IMA system interface resource allocation method based on hierarchical immune algorithm

Abstract

This invention discloses an IMA system interface resource allocation method based on a hierarchical immune algorithm, comprising: S1) determining the onboard installation location of airborne equipment according to its function, and clarifying the interface type, number of interfaces, interface constraints, and access cost of the IMA system network transmission and interface conversion devices used by the airborne equipment interface; S2) determining the number of IMA system network transmission devices and interface conversion devices, networking the IMA system and airborne equipment, determining the total number of available interface types for each type of network transmission device and interface conversion device, and determining the layout restriction area; S3) first performing system decoupling and redundancy layering on the system network devices, interface devices, and access interfaces, and then performing optimization for conversion interface allocation and network interface allocation on each redundancy layer. This invention can ensure a reasonable layout of IMA system interface devices while meeting interface relationship constraints and the total number of interface resources for different types of devices, and the overall access cost is low.

Description

Technical Field

[0001] This invention relates to a system interface resource allocation method, and more particularly to an IMA system interface resource allocation method based on a hierarchical immune algorithm. Background Technology

[0002] Avionics systems are the "brain" and "nerves" of an aircraft. With the increasing integration of avionics systems, the Integrated Modular Avionics (IMA) architecture is being used in more and more aircraft due to its low cost, ease of scalability, and high flexibility. IMA systems are configured according to the resource requirements of the aircraft systems, providing computing, networking, and interface resources to various airborne systems. They rely on network transmission equipment to provide a highly reliable airborne data bus and on interface conversion equipment to handle the acquisition, conversion, and processing of thousands of interfaces from different vendors. How to rationally deploy network transmission equipment and remote data conversion equipment on board, and how to rationally allocate network interface resources and conversion interface resources while meeting constraints, is one of the challenges in aircraft system layout design and resource allocation.

[0003] The resource sharing and module performance constraints of the IMA system dictate that interfaces from different vendors must compete for limited shared resources. A well-planned layout and resource allocation of network transmission and interface conversion equipment can reduce aircraft weight. Equipment layout and resource allocation for network and data conversion interfaces constitute an NP-complete problem. Existing methods primarily rely on manual layout and allocation, depending on engineers' experience and iterative processes. This is inefficient and yields suboptimal results. Each change incurs significant costs, making it difficult to meet the interface allocation and optimization requirements of large aircraft in complex scenarios, thus hindering the achievement of maximum weight reduction. Traditional center-of-gravity methods can be used for single-device location, and mixed-integer programming can be used for interface allocation. However, these static strategies require prior determination of the equipment layout, and the interface conversion equipment also needs to connect to network transmission equipment, increasing the complexity of layout allocation due to their coupling. Current fragmented optimization of network transmission and interface conversion equipment layout and resource allocation leads to suboptimal overall costs and lacks a systematic approach.

[0004] Therefore, how to rationally deploy network transmission equipment and interface conversion equipment on the aircraft to meet the on-board layout constraints, and how to rationally allocate a large number of interfaces from different vendors to each network transmission equipment and each interface conversion equipment, has always been a research hotspot in this field. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide an interface resource allocation method for an IMA system based on a hierarchical immune algorithm, which can reasonably arrange network transmission equipment and remote data conversion equipment, and reasonably allocate network interface resources and conversion interface resources while meeting constraints, so as to reduce the overall access cost of different types of interfaces accessing the IMA system.

[0006] The technical solution adopted by this invention to solve the above-mentioned technical problems is to provide an IMA system interface resource allocation method based on a hierarchical immune algorithm, including the following steps: S1) Determine the onboard installation location of the airborne equipment according to its function, and clarify the interface type, number of interfaces, interface constraints, and access cost of the network transmission and interface conversion equipment of the IMA system used by the airborne equipment interface; S2) Determine the number of network transmission equipment and interface conversion equipment in the IMA system, network the IMA system and airborne equipment, determine the total number of available interface types of each network transmission equipment and interface conversion equipment, and determine the layout restriction area; S3) Based on the interface resource requirements, the redundancy architecture design of the network transmission equipment and interface conversion equipment, first allocate the system network... The system decouples and redundancy layers network devices, interface devices, and access interfaces. Then, for each redundancy layer, the first layer optimization is performed first, followed by the second layer optimization, as follows: S4) First layer optimization: Based on the resource capabilities and constraints of the interface conversion device, a comprehensive optimization strategy for the layout of the interface conversion device and the allocation of other interfaces is formulated, and a collaborative optimization method based on immune algorithm and mixed integer programming is executed; S5) Second layer optimization: The interface conversion device is one of the access network transmission devices, and based on the resource requirements of other network interfaces, the resource capabilities of network transmission devices, and their constraints, a comprehensive optimization strategy for the layout of network transmission devices and the allocation of network interfaces is formulated, and a collaborative optimization method based on immune algorithm and mixed integer programming is executed.

