multi-objective optimization model solving method provided by an embodiment of the present application.

[0020] Figure 5 is a network diagram of the communication method provided by an embodiment of the present application. Detailed Embodiments

[0021] All features disclosed in this specification, or steps in all methods or processes disclosed, can be combined in any way, except for mutually exclusive features and / or steps.

[0022] Any feature disclosed in this specification (including any additional claims, abstract) can be replaced by other equivalent or similar-purpose alternative features, unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only an example of a series of equivalent or similar features.

[0023] As Figure 1 shown, for the multi-path routing requirement between a source node and a destination node, traditional methods either convert the multi-objective optimization problem into a single-objective optimization problem for separate solutions, or use methods such as particle swarm algorithm, ant colony algorithm, genetic algorithm, etc. to solve the multi-objective optimization problem. The former obtains the multi-objective optimization result by weighted summation of the single-objective optimization results, which faces problems such as difficult determination of weighting values, inconsistent dimensions of each objective, and low robustness; the methods adopted by the latter face problems such as long iteration time, low algorithm efficiency, strong influence of subjective factors, and lack of diversity in the optimal solution set. In view of these deficiencies, the embodiments of this application provide a multi-path routing method, device, storage medium, and communication method for mobile ad hoc network (MANET), which comprehensively consider the indicators reflecting the path quality for path planning, aiming to improve one or more of the objectivity, accuracy, and computational efficiency of multi-path routing.

[0024] In some embodiments, as Figure 2 shown, the mobile ad hoc network multi-path routing method includes the following steps:

[0025] S1. Construct a multi-objective optimization model for path routing, where the multi-objective optimization model takes the indicators characterizing the path service quality as the optimization objectives.

[0026] In a possible implementation manner, in this step S1, the indicators to be optimized in the constructed multi-objective optimization model are quantifiable indicators, which are used to measure the degree of satisfaction of service requirements.

[0027] In view of the service quality QoS access design characteristics of MANET, in some embodiments, a multi-objective optimization model is constructed by comprehensively considering two or more of the indicators reflecting the bandwidth situation, packet loss situation, and delay situation to characterize the path service quality.

[0028] Specifically, as a feasible implementation method, two or more indicators among bandwidth availability, delivery rate and load availability are selected as optimization targets of the multi-objective optimization model. Among them, bandwidth availability reflects interference and other conditions in the data transmission process; delivery rate reflects packet loss rate and other conditions in the data transmission process; load availability reflects delay and jitter and other conditions in the data transmission process. Through multi-objective optimization of higher transmission bandwidth, lower data packet loss rate and lower transmission delay, the service quality of the overall network transmission business can be improved.

[0029] In some embodiments, the index characterizing the service quality of the path is constructed based on the index characterizing the service quality of the corresponding nodes in the path, that is, the service quality of the path is calculated based on the service quality of the corresponding nodes on the path.

[0030] For the service quality index of the node, taking the bandwidth availability, delivery rate and load availability in the previous embodiment as an example, the characterization method is as follows:

[0031] 1) Node bandwidth availability :

[0032] Formula (1): ;

[0033] In the formula, Indicates the measurement period; For Node i Neighboring nodes j exist The NAV (Network Allocation Vector) value in is the DCF interframe space, For Node i The value of the backoff counter. Obviously, the objective function of this indicator does not require model parameters or coefficients and will not introduce the designer's subjective experience bias.

[0034] 2) Node delivery rate :

[0035] Formula (2): ;

[0036] Representation Node i To Node i+1 delivery rate.

[0037] 3) Node load availability :

[0038] Formula (3): ;

[0039] In the formula, Representation Node iThe length of the load queue, is the node i cache size.

[0040] Taking the above three indicators as the optimization objectives, the objective function of the constructed multi-objective optimization model is:

[0041] Formula (4): ;

[0042] In the formula, N represents the number of stages into which the path is divided, k represents the k stage. represents the available path bandwidth ratio, represents the path delivery ratio, represents the available path load ratio. This objective function represents the multi-objective optimization model in a phased form.

[0043] For the objective function, there are usually corresponding constraint conditions. For the multi-objective optimization model with the indicators representing the path service quality as the optimization objectives, in some embodiments, its constraint conditions include the constraint on the degree of deviation of the corresponding nodes on the path from the direction of the destination node.