[0007] Further, step S1 quantifies the access interface according to the following formula: the interface set of the access interface conversion device: IO m ={io1, io2, ..., io m There are m interfaces that need to be connected to the IMA system interface conversion device; the set of interfaces for connecting to the network transmission device: IO u ={io1, io2, ..., io u There are a total of u interfaces that need to be connected to the IMA system network transmission equipment;

[0008] io i =(posi i type i ,num i cost i bind i );

[0009] posi i= (posi_x) i posi_y i posi_z i );

[0010] Among them, io i posi represents the i-th access interface. i Indicates the location of the access interface, posi_x i posi_y i posi_z i Indicates the three-dimensional coordinates of this location; type i Indicates the type of the access interface, num i Indicates the number of access interfaces; cost i This represents the access cost coefficient for this type of access interface, bind. i This indicates the associated requirements for the access interface;

[0011] The interfaces of the network transmission device include an A664 network interface and a TTE network interface. The interfaces of the interface conversion device include an A429 transmitting interface, an A429 receiving interface, an A825 interface, an analog interface, a discrete interface, and a sensor interface. For interface devices without special installation location requirements, after selecting the appropriate shortest distance for installation, the network interface type is connected to the network transmission device, and other types of interfaces are connected to the interface conversion device.

[0012] Further, step S2 quantifies the devices according to the following formula: Interface conversion device set: IOM n ={iom1, iom2, ..., iom n There are n interface conversion devices in total; the network transmission device set is: IOM. v ={iom1, iom2, ..., iom v There are a total of v network transmission devices;

[0013] iom j = (iom_posi j max_type_1 j max_type_2 j ...max_type_g j )

[0014] iom_posi j = (iom_p_x) j ,iom_p_y j ,iom_p_z j );

[0015] Layout restriction area set: ZONE = {zone1, zone2, ..., zone...} p There are a total of p prohibited layout regions;

[0016] zone s = (zone_type) s zone_Center s zone_par1 s zone_par2 s zone_par3 s );

[0017] zone_Center s = (zone_p_x) s zone_p_y s zone_p_z s );

[0018] Among them, iom j IOM_posi represents the j-th interface device in the IMA system. j Indicates the location of the interface device, iom_p_x j ,iom_p_y j ,iom_p_z j Indicates its three-dimensional coordinates; max_type_1 j max_type_2 j ...max_type_g j This indicates the total number of the first, second, ... g-th type interfaces of the interface device;

[0019] zone s This represents the s-th layout constraint region within the layout space, represented by zone_type. s Indicates the type of the restricted area in this layout, zone_Center s This indicates the location of the restricted area of ​​the layout.

[0020] Furthermore, steps S4 and S5 employ no-distribution zone constraints and device connection constraints, ensuring that any network transmission device or interface conversion device is located outside the no-distribution zone, and that device connection constraints are implemented by controlling the sum of the interconnection distances of each interface conversion device or network transmission device.

[0021] Furthermore, the collaborative optimization method based on immune algorithm and mixed integer programming in steps S4 and S5 includes: S601: antibody encoding and population initialization; S602: affinity evaluation based on joint cost; S603: immune selection, using an elite retention and dynamic elimination mechanism, calculating the affinity of each antibody in each population update iteration, and selecting excellent antibodies to store in the memory bank; S604: clonal selection and mutation; S605: clonal suppression; S606: new antibodies and population update; S607: termination and output.

[0022] Further, step S601 includes: Encoding rules: Representing the feasible solutions to the problem as chromosomes in the solution space through encoding. A chromosome represents an array of length equal to the total number of devices. Each gene position corresponds to an interface device and its location coordinates. For interface conversion devices, the gene value ∈ n, and for network transmission devices, the gene value ∈ v, representing the device number of that type; Constraint preprocessing: Generating an initial solution based on forbidden region constraints to reduce invalid searches; Randomly initializing each individual in the generated population and checking that each device position satisfies the forbidden region constraint, i.e., it is outside the forbidden region; Parameter settings: Defining the population size, mutation probability, and maximum number of iterations.

[0023] Further, step S602 includes: evaluating the merits of each individual through an affinity function, which is designed as a joint cost evaluation function. The interface access cost is fused through device location layout optimization cost, where the layout optimization cost includes device connection cost and no-access zone penalty cost. During the evolutionary process, solutions that do not meet the conditions are gradually eliminated, and the solution with the highest affinity is selected to achieve device layout and interface resource allocation. The affinity function is as follows:

[0024] Fitness = α·cost_dist + β·cost_ penalty + γ·cost_ Allocate ；

[0025] Where: α, β, γ Optimize through experiments; cost_dist : Indicates the cost of device connectivity; cost_ penalty : Represents the number of devices placed in the no-layout zone multiplied by the penalty coefficient, ensuring that the layout constraints are met; cost_ Allocate : Represents the cost of all interfaces being allocated to all interface conversion devices and network transmission devices. The cost of interface conversion device allocation is calculated in its optimization layer. At the same time, interface conversion devices are coupled with network transmission devices. As one of the interfaces accessing network transmission devices, the cost of interface conversion devices is included in the interface allocation cost calculation of the network transmission device optimization layer.

[0026] Furthermore, step S602 in its respective optimization levelcost_ Allocate The calculation is as follows:

[0027] i. Define decision variable x i,j and y k,j Each interface is assigned to one interface device;

[0028] x i,j =

[0029] y k,j =

[0030] ,Right now It belongs to one of the g types of interfaces provided by the interface device;

[0031] ii. Set allocation constraints to assign interfaces with interface association requirements to the same interface device; define allocation weight factors based on a greedy sorting allocation strategy, sort all interfaces according to the weight factors, and allocate interfaces with larger weight factors first.

[0032] Set a total limit on the number of interface types and allocate them to the interface devices (iom). j any type of interface i The sum of the interface resources does not exceed the total number of interface resources of the interface device, max_type_g j And any type of interface i The sum of the interface resources does not exceed the interface resource threshold of the interface device;

[0033] iii. Set the objective function based on minimizing the allocation cost. The allocation cost for a single interface is defined as follows: cost_ io i This indicates the cost of connecting the interface to a certain interface device;

[0034] cost_ io i =io_dist×num i ×cost i ;

[0035] io_dist represents the distance from the access interface to the device connected to that interface, num i Indicates the number of access interfaces, cost i This represents the access cost coefficient of the access interface. cost_ Allocate This represents the cost of allocating all interfaces to all interface devices;

[0036] cost_ Allocate = .

[0037] Further, step S603 involves immune selection based on affinity, retaining the antibodies with the highest fitness as memory cells in each generation; and defining a similarity function between antibodies, calculating the distance between antibodies based on Hamming distance or affinity function. If the similarity is greater than a threshold, low-affinity antibodies are randomly eliminated. Step S604 uses Gaussian mutation or random mutation to perform targeted mutation on the clones, randomly perturbing the clone genes and constraining the boundaries of the mutated gene loci. Step S605, after the clonal mutation operation, reselects the progeny antibodies generated by the clonal mutation, suppressing antibodies with low affinity and retaining antibodies with high affinity to enter a new antibody population. Step S606 introduces random new antibodies, which are then sorted by affinity along with the retained memory library antibodies and clonal mutation antibodies. The top-ranked antibodies are selected for population recombination to maintain a constant population size.

[0038] Furthermore, the stopping criterion in step S607 is that the number of iterations and / or the change in the optimal affinity value is less than a preset threshold. In step S607, the high affinity antibody that meets the constraint conditions in the memory bank is output.

[0039] Compared with existing technologies, this invention offers the following advantages: The IMA system interface resource allocation method based on a hierarchical immune algorithm provided by this invention relies on the installation location, interface type, and quantity of equipment from different vendors, and considers the total resource volume, coupling relationship, and on-board layout constraints of each interface type in network transmission equipment and interface conversion equipment. It employs an optimization method based on a hierarchical immune algorithm, first distinguishing redundant equipment and redundant interfaces according to the system architecture, and then implementing a hierarchical interface design strategy for each redundant layer. Through hierarchical design, and considering dynamic constraint processing and cost weight balancing, global optimality is achieved, thereby solving the problem of network transmission and interface conversion equipment layout and interface resource allocation in IMA systems under multiple constraints. With tens of thousands of interfaces on the machine needing to connect to interface conversion equipment and network transmission equipment, manual equipment layout and interface allocation often takes more than a month. The method of this invention can generate an effective allocation scheme in minutes, ensuring that interface relationship constraints and the total resource volume requirements of different types of interfaces on each device are met, with low overall access cost. Attached Figure Description

[0040] Figure 1 This is a flowchart of the interface resource allocation process for the IMA system based on the hierarchical immune algorithm of this invention.

[0041] Figure 2 This is a flowchart of the collaborative optimization process based on immune algorithm and mixed integer programming in this invention. Detailed Implementation

[0042] The present invention will now be further described with reference to the accompanying drawings and embodiments.

[0043] This invention provides a hierarchical immune algorithm-based interface resource allocation method for IMA systems. It considers the installation location and interface types and quantities of equipment from different vendors, the total resource volume of each interface type in network transmission equipment and interface conversion equipment, on-board layout constraints, and system architecture redundancy relationships, employing a hierarchical interface design strategy. First, the system's network equipment, interface equipment, and access interfaces are decoupled and redundantly layered. Then, optimizations are performed on the allocation of conversion interfaces and network interfaces for each redundancy layer. Specifically, the layout and allocation of conversion interfaces are optimized first, with the interface conversion equipment connected to the network transmission equipment. Then, the layout and allocation of the network transmission equipment are optimized. Using this method, the layout and location of network transmission and interface conversion equipment in the IMA system are determined while allocating a large number of interfaces to interface equipment, achieving a relatively low overall cost and solving the problem of layout and interface resource allocation for network transmission and interface conversion equipment in IMA systems under multiple constraints. The specific implementation steps of this invention are as follows: Figure 1 As shown.