[0044] For the degree of deviation of the node from the direction of the destination node, in some embodiments, it is characterized from two dimensions of the offset amount and the offset angle, that is, for the constraint conditions of the multi-objective optimization model, there are constraints in two dimensions of the offset amount and the offset angle.

[0045] Taking the example in the previous embodiment where the multi-objective optimization model is represented in a phased description form, in some embodiments, the above offset amount is the lateral offset amount of the current stage node deviating from the first baseline and the above offset angle is the lateral offset angle of the next stage node deviating from the second baseline . The first baseline is the connection line between the source node and the destination node, and the second baseline is the connection line between the current stage node and the destination node.

[0046] On the basis that the constraint objects have been determined, the constraint objects in two dimensions are constrained. In some embodiments, the constraint conditions for the lateral offset amount and the lateral offset angle are designed as:

[0047] Formula (5): ;

[0048] In the formula, is the lateral offset amount of the k stage node deviating from the first baseline ; is the minimum lateral offset amount, is the maximum lateral offset. is the lateral offset angle at the k stage, that is, the connection line k between the nodes at the k+1 stage and the nodes at the stage, k and the second baseline of the nodes at the stage, , and respectively represent the minimum and maximum values of this included angle; as a feasible way, set , . .

[0049] S2. Solve the multi-objective optimization model to obtain the non-dominated solution set.

[0050] In some embodiments, this step S2 is solved by using the non-dominated sorting dynamic programming method. Specifically, the multi-objective optimization model is divided into a multi-stage routing problem for solution. By decomposing the problem of solving the multi-objective optimization model into several overlapping sub-problems, iteratively solving and caching the solutions of each sub-problem, and using the solutions of the sub-problems to deduce the solution of the multi-objective optimization model. This method is essentially different from decomposing the original problem into single-objective optimization problems. It is a synchronous solution for multiple objectives rather than an independent solution, does not involve the weighting problem, and does not depend on the configuration of model parameters, ensuring the accuracy and objectivity of the solution result. And the solution result of each stage is cached for use in the next stage, without repeated calculation, improving the calculation efficiency of the algorithm, reusing the cached resources, and saving the storage space. And the number of divided stages, in some embodiments, is the same as the number of stages divided in the embodiment of establishing the objective function of the multi-objective optimization model in formula (4).

[0051] In some embodiments, the calculation of the non-dominated solution set of the multi-objective optimization model is solved by using the parameters constrained in the constraint conditions in the previous embodiments as variables.

[0052] Taking the constraint conditions proposed in the previous embodiment of formula (5) as an example, the constrained parameters are the lateral offset and the lateral offset angle, then these two parameters can be used as variables to solve the multi-objective optimization model.

[0053] As a feasible way, in the solution of each stage, the lateral offset of the current stage node deviating from the first baseline is used as the state variable, that is, is used as the state variable x; the lateral offset angle of the next stage node deviating from the second baseline is used as the control variable u, that is, is used as the control variable. The first baseline and the second baseline is defined in the corresponding embodiment above. It should be noted that each stage has several state variables x, and there are several control variables under each state variable x.

[0054] For the multi-objective optimization model after stage division, in some embodiments, the non-dominated solution set of the multi-objective optimization model can be iteratively solved from the last stage to the initial stage according to the divided multiple stages; in other embodiments, the non-dominated solution set of the multi-objective optimization model can be iteratively solved from the initial stage to the last stage according to the divided multiple stages. Regardless of the iteration order adopted, when solving each stage, it is based on the non-dominated solution set obtained by solving the previous stage, and the non-dominated solution set of the current stage is iteratively solved. In this way, the non-dominated solution set obtained by iterating to the last stage is the non-dominated solution set of the multi-objective optimization model.

[0055] In some embodiments, the method for solving the non-dominated solution set of the current stage based on the non-dominated solution set of the previous stage is to calculate the solution of the multi-objective optimization model of the current stage based on the non-dominated solution set obtained by solving the previous stage, and select the non-dominated solution set of the current stage through non-dominated sorting of the solution of the multi-objective optimization model of the current stage.

[0056] It should be noted that since each stage has several state variables, and there are several control variables under each state variable, therefore, the calculation of the non-dominated solution set of the current stage here is for each control variable respectively. That is, for each control variable (representing the k th stage, the i th state variable, and the j th control variable), the corresponding non-dominated solution needs to be solved and then put into the non-dominated solution set. And each stage also needs to traverse each control variable. As Figure 3 shown is the flowchart for solving the non-dominated solution set of a single stage in this embodiment.

[0057] As a feasible implementation manner, the solution of the multi-objective optimization model of the current stage is obtained by adding the instantaneous objective solution of the current stage to the non-dominated solution set obtained by solving the previous stage. The so-called instantaneous objective solution is the value of each optimization objective obtained under the action of the variables of the current stage.

[0058] Taking the solution calculation starting from the last stage as an example, taking ) to represent the k th stage, the i th state variable and the j th control variable under the action of the instantaneous objective solution, and taking Denote the non - dominated solution set of the previous stage (i.e., the (k + 1)-th stage), then the solutions corresponding to the multi - objective optimization model of the current stage are represented as and are denoted as . By traversing each state variable k of the current (the ) stage, and the control variable under each state variable , all the instantaneous objective solutions of the current stage can be calculated ).

[0059] In some embodiments, when selecting the non - dominated solution set of the current stage based on the non - dominated sorting of the solutions of the multi - objective optimization model of the current stage, for each state variable , at most a set maximum number of solutions are selected.

[0060] In some specific embodiments, the method for selecting the non - dominated solution set of the current stage based on the non - dominated sorting of the solutions of the multi - objective optimization model of the current stage includes:

[0061] Perform non - dominated sorting on the solutions of the multi - objective optimization model under each state variable of the current stage respectively, and select the non - dominated solution set under each state variable;

[0062] If the number of solutions in the selected non - dominated solution set exceeds the set maximum number, then screen out the set maximum number of solutions from the selected non - dominated solution set.

[0063] For example, assume that the set maximum number is pareto - num, that is, the number of non - dominated solutions limited for each state variable. For the state variable , by traversing each of its control variables , the set of solutions of the multi - objective optimization model under the state variable is obtained, which is denoted as ) here. Perform non - dominated sorting on ) and select the non - dominated solution set ). If the number of solutions in ) is greater than pareto - num, then select pareto - num non - dominated solutions from it as ), and this ) is the non - dominated solution set retained when the k -th stage iterates to the state variable .

[0064] As a feasible implementation for screening out pareto - num non - dominated solutions from the ) obtained after non - dominated sorting, when performing non - dominated solution screening, it is carried out in the order of the crowding degree of each non - dominated solution from large to small. That is, if ) If the number of non-dominated solutions is greater than pareto-num, calculate the crowding degree of each non-dominated solution among them, and select pareto-num non-dominated solutions in descending order of crowding degree as the retained ).

[0065] Traverse each state variable of the k stage through the same method , and respectively obtain the corresponding retained non-dominated solution sets ), that is, complete the solution of the multi-objective optimization model in the k stage. Repeat this solution process for each stage, and the non-dominated solution set of the entire multi-objective optimization model can be obtained.

[0066] As an executable embodiment of a computer, refer to Figure 4 , step S2 includes the following processes:

[0067] S2.1. Set the lateral offset angle as the control variable , and set the lateral offset L as the state variable .

[0068] S2.2. Set the maximum number pareto-num of non-dominated solutions in the retained non-dominated solution set; divide the solution problem of the multi-objective optimization model into N stages, that is , each stage has m state variables, that is, for each stage k, there is , and each state variable corresponds to n control variables, that is, for each , there is . Let k = N.

[0069] S2.3. Calculate the instantaneous objective solution generated under ), that is, the values of each optimization objective under the control variable k at stage and state .

[0070] S2.4. Calculate the cumulative multi-objective optimization model solution under the control variable .

[0071] S2.5. For state , traverse each , that is , execute steps S2.3 - S2.4, and respectively obtain under each control variable , and put them into the multi-objective optimization model solution set ) among them.

[0072] S2.6. Perform non-dominated sorting on ) and select the non-dominated solution set ); If ) the number of non-dominated solutions is greater than pareto-num, calculate the crowding degree of each non-dominated solution, and select pareto-num non-dominated solutions as the retained ) according to the order of the crowding degree from large to small.