[0044] Step S1: Quantify access interfaces. Determine the location of each airborne device using the IMA system network transmission equipment and interface conversion equipment interface resources on the aircraft according to the airborne equipment functions. The quantification formula for the interface type, quantity, interface constraints, and access cost of this airborne equipment is as follows:

[0045] The interface set of the access interface conversion device: IO m ={io1, io2, ..., io m There are a total of m interfaces that need to be connected to the IMA system interface conversion device;

[0046] The set of interfaces for accessing network transmission devices: IO u ={io1, io2, ..., io u There are a total of u interfaces that need to be connected to the IMA system network transmission equipment;

[0047] io i =(posi i type i ,num i cost i bind i );

[0048] posi i = (posi_x) i posi_y i posi_z i );

[0049] Among them, io i posi represents the i-th access interface. iIndicates the location of the access interface, posi_x i posi_y i posi_z i Indicates the three-dimensional coordinates of this location; type i This indicates the type of access interface: network interface or other interface. A network interface indicates an interface for accessing the airborne backbone network, such as an A664 network interface or a TTE network interface. Other interfaces include A429 transmit interface, A429 receive interface, A825 interface, analog interface, discrete interface, sensor interface, etc. i Indicates the number of access interfaces; cost i This indicates the access cost coefficient for this type of access interface. The access cost coefficient varies depending on the interface type and the cable used; bind i This indicates the associated requirements of the access interface. Some interfaces come from the same device and want to be connected to the same interface conversion device, or interfaces that are closely related to each other need to be connected to the same interface conversion device. The same applies if the network transmission device has this requirement.

[0050] For interface devices without special installation location requirements, after the interface allocation is completed through the algorithm in the invention, the device can be installed at the shortest distance of any suitable location. The network interface type can be connected to the network transmission device, and other types of interfaces can be connected to the interface conversion device.

[0051] Step S2: Quantify equipment capabilities, determine the number of network transmission devices and interface conversion devices in the IMA system, the layout restriction area, and the types and quantities of interface resources available to the network transmission devices and interface conversion devices. The quantification formula is as follows:

[0052] Interface conversion resource device set: IOM n ={iom1, iom2, ..., iom n There are a total of n interface conversion devices;

[0053] Network transmission resource device set: IOM v ={iom1, iom2, ..., iom v There are a total of v network transmission devices;

[0054] iom j = (iom_posi j max_type_1 j max_type_2 j ...max_type_g j )

[0055] iom_posi j = (iom_p_x)j ,iom_p_y j ,iom_p_z j );

[0056] Layout restriction area set: ZONE = {zone1, zone2, ..., zone...} p There are a total of p prohibited layout regions;

[0057] zone s = (zone_type) s zone_Center s zone_par1 s zone_par2 s zone_par3 s );

[0058] zone_Center s = (zone_p_x) s zone_p_y s zone_p_z s );

[0059] Among them, iom j IOM_posi represents the j-th interface device in the IMA system. j This indicates the location of the interface device, iom_p_x j ,iom_p_y j ,iom_p_z j Indicates its three-dimensional coordinates; max_type_1 j max_type_2 j ...max_type_g j This represents the total number of interface types 1, 2, ..., g of the interface device, i.e., the maximum number of interface resources of each interface type that the device can provide. `type_g` indicates that there are g interface types. i It must be one of the interfaces provided by the interface device.

[0060] zone s This represents the s-th layout constraint region within the layout space, represented by zone_type. s Indicates the type of the restricted area in this layout, zone_Center s This indicates the location of the layout restriction area, specifically through zone_p_x. s zone_p_y s zone_p_z s Confirmed, zone_par1 szone_par2 s zone_par3 s This represents the parameters that define the restricted area of ​​this layout.

[0061] Step S3: Based on the interface resource requirements, network transmission equipment, and interface conversion equipment redundancy architecture design, perform redundancy layering to improve system availability and security, and ensure stable system operation through redundancy design; distinguish between redundant equipment and redundant interfaces, such as dividing into redundant layer A and redundant layer B, and then implement a layered interface design strategy for each redundant layer, that is, the first layer optimizes other types of interfaces accessed through interface conversion equipment except for network interfaces, and the second layer optimizes network interfaces accessed through network transmission equipment to the backbone network.

[0062] Step S4: First-level optimization. Based on the resource requirements of other interfaces, the resource capabilities of the interface conversion equipment, and their constraints, formulate a comprehensive optimization strategy for the layout of the interface conversion equipment and the allocation of other interfaces. Execute a collaborative optimization method based on immune algorithm and mixed integer programming (Step 6). That is, use an improved immune algorithm to optimize the spatial layout of the interface conversion equipment, and use mixed integer programming to optimize the allocation of other interface resources.