[0073] S2.7. For the k stage, traverse each , that is , each repeats steps S2.3 - S2.6, that is, the non-dominated solution set k of the stage is obtained. Thus, the solution calculation of the non-dominated solution set for a single stage is completed. Let k = k - 1.

[0074] S2.8. Repeat steps S2.3 to S2.7 until k = 1. After execution, the non-dominated solution set of the entire multi-objective optimization model is obtained.

[0075] If iterating from the initial stage to the final stage, similarly, adaptively modify the parameters related to the stage and the relationship between the previous and subsequent stages in steps S2.2, S2.4, S2.7, and S2.8.

[0076] S3. Determine the multi-path routing according to the non-dominated solution set.

[0077] Determine the multi-path routing according to the non-dominated solution set, so that the optimal solution set of the multi-path routing has the characteristic of diversity.

[0078] The number of path routings corresponding to the non-dominated solution set calculated in step S2 may not be exactly the same as the required number. If the number of path routings exceeds the requirement, the path routings need to be screened. In some embodiments, when the number of path routings indicated by the non-dominated solution set calculated in step S2 exceeds a predetermined value, screen out the path routings with the number of the predetermined value.

[0079] As a feasible implementation manner, when the number of path routings indicated by the non-dominated solution set exceeds a predetermined value, sort all the path routings according to the set rules, or sort each path routing by calculating the crowding degree of each path routing, and screen out the top predetermined number of path routings.

[0080] The embodiment of the present application also provides a storage medium, which stores a computer program, and running the computer program can execute the mobile ad hoc network multi-path routing method of the above embodiment.

[0081] In addition, an embodiment of the present application further provides a mobile ad-hoc network multi-path routing device, which includes a processor and a storage medium. A computer program is stored in the storage medium. When the processor runs the computer program in the storage medium, it executes the mobile ad-hoc network multi-path routing method of the above embodiment.

[0082] The solution provided by the present application can be applied to the mobile Internet communication scenario, or can also be applied to the communication scenario of the integration of satellite Internet and mobile Internet.

[0083] For the communication scenario of the integration of satellite Internet and mobile Internet, the present application further provides a communication method. In this method, as Figure 5 shown, a first terminal in a first subnet communicates with a second terminal in a second subnet, and the first subnet and the second subnet communicate through the satellite Internet. The mobile ad-hoc network multi-path routing method of the above embodiment is used to determine the multi-path routing between the first terminal and the first satellite Internet terminal in the first subnet. This embodiment improves the path routing service quality of the integration communication of satellite Internet and mobile ad-hoc network, and improves the overall link transmission efficiency and resource utilization rate.

[0084] As a possible implementation manner, the first terminal transmits data to the second terminal. At this time, in the first subnet, the first terminal is the source node, and the first satellite Internet terminal is the destination node. The mobile ad-hoc network multi-path routing method of the above embodiment is used to determine the multi-path routing between the first terminal and the first satellite Internet terminal. The data of the first terminal is transmitted to the satellite Internet through the first satellite Internet terminal, and then transmitted to the second subnet, and is received by the second satellite Internet terminal in the second subnet. In the second subnet, the second terminal is the destination node, and this second satellite Internet terminal is the source node. The mobile ad-hoc network multi-path routing method of the above embodiment can be used to determine the multi-path routing between the second satellite Internet terminal and the second terminal.

[0085] The present invention is not limited to the foregoing specific embodiments. The present invention extends to any new feature or any new combination disclosed in this specification, as well as any new combination of steps of any new method or process disclosed.