[0063] Step S5: Second-level optimization. Based on the network interface resource requirements (including the network interfaces of interface conversion devices), the resource capabilities of network transmission devices and their constraints, a comprehensive optimization strategy for the layout of network transmission devices and the allocation of network interfaces is formulated. A collaborative optimization method based on immune algorithms and mixed integer programming is executed (Step 6), that is, the improved immune algorithm is used to optimize the spatial layout of network transmission devices, while the mixed integer programming is used to optimize the allocation of network interface resources.

[0064] Step S6: A collaborative optimization method based on immune algorithms and mixed integer programming, such as... Figure 2 As shown, a mathematical model is established with the following layout constraints:

[0065] 1) Restrictions on prohibited areas:

[0066] iom_posi j No network transmission equipment or interface conversion equipment should be placed within the restricted area.

[0067] 2) Device Connection Constraints: After the interface conversion devices are deployed, they also need to be connected to network transmission devices, and the network transmission devices also need to be interconnected. Therefore, the devices are required to have a certain degree of clustering. This is represented by distance cost, and the model is established as follows:

[0068] Minimize cost_dist= , where iom_posi n+1 =iom_posi1,

[0069] This represents the distance from the j-th device to the (j+1)-th device; cost_dist represents the device connection cost; additionally, the device connection constraint can also be represented by the sum of the interconnection distances of each interface conversion device or network transmission device.

[0070] Step 601: Antibody Encoding and Population Initialization

[0071] 1) Encoding rules: The feasible solutions to the problem are represented as chromosomes (individuals) in the solution space through encoding. The chromosome represents an array of length equal to the total number of devices. Each gene position corresponds to an interface device and its location coordinates. When the interface conversion device is used, the gene value ∈ n, and when the network transmission device is used, the gene value ∈ v, representing the device number.

[0072] 2) Constraint Preprocessing: To accelerate convergence and improve efficiency, an initial solution is generated based on the forbidden region constraint, reducing invalid searches. Forbidden Region Constraint: Each individual in the generated population is randomly initialized, and the location of each device is checked to ensure it satisfies the forbidden region constraint, i.e., it is outside the forbidden region.

[0073] 3) Parameter settings

[0074] Define algorithm control parameters such as population size, mutation probability, and maximum number of iterations.

[0075] Step 602: Affinity assessment based on joint cost

[0076] Individual affinity is calculated based on joint cost: the merits of each individual are evaluated through an affinity function, and the affinity value reflects the optimization objective of the solution. The affinity function is designed as a joint cost evaluation function, which integrates the device location layout optimization cost with the interface access cost. The layout optimization cost includes device connection cost and no-distribution area penalty cost. In the evolution process, solutions that do not meet the conditions are gradually eliminated, and the solution with the highest affinity is selected to realize device layout and interface resource allocation.

[0077] Affinity function:

[0078] Fitness = α·cost_dist + β·cost_ penalty + γ·cost_ Allocate ；

[0079] Where: α, β, γ can Optimize through experiments;

[0080] cost_dist : Indicates the cost of device connectivity;

[0081] cost_ penalty : Represents the number of devices placed in the no-layout zone multiplied by the penalty coefficient, ensuring that the layout constraints are met;

[0082] cost_ Allocate : Represents the cost of allocating all interfaces to all interface conversion devices and network transmission devices. cost_ Allocate The calculation is as follows: Based on the interface resource requirements and their constraints, and the device resource capabilities, a mixed-integer programming optimization method is executed, that is, mixed-integer programming is used for the dynamic allocation of access interfaces. The main model and steps are as follows:

[0083] a. Define decision variables

[0084] To describe the interface access to the interface device, define the following decision variables:

[0085] x i,j =

[0086] A decision variable of 1 indicates that the j-th interface device provides interface resources for that interface, i.e., the I / O... i Assigned to the j-th interface device; the decision variable is 0, indicating that the I / O is... i Not assigned to this interface device IOM j .

[0087] Uniqueness of interface allocation: Each interface must be assigned to a single interface device;

[0088] , j = 1, 2,..., n.

[0089] To describe the connection of a certain type of interface to an interface device, define the following decision variables:

[0090] y k,j =

[0091] ,Right now It belongs to one of the g types of interfaces provided by the interface device.

[0092] The decision variable is 1, and the interface type is io i Assigned to the j-th interface device; the decision variable is 0, indicating that the I / O is... i Not assigned to this interface device IOM j .

[0093] b. Define assignment constraints

[0094] 1) Define interface association constraints

[0095] Describe the interface association requirements, interface I / O i io l Must be assigned to the same interface device: x i,j =x l,j , l m, j n;

[0096] When io i When allocated to a certain IOM, io l It must be allocated to this IOM.

[0097] The greedy sorting and allocation strategy defines an allocation weight factor and sorts all interfaces according to the weight factor, prioritizing those with larger weight factors.

[0098] 2) Define the total number of interface types constraints

[0099] Describe the resource total constraints of the interface device interface type and allocate them to the interface device IOM. j any type of interface i The sum of the interface resources does not exceed the total number of interface resources of the interface device, max_type_g j .

[0100] , j = 1, 2,..., n , k = 1, 2,..., g; ...