Claims

1. A mobile ad-hoc network multipath routing method, characterized in that Including: Construct a multi-objective optimization model for path-oriented routing, where the multi-objective optimization model takes the metrics characterizing the path service quality as the optimization objectives, and the metrics characterizing the path service quality include two or more of the bandwidth availability rate, the delivery rate, and the load availability rate; The multi-objective optimization model is characterized in a phased description form; The objective function of the multi-objective optimization model is: or or or , Among them, respectively represent the path bandwidth availability rate, the path delivery rate, and the path load availability rate, ; N represents the number of stages into which the path is divided, k represents the k stage; Among them, represents the node broadband availability rate; represents the node delivery rate; represents the node load availability rate; represents the measurement period; is the node i 's neighboring node j within the NAV value, is the DCF inter-frame spacing, is the node i value of the backoff counter; represents the node i to node i+ 1's delivery rate; represents the node i 's load queue length, is the node i 's cache size; The constraint conditions of the multi-objective optimization model are: ， Among them, is the lateral offset of the node at the k stage from the first baseline, where the first baseline is the line connecting the source node and the destination node; is the lateral offset angle at the k-th stage, which is the angle between the line connecting the node at the k-th stage and the node at the (k + 1)-th stage and the second baseline of the node at the k-th stage, where the second baseline is the line connecting the current stage node and the destination node; are the minimum lateral offset and the maximum lateral offset respectively; are the minimum lateral offset angle and the maximum lateral offset angle respectively; Divide the multi-objective optimization model into multi-stage routing problems for solution to obtain a non-dominated solution set, including: solving the multi-objective optimization model with the parameters constrained in the constraint conditions as variables; in the solution of each stage, taking the lateral offset of the current stage node from the first baseline as the state variable, and taking the lateral offset angle of the next stage node from the second baseline as the control variable; Determine the multi-path routing according to the non-dominated solution set.

2. The multi-path routing method for mobile ad-hoc network according to claim 1, characterized in that, According to the divided multiple stages, iteratively solve the non-dominated solution set of the multi-objective optimization model from the last stage to the initial stage; or, iteratively solve the non-dominated solution set of the multi-objective optimization model from the initial stage to the last stage.

3. The multi-path routing method for mobile ad-hoc network according to claim 2, wherein When solving each stage, based on the non-dominated solution set obtained by solving the previous stage, iteratively solve the non-dominated solution set of the current stage.

4. The mobile ad-hoc network multi-path routing method according to claim 3, characterized in that, Calculate the solution of the multi-objective optimization model of the current stage based on the non-dominated solution set obtained by solving the previous stage, and select the non-dominated solution set of the current stage through non-dominated sorting of the solution of the multi-objective optimization model of the current stage.

5. The multi-path routing method for mobile ad hoc network according to claim 4, wherein Obtain the solution of the multi-objective optimization model of the current stage by adding the instantaneous objective solution of the current stage to the non-dominated solution set obtained by solving the previous stage.

6. The mobile ad-hoc network multipath routing method according to claim 5, wherein Calculate all the instantaneous objective solutions of the current stage by traversing each state variable of the current stage and the control variables under each state variable.

7. The multi-path routing method for mobile ad hoc network according to any one of claims 4-6, characterized in that When selecting the non-dominated solution set of the current stage through non-dominated sorting of the solution of the multi-objective optimization model of the current stage, for each state variable, at most select a set maximum number of solutions.

8. The multi-path routing method for mobile ad-hoc network according to claim 7, wherein, The method for selecting the non-dominated solution set of the current stage through non-dominated sorting of the solution of the multi-objective optimization model of the current stage includes: Perform non-dominated sorting on the solutions of the multi-objective optimization model under each state variable of the current stage respectively, and select the non-dominated solution set under each state variable; If the number of solutions in the selected non-dominated solution set exceeds the set maximum number, then further screen out the set maximum number of solutions from the selected non-dominated solution set.

9. The mobile ad hoc network multi-path routing method according to claim 8, characterized in that, When screening solutions from the selected non-dominated solution set, screen them in the order of the crowding degree of each solution from large to small.

10. A communication method, characterized in that, A first terminal in a first subnet communicates with a second terminal in a second subnet, and the first subnet and the second subnet communicate through a satellite internet; Use the mobile ad-hoc network multi-path routing method as described in any one of claims 1-9 to determine the multi-path routing between the first terminal and the satellite internet terminal in the first subnet.

11. A storage medium stores a computer program, characterized in that, Running this computer program can execute the mobile ad-hoc network multi-path routing method as described in any one of claims 1-9.

12. A mobile ad-hoc network multi-path routing device, comprising a processor and a storage medium, wherein a computer program is stored in the storage medium, characterized in that, When the processor runs the computer program in the storage medium, it executes the mobile ad hoc network multi-path routing method according to any one of claims 1-9.

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