[0101] , j = 1, 2,..., n, k = 1, 2,..., g.

[0102] To balance interface device capacity and allocation, any type of interface... i The sum of interface resources can be limited to no more than the interface resource threshold of the interface device. The interface resource threshold can be determined by referring to the average proportion of the total number of interfaces of this type that need to be allocated.

[0103] c. Definition of the objective function

[0104] Minimize allocation cost; the allocation cost for a single interface is defined as follows: cost_ io i This indicates the cost of connecting the interface to a certain interface device.

[0105] cost_ io i =io_dist×num i ×cost i ;

[0106] `io_dist` represents the distance from the access interface to the device connected to that interface. `num` i Indicates the number of access interfaces, cost i This represents the access cost coefficient of the access interface, which is closely related to the access interface type.

[0107] cost_ Allocate This represents the cost of allocating all interfaces to all interface devices. Interface conversion devices and network transmission devices calculate this cost at their respective optimization levels.

[0108] cost_ Allocate = 。

[0109] Based on the quantified interfaces and allocation constraints, and combined with the device layout input, complete the allocation of all interfaces.

[0110] The joint cost evaluation of the integration layout cost and the interface resource allocation cost is achieved through the design of an affinity function.

[0111] The number of interfaces mentioned above is m for interface conversion devices and u for network transmission devices.

[0112] The number of devices mentioned above is n for interface conversion devices and v for network transmission devices.

[0113] Step 603: Immune Selection

[0114] An elite retention and dynamic elimination mechanism is adopted. In each population update iteration, the affinity of each antibody is calculated, and excellent antibodies are selected and stored in the memory bank to prevent the loss of high-quality solutions.

[0115] Immune selection method: Affinity-based immune selection retains the antibodies with the highest fitness as memory cells in each generation. Similarity suppression: A similarity function is defined between antibodies. Based on Hamming distance or affinity function, the distance between antibodies is calculated. If the similarity is greater than a threshold, low-affinity antibodies are randomly eliminated to maintain population diversity and avoid premature convergence.

[0116] Step 604: Clonal Selection and Mutation

[0117] Cloning and amplification: Based on antibody affinity, the selected superior antibodies are replicated in proportion. That is, the selected superior antibodies are cloned and mutated to enhance the exploration ability and maintain the diversity of the population.

[0118] Mutation: Targeted mutation is performed on clones, randomly perturbing (adjusting values) the clone genes to increase population diversity. The perturbation can use Gaussian mutation or random mutation, and boundary constraints are applied to the mutated gene loci.

[0119] Step 605: Clonal Suppression

[0120] After clonal mutation, the progeny antibodies produced by the clonal mutation are reselected to suppress antibodies with low affinity and retain antibodies with high affinity to enter a new antibody population.

[0121] Step 606: New Antibodies and Population Update

[0122] Novel Antibodies: Introduce random novel antibodies to maintain population diversity. The random novel antibodies should meet the constrained pretreatment conditions.

[0123] Population recombination: Retaining memory bank antibodies, clonal variant antibodies, and random new antibodies, and sorting them by affinity, the population size is kept constant. During the population renewal process, excellent antibodies are retained, while maintaining population diversity and avoiding premature convergence.

[0124] Step 607: Termination and Output

[0125] 1) Stopping Criteria

[0126] The algorithm terminates if one or a combination of the following conditions are met:

[0127] Reach the preset number of iterations;

[0128] The optimal affinity value no longer increases significantly (convergence).

[0129] 2) Output Results

[0130] Output the high-affinity antibodies in the memory that meet the constraints, i.e., the optimal device layout and interface resource allocation scheme.

[0131] In summary, this invention provides a method for the layout and interface resource allocation of network transmission and interface conversion devices in an IMA system. It employs an optimization method based on a hierarchical immune algorithm. First, redundant devices and redundant interfaces are distinguished according to the system architecture. Then, a hierarchical interface design strategy is executed for each redundant layer. The first layer optimizes interfaces other than network interfaces accessed through interface conversion devices. The second layer optimizes network interfaces accessed through network transmission devices to the backbone network, including network interfaces of interface conversion devices connected to network transmission devices. For device layout and interface resource allocation, an improved immune algorithm is used to optimize the spatial layout of devices, while mixed-integer programming is used for dynamic allocation of access interfaces. Then, through joint cost evaluation of layout cost and allocation cost, the spatial layout and interface allocation of devices are completed. Through hierarchical design, and considering dynamic constraint processing and cost weight balancing, global optimality is achieved, solving the problem of network transmission and interface conversion device layout and interface resource allocation in IMA systems under multiple constraints.

[0132] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications and improvements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be defined by the claims.

Claims

1. A method for allocating interface resources in an IMA system based on a hierarchical immune algorithm, characterized in that, Includes the following steps: S1) Determine the onboard installation location according to the function of the airborne equipment, and clarify the interface type, number of interfaces, interface constraints and access cost of the network transmission and interface conversion equipment of the IMA system used by the airborne equipment interface; S2) Determine the number of network transmission devices and interface conversion devices in the IMA system, network the IMA system and airborne equipment, determine the total number of available interface types for each type of network transmission device and interface conversion device, and determine the layout restriction area; S3) Based on the interface resource requirements, network transmission equipment, and redundant architecture design of interface conversion equipment, first decouple and redundancy layer the system's network transmission equipment, interface conversion equipment, and access interfaces. Then, for each redundant layer, perform the first layer optimization first, followed by the second layer optimization, as follows: S4) First-level optimization: Based on the resource capabilities and constraints of the interface conversion equipment, a comprehensive optimization strategy for the layout of the interface conversion equipment and the allocation of the conversion interfaces is formulated, and a collaborative optimization method based on immune algorithm and mixed integer programming is executed. S5) Second layer optimization: The interface conversion device is one of the access network transmission devices. Based on the network interface resource requirements of other airborne devices, the resource capabilities of network transmission devices and their constraints, a comprehensive optimization strategy for the layout of network transmission devices and the allocation of network interfaces is formulated, and a collaborative optimization method based on immune algorithm and mixed integer programming is executed.

2. The IMA system interface resource allocation method based on hierarchical immune algorithm as described in claim 1, characterized in that, Step S1 quantifies the access interface according to the following formula: io j ∈TIO, io j TIO represents the set of interfaces that need to be connected to the IMA system interface conversion device, and there are a total of m interfaces that need to be connected to the IMA system interface conversion device. io k ∈NIO, io k This indicates the conversion interface that needs to be connected to the IMA system network transmission device. NIO represents the set of interfaces that need to be connected to the network transmission device, with a total of u network interfaces that need to be connected to the IMA system network transmission device. TIO IO, NIO IO, where IO represents the set of all network interfaces and conversion interfaces connected to the IMA system; io i =(posi i ,type i ,num i ,cost i ,bind i ); positions i =(posi_x i ,posi_y i ,posi_z i ); Among them, io i Represents the i-th access interface, posi i Indicates the location of the access interface, posi_x i posi_y i posi_z i Indicates the three-dimensional coordinates of this location; type i Indicates the type of the access interface, num i Indicates the number of access interfaces; cost i This represents the access cost coefficient for this type of access interface, bind. i This indicates the associated requirements for the access interface; The network transmission device includes an A664 network interface and a TTE network interface. The interface conversion device includes an A429 transmitting interface, an A429 receiving interface, an A825 interface, an analog interface, a discrete interface, and a sensor interface. For interface devices without special installation location requirements, they can be installed at the shortest possible distance, with the network interface connected to the network transmission device and the conversion interface connected to the interface conversion device.

3. The IMA system interface resource allocation method based on hierarchical immune algorithm as described in claim 1, characterized in that, The device is quantified in step S2 according to the following formula: iom i ∈TIOM, ,iom i This represents the interface conversion device for the IMA system, and TIOM represents the set of interface conversion devices, with a total of n IMA system interface conversion devices. iom k ∈NIOM, ,iom k NIOM represents the network transmission equipment of the IMA system, and NIOM represents the collection of network transmission equipment, with a total of v IMA system network transmission equipment. TIOM IOM, NIOM IOM, IOM represents the set of all IMA system interface devices, including interface conversion devices and network transmission devices; iom j =(iom_posi j ,max_type_1 j ,max_type_2 j ,...max_type_g j ); iom_posi j =(iom_p_x j ,iom_p_y j ,iom_p_z j ); Layout restriction area set: ZONE = {zone1, zone2, ..., zone...} p There are a total of p prohibited layout regions; zone s =(zone_type s ,zone_Center s ,zone_par1 s ,zone_par2 s ,zone_par3 s ); zone_Center s =(zone_p_x s ,zone_p_y s ,zone_p_z s ); Among them, iom j IOM_posi represents the j-th interface device in the IMA system. j Indicates the location of the interface device, iom_p_x j ,iom_p_y j ,iom_p_z j Indicates its three-dimensional coordinates; max_type_1 j max_type_2 j ...max_type_g j This indicates the total number of the first, second, ... g-th type interfaces of the interface device; zone s This represents the s-th layout constraint region within the layout space, represented by zone_type. s Indicates the type of the restricted area in this layout, zone_Center s This indicates the three-dimensional coordinates of the center of the restricted area of ​​this layout, zone_p_x s zone_p_y s zone_p_z s The three-dimensional coordinate position parameters are the center point of the layout constraint region. zone_par1s, zone_par2s, and zone_par3s represent the parameters that define the layout constraint region.

4. The IMA system interface resource allocation method based on hierarchical immune algorithm as described in claim 1, characterized in that, Steps S4 and S5 employ no-distribution zone constraints and device connection constraints. Each network transmission device or interface conversion device is located outside the no-distribution zone, and device connection constraints are imposed by controlling the sum of the interconnection distances of each interface conversion device or network transmission device.

5. The IMA system interface resource allocation method based on hierarchical immune algorithm as described in claim 1, characterized in that, The collaborative optimization method based on immune algorithm and mixed integer programming in steps S4 and S5 includes: S601: Antibody encoding and population initialization; S602: Affinity assessment based on joint costs; S603: Immune selection employs an elite retention and dynamic elimination mechanism. In each population update iteration, the affinity of each antibody is calculated, and the best antibodies are selected and stored in the memory bank. S604: Clonal Selection and Mutation; S605: Clonal inhibition; S606: New Antibodies and Population Renewal; S607: Termination and Output.

6. The IMA system interface resource allocation method based on hierarchical immune algorithm as described in claim 5, characterized in that, Step S601 includes: Encoding rule: The feasible solution to the problem is represented as a chromosome in the solution space through encoding. The chromosome represents an array of length equal to the total number of devices. Each gene position corresponds to an interface device and its location coordinates. When it is an interface conversion device, the gene value is ∈n. When it is a network transmission device, the gene value is ∈v, which represents the device number of that type. Constraint preprocessing: Initial solutions are generated based on forbidden regions constraints to reduce invalid searches; each individual in the generated population is randomly initialized, and each device location is checked to ensure it satisfies the forbidden region constraint, i.e., it is outside the forbidden region; Parameter settings: Define population size, mutation probability, and maximum number of iterations.

7. The IMA system interface resource allocation method based on hierarchical immune algorithm as described in claim 5, characterized in that, Step S602 includes: evaluating the merits of each individual solution using an affinity function, which is designed as a joint cost evaluation function. This function integrates the device location layout optimization cost with the interface access cost. The layout optimization cost includes device connection cost and no-access zone penalty cost. During the evolutionary process, solutions that do not meet the conditions are gradually eliminated, and the solution with the highest affinity is selected to achieve device layout and interface resource allocation. The affinity function is as follows: Fitness=α·cost_dist+β·cost_ penalty +γ·cost_ Allocate ； Where: α, β, γ Optimize through experiments; cost_dist : Indicates the cost of device connectivity; cost_ penalty : Represents the number of devices placed in the no-layout zone multiplied by the penalty coefficient, ensuring that the layout constraints are met; cost_ Allocate This represents the cost of allocating all interfaces to all interface conversion devices and network transmission devices. The cost of interface conversion devices is included in the calculation when network transmission devices are allocated and optimized.

8. The IMA system interface resource allocation method based on hierarchical immune algorithm as described in claim 7, characterized in that, Step S602 is in its respective optimization level cost_ Allocate The calculation is as follows: i. Define decision variable x i,j and y k,j Each interface is assigned to one interface device; x i,j = y k,j = ,Right now It belongs to one of the g types of interfaces provided by the interface device; ii. Set allocation constraints to assign interfaces with interface association requirements to the same interface device; define allocation weight factors based on a greedy sorting allocation strategy, sort all interfaces according to the weight factors, and allocate interfaces with larger weight factors first. Set a total limit on the number of interface types and allocate them to the interface devices (iom). j any type of interface i The sum of the interface resources does not exceed the total number of interface resources of the interface device, max_type_g j And any type of interface i The sum of the interface resources does not exceed the interface resource threshold of the interface device; iii. Set the objective function based on minimizing the allocation cost. The allocation cost for a single interface is defined as follows: cost_ io i This indicates the cost of connecting the interface to a certain interface device; cost_ I i =io_dist×num i ×cost i ; io_dist represents the distance from the access interface to the device connected to that interface, num i Indicates the number of access interfaces, cost i This represents the access cost coefficient of the access interface. cost_ Allocate This represents the cost of all interfaces being allocated to all interface devices, where m represents the number of all interfaces. cost_ Allocate = 。 9. The IMA system interface resource allocation method based on hierarchical immune algorithm as described in claim 5, characterized in that, Step S603 involves immune selection based on affinity, with each generation retaining the antibodies with the highest fitness as memory cells. A similarity function between antibodies is defined, and the distance between antibodies is calculated based on Hamming distance or affinity function. If the similarity is greater than a threshold, low-affinity antibodies are randomly eliminated. In step S604, Gaussian mutation or random mutation is used to perform targeted mutation on the clone, randomly perturbing the clone genes and constraining the boundaries of the mutated gene sites. In step S605, after the clonal mutation operation, the progeny antibodies generated by the clonal mutation are reselected to suppress antibodies with low affinity and retain antibodies with high affinity to enter a new antibody population. In step S606, random new antibodies are introduced and sorted by affinity together with the retained memory library antibodies and clonal mutation antibodies. The antibodies with the highest ranking are selected for population recombination to maintain a constant population size.

10. The IMA system interface resource allocation method based on hierarchical immune algorithm as described in claim 5, characterized in that, The stopping criterion in step S607 is that the number of iterations and / or the change in the optimal affinity value is less than a preset threshold. In step S607, the high affinity antibody that meets the constraint conditions in the memory is output.

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